Category: Practical

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Introducing GT-Viz: Visualize FactGrid Data on a Map

GT-Viz is a browser-based tool for visualizing geospatial and temporal data from SPARQL endpoints. You write a SPARQL query, provide the SPARQL endpoint for example FactGrid or Wikidata, and the results appear on an interactive map with a timeline.

It was built by a group of students at RWTH Aachen University as part of the Knowledge Graph Lab course.

Try it here: https://gtviz-kgl.wikidata.dbis.rwth-aachen.de/tutorial

Input

The only input needed is a SPARQL query. A set of built-in example queries covers FactGrid (Thirty Years’ War battles), Wikidata (Napoleon, WW1 & WW2, Magellan and Columbus voyages, Olympic venues), and can be loaded for testing the functionalities. The sidebar holds a SPARQL editor with syntax highlighting and validation. A Help panel documents the expected query variables.

The tool reads these variables from your query results: ?location (WKT point), ?time (date), ?category, ?parentCategory, and optionally ?name, ?description, and ?pathId.

Map View

Query results appear as markers on an OpenStreetMap base layer. Parent categories each get a distinct color; sub-categories within a parent are separated by fill patterns. Clicking a marker shows its name, description, category, and date.

Two display options can be toggled: whether to draw connecting lines between points that share a ?pathId, and whether to show points that have no date.

The Group Visibility panel shows the full category hierarchy from the query results. Individual sub-groups or entire parent categories can be toggled on or off. Item counts are shown at every level.

Timeline and Animation

The timeline at the bottom filters the map to a selected date window. Drag the handles to set start and end dates; the map updates immediately. The Play button animates the window forward through time at an adjustable speed (configurable in days, weeks, months, or years per second).

Historic Map Overlays

As an experimantal feature it is possible to load historic maps. The historic maps are overlayed on the base layer as they only cover a small portion of the globe. For testing we provided a small set of over 20 different historic maps. Only thing needed to integrate such a historic map is a tile server serving the map thus the set of supported historic maps can easaly be extended.

Example: Thirty Years’ War battles from FactGrid

    1. Open https://gtviz-kgl.wikidata.dbis.rwth-aachen.de
    2. Click the lightbulb icon and select “FactGrid: Battles of the Thirty Years’ War” — the endpoint and query fill in automatically.
    3. Click Run. Battles appear across central Europe; the timeline sets itself to 1618–1648.

From there, use the Play button to step through the war year by year, the filter panel to isolate specific belligerents, and the Map Config tab to add a period map beneath the markers.

Feedback

GT-Viz is a student project in its early stages. We are looking for feedback from the FactGrid community on what works, what is missing, and what would be most useful.

→ Take the survey

Continue reading “Introducing GT-Viz: Visualize FactGrid Data on a Map”

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…an eery conversation with ChatGPT about FactGrid

You remember the iconic scene when Star Trek’s Scotty (after a jump from the 23rd century back into the year 1986) is forced to use a 20th-century computer? His prompt “Computer” is his first stupidity. When he eventually grabs the thing he is supposed to use, the mechanical mouse on the table, and repeats his prompt: “Computer” his skills look even worse. He needs another hint at the use of the odd thing before he can recover his fame as the man who can talk to any machine.

Here is my last night’s conversation with ChatGPT abou FactGrid, Wikidata and about Large Language Models (LLMs). ChatGPT allowed the reproduction. There is even a link that allows you to see our conversation on their side:

https://chatgpt.com/share/68be02f4-f454-8009-aa68-cdae9c18ba78

I was trying to understand how the LLM driven machine is presently improving its FactGrid-SPARQL skills at such a breath-taking speed. I opened with the broadest question imaginable: “What kind of thing is FactGrid”? in order to get the scope of answers the machine would propose to consider.

Our talk had its eery moments. The SPARQL Queries which ChatGPT gives in the following are all fails, although ChatGPT has become the greatest SPARQL assistant over the last six months. I did not address the fail immediately; it became a turning point in our chat nonetheless. ChatGPT had hallucinated properties, data structures and Q-numbers as it was expalnding without my specific prompt and feedback. You have to outline questions, and you must name properties and items that are to become the keys in your query if you want to get operable queries from ChatGPT.

I did not intervene on the first query fail, yet the machine understood (so to say) that it had been hallucinating and it revised its own query using a source it had not mentioned so far: It suddenly claimed to remember queries which it had scripted for me and it was now eager to use the properties it had learned back then. This was a turn in our conversation because it made it clear that we are teaching ChatGPT with our FactGrid queries wherever we go into detailed feedback loops.

Another detail startled me as just as eery. As the machine dived into the differences between Wikidata and FactGrid it passed a little detail: If you want to get a new Wikidata property, that’s a complex internal process with a community decision to be passed. If you ask for a new FacctGrid property that is a far more experimental thing: We offer new properties so that researches can ask new and specific questions. A team will get a new property within minutes after Bruno or I “talked it through” with them. Experienced teams create their own properties with a look at the entire data structure. Has this insight into how we are organising ourselves been published elsewhere so that ChatGPT could get this insight?

I eventually wondered to what extent ChatGPT comes with an idea of the ongoing trends in the field – but read yourself:

ChatGPT Chat of 7 Sept 2025, 23:00 CET

OS: what kind of thing is FactGrid

ChatGPT: FactGrid is a research database built on Wikibase (the same software that powers Wikidata).
Here’s what it is in a nutshell:

  • Collaborative database – FactGrid is designed for historians, researchers, and other scholars who want to store and link structured data.
  • Specialized Wikibase instance – While Wikidata is very broad and general, FactGrid is tailored to historical research data (people, events, places, organizations, sources, etc.).
  • Open and linked – It allows researchers to enter their own datasets, connect them with each other, and link them out to external databases like GND, Wikidata, or VIAF.
  • Long-term archiving – The project (supported since 2023 by NFDI4Memory in Germany) aims to make research data sustainably available for future use.
  • Use cases – It’s often used to map networks of people, locations, events, or concepts in historical projects.

So, you can think of FactGrid as a specialized, historian-friendly version of Wikidata: a structured, queryable database that enables collaborative work with historical datasets.

☞ Do you want me to explain how it differs from Wikidata in practice (in terms of scope, openness, and data modeling)?

OS: have you harvested their data?

ChatGPT: I don’t have a local copy of FactGrid’s database — so no, I haven’t “harvested” it.
But:

  • FactGrid is openly available. Like Wikidata, it runs on Wikibase, and it exposes its contents via a SPARQL endpoint (where you can run structured queries) and via APIs.
  • That means anyone can query, browse, and even download large portions of its data directly.
  • My training didn’t include a specific dump of FactGrid, but I can help you formulate SPARQL queries to pull out whatever you need from it.

Would you like me to show you how to grab a full dataset or are you more interested in specific types of data?

Would you like me to show you how to grab a full dataset (e.g. a JSON/CSV export from their SPARQL endpoint), or are you more interested in specific types of data (like all people, or all places, or a certain project’s records)?

OS: ah, no, I was wondering to what extend you are exploring these things to get a base of information – next to a large language model.

ChatGPT: Got it — you’re asking whether I, as a model, go out and explore databases like FactGrid in order to build up a base of structured information alongside the language model.

The short answer: no, I don’t autonomously harvest or explore databases.

Here’s how it works on my side:

  • Training data → I was trained on a mixture of licensed material, publicly available text, and data created by human trainers. That gave me broad linguistic and factual knowledge.
  • Not self-updating → I don’t crawl or explore new datasets on my own after training. So I don’t have a live copy of FactGrid or Wikidata inside me.
  • Live connections → What I can do is connect to external sources (like FactGrid’s SPARQL endpoint or Wikidata) during a conversation, if you ask me to query them. That’s an on-demand lookup, not a background harvest.
  • Structured vs. unstructured → I’m strongest with text, but I can also help you work with structured data (SPARQL queries, CSVs, APIs), which means I can effectively “use” FactGrid if you point me at it.

So: my “base” is the language model; external knowledge bases like FactGrid or Wikidata remain outside, but I can interact with them when asked.

☞ Would you like me to sketch what a workflow combining me + FactGrid might look like for a historian (e.g. me helping with queries, cleaning results, interpreting patterns)?

OS: not necessary right now. I have been using your SPARQL skills already and was impressed – so impressed that I wondered how much you accessed.

ChatGPT: I see 🙂 Thanks for saying that!

To clarify: when I help with SPARQL, it’s not because I have FactGrid (or Wikidata) stored inside me — it’s because I’ve been trained on the structure of SPARQL, on examples of how people query linked open data, and on general patterns in Wikibase setups. That lets me “speak the language” and generate queries that usually work on FactGrid (or similar databases), even though I don’t have the database itself.

If you ask me for actual data, I either:

  • rely on what I learned before my cutoff (which doesn’t include FactGrid dumps), or
  • use a live query (via the SPARQL endpoint or API) if we decide to pull information together.

So the impression of access comes from fluency with the query language and the data model, rather than from me secretly harvesting the dataset.

Would you like me to explain the differences in data modeling between FactGrid and Wikidata that sometimes matter for writing queries?

OS: that’s actualy an interesting point.

ChatGPT: Yes, it is! The differences in data modeling between Wikidata and FactGrid are subtle but really shape how you query. Let me sketch the most important ones:

1. Ontology / Property System

Wikidata

  • Has thousands of properties, heavily curated through community consensus.
  • Global in scope — properties are designed to cover everything (people, chemistry, music, politics…).
  • Very stable, but also rigid: if you need a new property, you have to propose and wait.

FactGrid

  • Uses its own property set, designed primarily for historical research data.
  • More flexible — new properties can be introduced more quickly for specific research projects.
  • This means the property universe is smaller but more specialized (e.g. genealogy, historical events, archival sources).

2. Scope of Items

Wikidata → Global knowledge graph: current and historical persons, abstract concepts, species, celestial bodies, etc.

FactGrid → Focuses on historical entities: people, organizations, places, events, sources.

  • Often project-driven: research groups upload their own datasets (e.g. local networks, prosopographies).
  • Less encyclopedic, more deep and fine-grained in specific domains.

3. Statement Usage

Both use the same Wikibase “subject–predicate–object” model with qualifiers and references.

  • Wikidata → Tends toward “canonical” statements (e.g. one date of birth, sourced to a publication).
  • FactGrid → Encourages recording conflicting claims, multiple chronologies, or uncertain dates.

Great for history, where ambiguity is part of the data.

4. Linking Out

  • Wikidata → Central hub, designed to be linked to by others
  • FactGrid → Actively links out to GND, Wikidata, VIAF, archival identifiers.

    • Goal: make research datasets interoperable, not replace general-purpose Wikidata.

    5. Querying Implications

    • Property numbers differ — you can’t just copy a Wikidata query into FactGrid and expect it to work.
    • But patterns (e.g. ?item wdt:P… wd:Q…) are the same, because both use SPARQL over Wikibase.
    • In FactGrid, you sometimes need to query project-specific structures, whereas Wikidata aims for universal modeling.

    ☞ So in practice:

    • If you want global authority control → go to Wikidata.
    • If you want deep historical datasets with provenance → go to FactGrid.

    Would you like me to make a side-by-side example query (same research question run on Wikidata vs. FactGrid), so you can see the differences directly?

    OS: yes show one!

    ChatGPT: Perfect — let’s do a side-by-side example!

    Say we ask a very historian-style question:

    “Give me all people born in Gotha.”

    On Wikidata

    # People born in Gotha (Wikidata)
    SELECT ?person ?personLabel ?dob WHERE {
    ?person wdt:P31 wd:Q5; # instance of human
    wdt:P19 wd:Q3130; # place of birth = Gotha (Q3130)
    wdt:P569 ?dob. # date of birth
    SERVICE wikibase:label { bd:serviceParam wikibase:language “en”. }
    }
    LIMIT 50
    • P31 = instance of
    • Q5 = human
    • P19 = place of birth
    • Q3130 = Gotha
    • P569 = date of birth

    On FactGrid

    The modeling is similar but with different property IDs (FactGrid doesn’t reuse Wikidata’s numbers):

    # People born in Gotha (FactGrid)
    SELECT ?person ?personLabel ?dob WHERE {
    ?person wdt:P2 wd:Q7; # instance of human (P2=instance of, Q7=human)
    wdt:P92 wd:Q15121; # place of birth = Gotha (Q15121 in FactGrid)
    wdt:P131 ?dob. # date of birth
    SERVICE wikibase:label { bd:serviceParam wikibase:language “en,de”. }
    }
    LIMIT 50
    • P2 = instance of (FactGrid’s analog of Wikidata’s P31)
    • Q7 = human
    • P92 = place of birth
    • Q15121 = Gotha (FactGrid’s Gotha item)
    • P131 = date of birth

    Key differences

    • Property numbers differ (P31 vs P2, P19 vs P92).
    • Item IDs differ (Q3130 vs Q15121).
    • Structure of the query stays the same — you just swap in FactGrid’s ontology.

    Would you like me to run these queries live (so you see actual people returned), or just keep this as a structural comparison?

    OS: well it is clear that they have the bigger Gotha set. That’s where they organised a citizen science project.

    ChatGPT: Exactly — that’s a great observation.

    FactGrid’s Gotha dataset is unusually rich because of the citizen science project that was built around it:

    • Local historians, archivists, and volunteers entered people, families, institutions, and events connected to Gotha.
    • The project wasn’t limited to “famous” people (as Wikidata often is), but included ordinary residents, networks, and micro-histories.
    • This is precisely the kind of thing FactGrid was designed for: to host project-driven, fine-grained, and sometimes messy historical data that doesn’t quite fit Wikidata’s encyclopedic scope.

    So yes — if you compare the two:

    • Wikidata Gotha → mostly notable people (dukes, composers, politicians).
    • FactGrid Gotha → a much larger, denser social fabric of individuals and organizations, including those who’d never make it into Wikidata.

    ☞ That’s why for historians, FactGrid can feel like a deep local archive in linked data form, whereas Wikidata is more like a global reference desk.

    Would you like me to show you how you can query the Gotha citizen science dataset in FactGrid specifically (e.g. “all people born in Gotha with their occupations”)?

    OS: Jewish-German history would be a more interesting field.

    ChatGPT: Absolutely — and that’s a field where FactGrid can really shine compared to Wikidata.

    Here’s why:

    • Wikidata: You’ll find entries on notable figures in Jewish-German history — rabbis, philosophers, politicians, artists — but usually only those already present in encyclopedias or secondary literature.
    • FactGrid: Because it’s project-driven, it can host prosopographies of local Jewish communities, lists of individuals from sources like synagogue registers, deportation lists, business directories, or cemetery inscriptions — i.e. the broader social base, not just the elite.
    • Ambiguity handling: Jewish-German history often involves complex and conflicting data (different spellings, contested identities, uncertain dates, forced name changes). FactGrid’s openness to multiple parallel statements makes it a better environment to capture that complexity.

    Example query idea (FactGrid)

    “All Jewish individuals who lived in Thuringia in the 19th century.”

    Depending on the data available, this could look something like:

    SELECT ?person ?personLabel ?birth ?death ?residenceLabel WHERE {
    ?person wdt:P2 wd:Q7; # instance of human
    wdt:P434 wd:Q101614; # religion = Judaism (Q101614 in FactGrid)
    wdt:P92 ?birthPlace; # birth place
    wdt:P93 ?deathPlace; # death place
    wdt:P97 ?residence. # residence
    ?residence wdt:P1069 wd:Q421799. # residence in Thuringia
    OPTIONAL { ?person wdt:P131 ?birth. } # date of birth
    OPTIONAL { ?person wdt:P132 ?death. } # date of death
    SERVICE wikibase:label { bd:serviceParam wikibase:language “en,de”. }
    }
    LIMIT 100

    This would give:

    • Names of individuals
    • Dates of birth/death (if present)
    • Their residences in Thuringia
    • With a filter for Judaism as religion.

    ⚖ Of course, the result set depends heavily on what projects have been entered into FactGrid. If a local community or memorial initiative uploaded data, you’ll see far more than Wikidata ever could.

    ☞ Do you want me to check what Jewish-related datasets are already in FactGrid (so we see what’s actually there right now), or do you prefer to keep it conceptual for the moment?

    OS: you are not taking the Properties from their directory of Properties.

    ChatGPT: You’re absolutely right — what I sketched was illustrative, not literally taken from FactGrid’s property catalogue.

    FactGrid has its own directory of properties (just like Wikidata), and if we want to be precise, we need to query with their actual property IDs and labels, not guessed parallels.

    For example:

    On Wikidata,

    • religion = P140
    • place of residence = P551
    • date of birth = P569

    On FactGrid, the equivalents are different numbers.

    • religion is indeed P434 (you already pointed me there earlier).
    • date of birth is P131.
    • date of death is P132.
    • place of residence is P97.
    • instance of = P2.
    • human = Q7.

    So a correct FactGrid query for Jewish-German history might look like this:

    SELECT ?person ?personLabel ?birth ?death ?residenceLabel WHERE {
    ?person wdt:P2 wd:Q7; # instance of human
    wdt:P434 wd:Q101614; # religion = Judaism
    wdt:P97 ?residence. # residence
    OPTIONAL { ?person wdt:P131 ?birth. } # date of birth
    OPTIONAL { ?person wdt:P132 ?death. } # date of death
    SERVICE wikibase:label { bd:serviceParam wikibase:language “en,de”. }
    }
    LIMIT 100

    This one really uses FactGrid’s property directory.1ChatGPT is again haucinating the querie. The P131 refers to research projects and “date of death” would P38. The entire data modelling is groundless. If you want ChatGPT to write a query you will have to provide the P-numbers and you will have to give feedback after the first wrong start. ChatGPT can correct complex scripts but it is helpless where it has to define a query without exact paramaters.

    ✅ Thanks for catching me on that — if we want serious results in FactGrid, we always have to check the property catalogue first, otherwise the query will look fine but return nothing.

    Would it be helpful if I mapped a small table of common properties (Wikidata vs FactGrid equivalents) so you can quickly “translate” queries between the two?

    OS: so you remember my previous communications on viable SPARQL queries?

    ChatGPT: Yes — I remember! 🙂

    Earlier2The statement refers to chats weeks ago. I am using the fre version but have logged in in order to trigger and feed these learing mechanisms. you asked me to:

    • check and fix SPARQL queries that didn’t run properly on FactGrid,
    • adjust them so they used the right property IDs from FactGrid’s ontology instead of Wikidata’s,
    • and expand queries (like the one about Places in Thuringia and “all information on subproperties of P434”).

    That’s exactly why I realised in my last example I’d slipped back into “Wikidata-style guessing” instead of strictly following FactGrid’s property catalogue, which you had already flagged as important in earlier conversations.

    Would you like me to now systematically assemble a crosswalk table of the most common properties you’ve been working with (like instance of, date of birth, place of birth, residence, religion, etc.) — so you can reuse it in future queries without hunting in the directory each time?

    OS: ah, no. I have this list. I was trying to understand how you gain your knowledge. Do you mind if I publish our conversation on the FactGrid blog?

    ChatGPT: Not at all — you’re welcome to publish our conversation on the FactGrid blog.

    For clarity: the parts about FactGrid, SPARQL, and data modeling are based on my training (on publicly available text, examples of Wikibase/Wikidata usage, and general knowledge about linked open data) combined with live reasoning — I don’t have access to unpublished FactGrid data. Anything you quote would reflect my explanations and examples, not private data from FactGrid.

    If you like, I can also clean up or annotate the conversation a bit so it reads smoothly for blog readers and makes clear which parts are illustrative examples vs. actual FactGrid property IDs.

    Do you want me to do that?

    OS: No, I like it as it is. But you might be faster to convert it into wordpress html, I guess.

    [The was actually an utter failure, ChatGPT produced a condensed version of our chat, which I dismissed]

    OS: I guess the future is a mix of large language model and data base harvesting to create a backbone of stability. This is, in any case impressive. Thanks.

    Exactly — that’s a very good way to put it.

    A hybrid future could look like this:

    • Large Language Models (LLMs) provide flexibility, reasoning, and contextual understanding. They’re great at interpreting ambiguous queries, summarizing, and generating human-readable explanations.
    • Structured databases / knowledge graphs provide stability, precision, and verifiable facts. They give a reliable backbone that an LLM can query instead of “hallucinating” information.
    • Combined approach: The LLM handles interpretation and translation between human language and formal queries (like SPARQL), while the database ensures factual correctness and structured retrieval.

    It’s like having a brilliant, versatile research assistant (the LLM) who always double-checks facts against a trusted archive (the database) before giving answers — the best of both worlds.

    And you’re right — it’s already impressive how much can be achieved today, especially in domains like historical research where the data is both complex and distributed.

    If you want, I can sketch a conceptual workflow for combining an LLM with FactGrid that shows how queries, verification, and enriched outputs could work together. It’s a neat “future of research” picture.

    OS: Some other day, thanks

    Of course! You’re very welcome — I enjoyed the conversation. 🙂

    Footnotes

    • 1
      ChatGPT is again haucinating the querie. The P131 refers to research projects and “date of death” would P38. The entire data modelling is groundless. If you want ChatGPT to write a query you will have to provide the P-numbers and you will have to give feedback after the first wrong start. ChatGPT can correct complex scripts but it is helpless where it has to define a query without exact paramaters.
    • 2
      The statement refers to chats weeks ago. I am using the fre version but have logged in in order to trigger and feed these learing mechanisms.
Posted on

ddpp | Ein Datensatz deutscher politischer Parteien

Der hiermit vorgelegte Datensatz zur deutschen Parteiengeschichte dürfte – mit aktuell 873 gelisteten Parteien – im Moment der umfassendste seiner Art im Internet sein: Frei nutzbar, in beliebiger Konfiguration herunter zu laden, in Hintergrunddaten ausgreifend und zudem (mit einem frei erhältlichen FactGrid-Konto) unter beliebigen Forschungsinteressen auf die eigenen Bedürfnisse hin bearbeitbar.

Zum Vergleich: 59 aktive und 35 historische Parteien notiert der Datensatz des GBV zum Thema mit dem Angebot extrem unhandlicher Identifikatoren und ohne sehr viel tieferen Informationsgehalt. Wikidata überholte Projekte wie dieses in den letzten Jahren – allerdings auch immer wieder mit dem Nachteil, dass die dabei entstehenden Datensätze nicht so recht geplant waren und damit so einfach nicht zu handhaben sind. Die deutschen Parteien in Wikidata sind über keine einheitliche Recherche zu erfassen; es ging hier nicht darum, einen gleichmäßig ausgestatteten wie vollständigen Datensatz herzustellen. Einzelne, voneinander nicht informierte Eingaben akkumulierten sich hier eher planlos. Bewegt man sich in der Gegenwart, sollte man die Liste zu Rate ziehen, die die Bundeswahlleiterin 2024 vorlegte: Ausgewählte Daten politischer Vereinigungen, Stand 31.12.2023 (Wiesbaden, Juli 2024). Mit 637 Einträgen für die Jahre 1969 bis 2023 ist sie die dichteste Sammlung zur Parteienlandschaft der letzten 50 Jahre. Jede einzelne Partei ist hier mit Eckdaten aus der Buchführung der Wahlleitung versehen. Wikidata, in der quantitativen Erfassung wie in der Stringenz unterlegen, gewinnt an ganz anderen Stellen in der Vernetzung etwa mit Personen: Gut 15.000 Personen sind in Wikidata als Mitglieder mit der NSDAP verbunden, wohl weil die Informationen aus entsprechenden Wikipedia-Artikeln zur Verfügung standen. Wikidata bietet Serien von Mitgliederzahlen, externe Identifikatoren aus anderen Datenbanken oder Informationen zu Wahlen, in denen diese Parteien antraten.

Der vorliegende Datensatz ist genetisch ein Amalgam aus beiden Quellen und, gezielt als Partei-Datensatz angelegt, auf Homogenität hin gestaltet. Der Datensatz der Bundeswahlleiterin lässt sich dabei mit all seinen Eckdaten isolieren. Gleichzeitig reicht der vorliegende Datensatz dank Wikidata bis in die Frühphase des deutschen Parlamentarismus der vor der Reichseinigung stehenden Länder hinab.

Die gelisteten Parteien sind geschlossen mit einem Projekt-Item als zum „ddpp-Datensatz“ gehörige isolierbar, sie lassen sich gleichzeitig beliebig filtern (etwa um nur Parteien der BRD oder der DDR zu erfassen) oder ausdehnen: etwa im Blick auf historische Eckdaten, Organisationsbeziehungen, Parteiprogrammatiken oder involvierte Personen.

Statt einer einfachen Definition – Angebote, Definitionen selbst vorzunehmen

Der vorliegende Datensatz verfügt über kein klares Definitionskriterium und das sollte als Vorteil notiert sein. Man selbst kann ihn streng definieren oder öffnen, um etwa auch „Listen“, „Wahlgemeinschaften“ oder prominente „Orts-“ und „Landesverbände“ zu erfassen. Das ist sinnvoll schon allein, da sich aus all diesen auf Wahlzetteln auftauchenden Organisationsformen Parteien entfalten können, die am Ende bundesweit auftreten und sich im Parteiengefüge etablieren (wie dies etwa die Grünen taten). Die FactGrid-Datenbank hat kein Zentrum. Würde jemand NSDAP-Ortsverbände zu seinem Thema machen, wären diese das Zentrum seines Datensatzes und die Parteienlandschaft der Weimarer Republik nur der unüberschaubare Rand.

Die Problemlag wird mit der Frage nach dem Gründungsdatum der SPD im Detail greifbar: Der vorliegende Datensatz bietet tatsächlich vier Gründungsdaten mitsamt den Gründen, die sie im Einzelnen nahelegen. Es handelt sich bei drei der Datierungen um Gründungsdaten von Parteien, die die SPD als historische Wurzeln beansprucht. Wenn man diese Daten den einzelnen Vorgängern zuordnet, ist das Jahr 1890 mit dem Erfurter Gründungskonvent das allein der SPD zuzuordnende Gründungsdatum (und darum hier hochgewertet):

Der FactGrid „Datensatz zu deutschen politischen Parteien (ddpp)“ lebt in dieser Vielschichtigkeit der Möglichkeiten von seinem Angebot, ihn unterschiedlich zu fassen. Mit ihm sollte vor allem durchdacht werden, wie man sich dem Problem besser als mit einer Definition nähert. Die Antwort lautet: durch den beliebig umfassenden Datensatz, der jederzeit die Verlagerung und Verengung des Blickwinkels erlaubt.

Den ddpp-Datensatz praktisch nutzen

Der vorliegende Datensatz sollte es möglich machen, alle gelisteten Parteien eindeutig voneinander abzugrenzen und im komplizierten Fall direkt miteinander in Beziehung zu setzen: Es gibt in der Tat einen Unterschied zwischen WiR2020 (Q1182789) und Wir2020 (Q1211961); er liegt ostentativ im kleinen r, das Wir2000 als Erkennungsmerkmal beansprucht. Im spezifischen Datensatz wird klarer, dass die Partei mit dem kleinen r eine Abspaltung von und eine Kampfansage gegenüber der Mutter mit dem großen R ist. Die Q-Nummern erlauben es, die Unterschiede den Notizen in den Datensätzen zu überlassen. Im Datensatz sind im selben Moment Übersetzungen (aktuell englischer, französischer und spanischer Sprache) im Angebot sowie Verlinkungen zu Wikidata, zur GND, zur Publikation der Bundeswahlleiterin und zu Wikipedia-Artikeln, die mehr Informationen bieten.

In der praktischen Nutzung sollte jeweils der spröde „Basisdatensatz“ im Zentrum stehen, der es erlaubt, beliebig zu filtern wie beliebig zu erweitern. Im Basisdatensatz erscheint jede Partei nur einmal mit ihrer ID und kurzem Identifikationsangebot:

Die einzelnen Listen lassen sich nun beliebig erweitern, wobei Parteien in den dabei entstehenden Tabellen mitunter mehrere Zeilen erhalten, zum Beispiel, wenn mehrere GND-Nummern in der diesbezüglichen Spalte zu notieren sind.

  1. Datensatz: Die Parteien mit (soweit vorhanden) externen Identifikatoren: Wikidata, GND, Liste der Bundeswahlleiterin für die Jahre 1969 bis 2023 chronologisch nach Gründungsdatum sortiert.
  2. Datensatz: Die (Um-)Bennungen mit Datierungen. Diese Abfrage ist besonders nützlich in automatisierten Matching-Prozessen, da sie in ihnen Namensvarianten, die sich in Quellen finden, zur Verfügung stellt.
  3. Datensatz: Deutsche Parteien, die zwischen 1969 und 2023 bei der Bundeswahlleitung gelistet waren nach den Ausgewählten Daten politischer Vereinigungen, Stand 31.12.2023 herausgegeben von der Bundeswahlleiterin (Wiesbaden, Juli 2024), mit den dortigen Eckdaten und den Informationen zu respektiven Herausnahmen aus der Aktenführung.
  4. Datensatz: Von welchen Parteien spalteten sich welche Parteien ab?
  5. Datensatz: Alle Parteien, von denen die Bundeswahlleiterin soeben Unterlagen bereitstellt (aktuell 107)
  6. Datensatz: In welchen Parteien gingen die gelisteten auf?
  7. Datensatz: Ideologische Positionierung
  8. Datensatz: Politische Programmpunkte

Die letzten Suchen waren nurmehr punktuell mit Information bestückt; hier wartet der Datensatz im Moment auf Projekte, die in die Tiefe gehen. Eingehendere Suchen ließen sich zu Initiatoren, Gründungsmitgliedern, Parteivorsitzenden bieten, abermals jedoch im Moment nur punktuell.

Wahlergebnisse visualisieren

Seine zentrale Funktionalität entfaltet der Datensatz deutscher politischer Parteien, sobald man ihn im Rahmen des strukturell ganz anders gelagerten (und bis jetzt nur in punktuell vorliegenden) der Wahlergebnisse aller Reichstags- und Bundestagswahlen laufen lässt. Hier die letzte Reichstagswahl vom März 1933, wie sie in der SPARQL-Suche generiert wird und direkt mit den Datensätzen des Projektes kommuniziert:

Reichstagswahl 1933 Bubble Chart

Das FactGrid Datenmodell weicht hier vom Wikidata-Datenmodell ab. Statt auf verschiedene Properties ist hier auf eine einzige gesetzt, die die diversen Zählergebnisse (Erststimmen, Zweitstimmen, Parlamentssitze und Prozentanteile) aufnimmt und unter den Einheiten notiert – das ist praktisch, da sich nun beliebig verschiedenartige Ergebnisse anbieten lassen:

Man kann bei dieser Modellierung an drei Stellschrauben im SPARQL-Skript drehen:

SPARQL-Code für Visualisierung von Wahlergebnissen. Zeile 1: Form der Visualisierung, Zeile 3: die Wahl, deren Ergebnis Visualisiert werden soll (z.B. Bundestagswahl 2025), Zeile 11: Ergebnisaspekt (was dabei gezählt werden soll) etwa Zweitstimmen.

Die FactGrid-Datenlage zu Wahlen ist im Moment noch sehr unvollständig. Wie weit sie gediehen ist, findet sich auf der Projektseite in FactGrid laufend aktuell notiert. Hier ging es erst einmal um eine Realisierung, die handwerkliche Probleme der unterschiedlichen Wikidata-Modellierungen in den Griff kriegt.

Der vernetzte Datensatz

Anders als konventionelle Datensätze sind Wikibase-Datensätze fast immer komplex vernetzt, und das durchaus unüberschaubar.

Die banalste Vernetzung des vorliegenden Datensatz geht von den FactGrid notierten Personen aus:

  • Datensatz: Alle FactGrid-notierten SPD-Mitglieder (283 mit Stand vom März 2024)
  • Datensatz: Alle FactGrid-notierten NSDAP-Mitglieder (1559 mit Stand vom März 2024)
  • Datensatz: Alle FactGrid-notierten Personen, die sowohl in der SPD wie in der NSDAP Mitglieder wurden (21 mit Stand vom März 2024)

Die drei Mustersuchen zeigen, dass wir hier theoretisch sehr komplexe Fragen stellen können, etwa nach der Ausbildung von erfassten Parteimitgliedern oder nach Personennetzwerken, die sich etwa über Mitgliedschaften in Logen oder Studentenverbindungen ergaben. Der Vorteil des FactGrid-Datensatzes ist hier erneut, dass er kein Zentrum aufweist. Es ist möglich, laufend neue Aspekte des jeweiligen Interesses zu formulieren und diese danach aus ganz unterschiedlichen Zusammenhängen heraus zu erfassen.

Extrem komplex sind erwartungsgemäß die organisatorischen Vernetzungen der NDSDAP, setzte hier doch eine Partei systematisch staatliche Organisationsstrukturen außer Kraft, um diese danach mit Parteistrukturen wie denen der SS zu ersetzen. Der ddpp-Datensatz bildet dies gerade im Ansatz ab.

Der für die Weiterverwendung offene Datensatz

Jede der im Vorangegangenen angebotenen Mustersuchen lässt sich modifizieren und alle Ergebnisse lassen sich jeweils in verschiedenen Datenformaten (JSON, CSV, TSV, HTML) herunterladen. Am rechten Rand der Tabellen und Visualisierungen bietet stets ein Mouseover-Menü die verschiedenen Formate wie Bearbeitungsoptionen an. Die Daten sind samt und sonders CC0-lizensiert und damit komplikationslos auch ohne Zitat der Quellen in Grafiken verwendbar.

Interessant sollte es sein, Bearbeitungen des Datensatzes auf der Plattform selbst vorzunehmen, so dass andere Nutzer vom Wissenszuwachs direkt profitieren – vor allem aber auch mit dem Vorteil, dass das eigene Projekt an dieser Stelle nicht von vorne anfangen muss und Fragen nach der eigenen Nachhaltigkeit der kollektiven Arbeitsumgebung überlassen kann.

Einige klar benennbare Desiderate seien neben dieser grundsätzlichen Einladung, sich den Datensatz auf der Plattform anzueignen, ausgesprochen: Die Aufstellung der Bundeswahlleiterin erwies sich als überaus nützlich im Angebot harter Klarheiten. Ein enormes Desiderat wären jedoch Verlinkungen zu Digitalisaten der Unterlagen, die die Parteien vorlegten, sowie Notate der Personen, die diese Parteien gründeten und der Adressen der Anmeldungen. Es ist dies ein Desiderat vor allem im Blick auf die kleinen Dateien, die oft nach wenigen Jahren der Inaktivität schon wieder aus den Akten verschwanden und heute nicht einmal über Internetseiten, die sie lancierten sichtbar sind.

Im Historischen Datenzentrum Sachsen-Anhalt, das das Projekt Rahmen des R:hovono-Projektes initiierte, werden wir eine GND-Situierung des Datensatzes anstreben.

Gut wäre es, die Datenbankobjekte mit Expertise zu Positionierungen, Zielsetzungen, statistischen Daten und vor allem mit Quellverweisen, angereichert anbieten zu können.

  • Featured Image: Wahlwerbung der AIPD, die wohl eher zu den Kunstprojekten zu rechnen ist, und darum ein gesondertes FactGrid-Item hat. Screenshot der Website https://aipd-partei.de/
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Modelling Premodern Political Entities – Case Studies from Eastern Europe

Words shape our understanding of the world. Especially in times of Disinformation, we as scholars need to be sensitive about the framing in which we put the knowledge we would like to share. The obstacles of this task become even higher in Digital Humanities. When we write a paper, we might come up with lengthy explanations why something might not be that simple. But a Graph Database such as FactGrid confronts us with the challenge of describing complex, sometimes ambivalent, sometimes contradictory historical realities in a simple statement about certain objects and the relationship between them. In the following, I would like to present some problems, that came across me during a recent research Seminar at Halle University. I will present solutions, that we came up with; hopefully offering a model for others as well. While doing so, I argue that we as scholars should emphasize the importance of a qualitative sensibility for historical realities, even and especially in times, when our discipline is changing towards digital tools and Big Data.

Competing Claims and Composite Rulership

Let’s start with some facts1See my book for the following with more details and additional literature: https://www.degruyter.com/document/doi/10.1515/9783110748727/html: Around the beginning of 1254, Danylo Romanovych was crowned King of Rus’ by a Papal Legate. Danylo was the Duke of Halych-Volhynia, a political entity that came into being after his father Roman Mtislavich in 1199 united the two western principalities (Halych and Volhynia) within the Kyvian Rus (map 1). The Romanovych family would rule over Halych-Volhynia until the beginning of the 14th century. After several decades of power struggles, the region would finally been divided between the Kingdom of Poland and the Grand Duchy of Lithuania around 1366 (map 2). The southwestern parts of the former principality would later (1430/1434) become the Voivodship of Ruthenia within the Kingdom of Poland (map 3).

Map 1: Western Rus’ at the end of the 12th century (Wikimedia Commons)

But how would this story look like if we describe it within certain objects (items) and the relationship (properties) between them? We have two principalities, that were united in 1199. The new entity would then become a kingdom in 1254 and this kingdom would then be divided between the Kingdom of Poland and the Grand Duchy of Lithuania. The Polish parts would become the Voivodship of Ruthenia.

Map 2: The Struggle over the Rus’ian Principalities between the Kingdom of Poland and the Grand Duchy of Lithuania in the 14th century (Herder-Institut)

There are certain challenges to this linear narrative: Firstly, the two principalities weren’t united for good in 1199. In fact, when Roman Mtislavich died in 1205, several decades of struggle over the succession within the principalities followed. Secondly, when his son Danylo finally managed to stabilize his rulership and was crowned king, it would not transform the whole principality into a kingdom. The title of “King” was ad personam, as it was with his cotemporary Mindaugas in Lithuania in 1253. So Danylo was practically king, but not over a kingdom and none of his successors would later be called king as well. Thirdly, which date should we give for the transformation of this principality into the Polish voivodship? The area was conquered mostly between 1340 and 1366. It was briefly ruled by the Kingdom of Hungary, whose ruler Louis I would also be crowned King of Poland in 1370. The region was re-integrated into the kingdom of Poland in 1387/89 but it was only almost 50 years later, that the regular voivodship was established.

Map 3: The Polish Lithuanian Commonwealth in the 17th century (Wikimedia Commons)

To complicate matters even further, there was another ruler, who claimed to be king of this area. When the Hungarian King Andrew II secured the principality of Halych-Volhynia for the underaged Danylo, he began to add Rex Galiciae et Lodomeriae (Latin form of Halych-Volhynia) to his royal title. His son Coloman would even be crowned King of Halych-Volhynia in 1216. Even though he was only able to rule in Halych for a couple of years, the title Rex Galiciae et Lodomeriae would be claimed by Hungarian rulers until the early 15th century.

This claim was dormant for centuries but then was brought forth again in the late 18th century when the Polish-Lithuanian Commonwealth was divided between the Empires of Russia, Habsburg, and Prussia. The former voivodship of Ruthenia as well as the southern parts of Lesser Poland reaching until Cracow would be annexed by the Habsburg Monarchy (map 4).

Map 4: The Partitions of Poland-Lithuania 1772–1795 (Wikimedia Commons)

In search of a justification, Austrian legal historians came up with the old Hungarian claims towards Halych-Volhynia, given the fact, that the Empress Maria Theresia was Queen of Hungary as well. Therefore, the annexed territory would be transformed into the so called “Kingdom of Galicia and Lodomeria” (map 5). Ironically, this territory would now include areas, that where never a Rus’ian principality (Lesser Poland with Cracow) but would at the same time miss the whole area of Volhynia, which was never a part of the Polish voivodship of Ruthenia.

Map 5: The Kingdom of Galicia and Lodomeria within the Habsburg Monarchy 1897 (Wikimedia Commons)

To model this complex web of competing claims and composite rules properly poses quite a challenge. Obviously, you can’t describe the relationship between the Principality of Halych to the Principality of Halych-Volhynia with a mere P6 or P7 Property (“Continuation of” / “Continued by”) since it was no stable incorporation. After consulting with Olaf Simons, we agreed on applying the property “Linking back to” (P233 germ. “Entstehungsgeschichtlicher Zusammenhang”) that might be followed by a qualifier to describe this connection to the predecessor (P234), e. g. conquest or merging of rulership (Q704817), a personal union (Q220551) or a mere argumentative recourse (tba). This rather soft description can be used for many cases. To give another example: It would be wrong to say that the Personal Union of Poland and Lithuania after 1385 was succeeding the Kingdom of Poland and the Grand Duchy of Lithuania as both parts would remain distinct political entities at least until the real Union of Lublin 1569. And even afterwards, it was still formally two political bodies under one king with a joint foreign policy.
Following the current approach in FactGrid to avoid reciprocity, the property P233 should only be applied retrospectively, so from the younger backwards to the older entity. As much as I understand the importance of data contingency, I still think, it would also be very helpful to link certain political entities to later ones. In WikiData, this is done through the property P3842 “located in the present-day administrative territorial entity”. In FactGrid, this is sometimes done through the property P742 “Historical continuum”. Here, we should come up with a decision, whether to give this kind of information or not.

Another aspect mentioned was the differentiation between a royal title and a kingdom. It would be misleading to describe the Hungarian King Andrew II as King of Halych-Volhynia. There are some mentions of the so-called Regnum Russiae in the 13th and 14th century in Hungarian sources, but – despite the brief episode of 1370-1387 – there never was a stable Hungarian rulership over the area. The title, that was established during the reign of Andrew II must be characterized as a claim, that he tried to intensify with the coronation of his son but that would ultimately become only a diplomatic leverage. If we use the property “Career Statement” to connect a person with a certain title or office, we should work with a qualifier as well that would describe the usage of this title. I propose to create the property “Legal Character” (Rechtscharakter) to describe the quality behind a certain title. We could then create an item for royal titles, that are merely titular in nature, just as we already have for titular bishops (Q164311).

Bias towards Eastern Europe?

Eastern Europe seems to be particular rewarding for such reflexions since it offers many political entities that wouldn’t correspond to nowadays national states. On the contrary, one and the same premodern political entity can form an important part of different national historical narratives. This is especially visible for the Kyvian Rus’, which forms a contested source of historical identity both in the Russian Federation and Ukraine. But it’s also true for the Polish-Lithuanian Commonwealth, which would be a source of national narratives in Poland and Lithuania, but also in Belarus and Ukraine.2Timothy Snyder, The reconstruction of nations (New Haven, Conn.: Yale Univ. Press, 2003).

But one does not need to stay in Eastern Europe. Speaking of Sweden in the 18th century would mean to refer to an empire that comprised most of the surrounding areas of the Baltic Sea, including Finland, Latvia, parts of Estonia and even Russia.

Map 6: The Swedish Empire 1560 to 1815 (Wikimedia Commons)

But somehow, countries in Western or Northern Europe are considered to correspond much easier with contemporary states than in the East. Whereas the FactGrid item for Sweden (Q140553) simply uses the P2-Statement “Country” (Q21925), Ukraine is referred to as “state”, also with the respective P2-Statement “Sovereign State” (Q94416).

Instead of following Putin’s and other’s dark path towards a narrative of more or less “natural” states, we should use these examples as a reminder to differentiate – in analogue writing and in databases – between the different political entities we are describing. Are we talking about the contemporary sovereign state of Poland, are we talking about the Kingdom of Poland as a historical state or are we using the term more broadly to refer to a somehow blurry region? FactGrid already offers such a differentiation for the case of Germany, with different items for the “German Reich”, the “Weimar Republic” or the current “Federal Republic” but also with the item “Germany”, described as “German territories in the longer perspective” (Q140530). Following this model, I created an item for “Russia” to “describe the Russian territories that belonged to different states in a longer temporal perspective” (Q883327). Following the German example, I added the property “Subclass” (P420) to list the distinct political entities (historical and contemporary) that might be described as “Russia”. Here, it is important to stress, that those political entities were never only Russian. Since the expansion of the Grand Duchy of Moscow, this later Tsardom would include various people.3Andreas Kappeler, Rußland als Vielvölkerreich Entstehung – Geschichte – Zerfall (München: C.H. Beck, 2020). It’s so tragic that it took this ongoing war in Ukraine for the German and international public to realise the plurality of the Russian Federation or the Soviet Union. In Russian and German, there is the possibility to differentiate between русский/russkii (germ. russisch) for Russian and российский/rossiiskii (germ. russländisch) for the larger political entity that would include various people.

Summary

In this paper, I raised more questions instead of presenting answers. Premodern political entities are very difficult to describe. That is probably the reason, why FactGrid is still missing a distinct item for the Holy Roman Empire or the Habsburg Monarchy. It’s takes time to develop an idea, how to model these messy and fluid entities. But we should take the time to do it. The shift of Humanities towards Digital Methods is not only about collecting and analysing Big Data. We also must face the challenge of finding a new language to describe historical realities with the same historical accuracy that we would expect from a written paper. Over a hundred years ago, legal scholars of the Habsburg Empire faced similar problems, when they tried to transfer the dynamic political entity of the Habsburg composite rule into a sovereign state and a constitution. Scholars like Georg Jellinek and Hans Kelsen redefined legal theory in order to invent an accurate language to describe the states they lived in.4Natasha Wheatley, The life and death of states. Princeton (N. J.): Princeton University Press, 2023).

Now, we face a similar challenge to transfer historical realities into digital platforms such as FactGrid. One approach I discussed in this paper would be, to define properties, that offer enough flexibility to be applied to various cases and then describe them more precisely through qualifiers. The exchange over such decision in Item Discussions but also through articles like this is crucial. We need come up with a coherent digital language to describe incoherent historical realities.

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Erste Hilfe beim Zuordnen mittelalterlicher Ortsnamen (5770 Vorschläge)

Anfang des Jahres fragten wir (ich gab die Frage für Kathleen Schnabel und das Team Robert Gramsch-Stehfests ins Netz) die Welt der “Twitter Mediävisten” nach einem klugen Tipp, wie wir gut 3000 mittelalterliche Ortsnamen identifiziert bekämen. Es handelte sich um Ortsnennungen, die Studenten, die sich zwischen 1392 und 1450 an der Uni Erfurt einschrieben, zu ihren Namen in die Matrikellisten gaben, niedergeschrieben wohl immer nach Gehör.

Der Tweet war erstaunlich erfolgreich: 13.900 mal gesehen, 115 mal geliked, 100 mal weiterversandt. Hilfreiche Antworten kamen aus allen Richtungen.

Offenbar waren wir nicht die ersten, die an diesen besonderen Abgrund gerieten.


Aberwysczel
Abswinden
Adelenessen
Adelfessessen
Adenstede
Adernheym
Adirstete
Aemstelredam
Agghelbeke
Ahorn
Ailsfeldia
Akusgrann
…

Natürlich hätten wir einfach bei den Immatrikulationen, die wir verzeichneten, in einem eigenen Feld notieren können, was die Studenten als ihre Herkunftsorte angaben, respektive die Schreiber daraus machten. Das taten wir auch am Ende. Wer aber von den Studenten eines Jahres aus demselben Ort stammte? Wo das Einzugsgebiet der Uni lag? Wie es sich mit dem Aufstieg der Uni veränderte? – das alles ließ sich ohne Identifikationen der Orte nicht klarer ermessen.

Banal war es, die Ortsangaben in einem Google Spreadsheet allen Orten der Datenbank gegenüberzustellen und mit VLOOKUP (SVERWEIS) eine unimittelbare Zuordnung durchzuführen. Die Treffermenge fiel aber unbefriedigend schmal aus und war durchzogen von sich auftuenden unterschiedlichen Problemen. Gotha konnte in den Matrikeln als Gota oder Gotta auftauchen, nur ein einziger Buchstabe verhinderte in diesen Fällen das “matching”. Bei Namen wie Akusgrann lagen die Dinge dagegen komplexer. Hier sollte man wissen, dass der Ort lateinische auch als Aquisgranum und Aquae Grani bekannt war und so zu Eindeutschungen verleitete.

Michael Markert von der Thüringer Landesbibliothek Jena schlug mit einem YouTube Tutorial den Abgleich vor, der das Feld der Treffer in dieser misslichen Lage unmittelbar handhabbarer machte – eine GND/Lobid Anfrage, die einen mathematischen Buchstabenaustauschverfahren Varianten ins Kalkül brachte, mit denen sich alle geringfügigen Schreibunterschiede erst einmal auflösten:

Skurrile Treffer machten die sehr speziellen Schwächen dieses Angebots deutlich: Argentinische Studenten wollten sich da 90 Jahre vor der europäischen Entdeckung Südamerikas in Erfurt eingeschrieben haben, Studenten „de Argentina“, aus Straßburg.

Ein eigenes Problem blieben zudem die Orte mit aktuellen Namensgleichheiten. Dem Abgleich fehlten Wahrscheinlichkeitsparameter, Formen eines eigenen Kontextes: Von zwei Rothenburgs sollte das bei Fulda eher im Einzugsbereich der Erfurter Universität liegen als das an der Tauber oder das an der Wümme (entscheiden ließ sich das letztendlich jedoch nicht). In anderen Fällen war eher über Infrastrukturen nachzudenken: Wenn es zu einer Nennung ein Dorf und einen Ort mit mittelalterlicher Lateinschule gab, war vermutlich eher der Ort mit der Lateinschule der Entsender.

Am Ende blieb nichts übrig, als in einer Gruppensitzung alle Vorschläge zu überprüfen und bei vielen der Angaben historisches Wissen spielen zu lassen – bei 3000 Ortsnennungen ein gerade noch gangbarer Weg.

Das Endergebnis blieb eine Annäherung und erweist sich im Moment als vorurteilsbehaftet: Österreich, die Schweiz, die Niederlande sowie die ehemals deutschsprachigen Ostgebiete dürften in der folgenden Karte unterrepräsentiert sein (man kann in das Iframe hineinzoomen, die Karte wird bei jeder Browserauffrischung frisch aus den Datenbankeinträgen generiert). Was hier sichtbar wird, ist, dass wir Orte nach heutiger Nationalität gebündelt in die verschiedenen Schritte des Abgleichs brachten, um dabei annäherungsweise räumliche Nähe ins Spiel zu bringen.


Zoomfähige Kartendarstellung: Die Herkunftsorte der Erfurter Studentenjahrgänge 1392 bis 1450

Erst Blicke in die Biographien werden die Entscheidungen substantiieren können. Die Datenbank erlaubt es indes, Baustellen aufzumachen. Dies ist die Liste aller Orte, die wir im Moment für eine “manuelle” Überprüfung zurücklegten:

Doch ein nützliches Produkt am Ende

Dem Bedarf, der sich hier auftat, Rechnung tragend, spiegelten wir die durchgeführten Identifikationen am Ende auf die heutigen Ortsnamen zurück, so dass sie sich nun zwei neue Handhabungen ergeben:

Gibt man im Suchschlitz des MediaWikis einen Namen ein, den man nicht sofort einem heutigen zuweisen kann, so erhält man mögliche Treffer unmittelbar über die Alias-Funktion der Wikibase-Instanz angezeigt.

Ortszuweisung nach den Aliasangaben über den einfachen Suchschlitz

Spannender aber sollte die Liste unserer Zuweisungen sein. Sie lässt sich nun unmittelbar aus dem folgenden Fensterausschnitt als CSV, TSV oder JSON-Datei herunterladen (die Download-Links erscheinen am rechten Fensterrand im Mouseover; Quellennachweise finden sich in den einzelnen Datensätzen und können mit einer komplexeren Suche auch hinzugeladen werden):

Als TSV Datei heruntergeladen lässt sich die nun spaltenweise erscheinende Liste jeder eigenen in einem Excel- oder Google-Datenblatt gegenüberstellen und mit VLOOKUP/SVERWEIS auf einfache Art innerhalb des Datenblatts abgleichen.

Weihnachten rückt näher. Wunderbar wäre ein Tool, mit dem man Abfragen kontextualisieren könnte. Wir suchten Orte aus dem Spätmittelalter mit einer Fokussierung auf Erfurt und einer Privilegierung von Orten, die im Mittelalter über Schulen verfügten. Nicht einfach. Die Macher des GOV, des Genealogischen Ortsverzeichnisses sollten hier viel weiter sein – vielleicht dass wir einmal zusammen einen viel intelligenteren Service zu Ortsnamen auf die Beine stellen.

Publiziert im Rahmen des der NFDI4Memory Task Area “Data Connectivity”, Historisches Datenzentrum Halle, Projektnummer 501609550.

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Factgrid Federated: How to retrieve data from Wikidata and DBpedia from the Factgrid SPARQL endpoint

Unfortunately becoming an official source for federated {something missing} from Wikidata is not as easy as one writing Factgrid on a waiting list. More over the time perspective seems in unclear. {Gib mir den Absatz auf Deutsch…}

{Auch der nächste Satz, sags mir auf Deutsch und ich sags auf Englisch…} But: Becoming Factgrid becoming a starting point for federated queries to Wikidata and DBpedia is much easier. Thanks to Lucas Werkmeister from Wikimedia Deutschland the Factgrid SPARQL endpoint is now able to request data from Wikidata as well as DBpedia, the linked open data generated from Wikipedia articles.

So Factgrid is now not an isolated database anymore, but integrated in the linked open data universe. An easy example: Every Factgrid item that has a Wikidata QID can be queried for property-value statements from the both Wikidata and DBpedia.

How does it work? Let me explaint it quickly:

Use Prefixes as a gateway to other data sources

The main difference {between what and what?} is that you have to take into consideration the data sources with prefixes at the top of your query. A prefix is basically a signpost or gateway to other ontologies and data sources and must be included at the top of queries.

Since Factgrid uses the same software as Wikidata, the default it to use the prefix wd for items and wdt for properties in Factgrid, which is the default for Wikidata, hence the wd. This works fine if only one data source is used.

Integrating Wikidata in Factgrid queries, it makes sense to change the prefixes in such a way the data source is visible at first glance. I decided to use fg_ for Factgrid, wd_ for Wikidata and db_ for DBpedia. Of course, you can use any other prefix as signpost, like factgrid_item as long as you declare it as a prefix at the top, e.g. `PREFIX fgfactgrid_item: <https://database.factgrid.de/entity/>.

Possible prefixes:

# Factgrid
PREFIX fg: <https://database.factgrid.de/entity/>
PREFIX fgt: <https://database.factgrid.de/prop/direct/>
# DBPedia Categories
PREFIX dbc: <http://dbpedia.org/resource/Category:>
# dbpedia ontology
PREFIX dbo: <http://dbpedia.org/ontology/>
# dbpedia resource
PREFIX dbr: <http://dbpedia.org/resource/>
# Wikidata Prefixes
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wd: <http://www.wikidata.org/entity/>
# standard prefixes
PREFIX owl: <http://www.w3.org/2002/07/owl#>
PREFIX dct: <http://purl.org/dc/terms/>

Structure of federated queries

There are many to Rome and to federated queries. The ones I generated so far have the follwing basic structure:

  • set the prefixes
  • look up a specific item in Factgrid named fg_item
  • look for the Wikidata QID of the Factgrid item and convert this string it to an IRI called wd_item in order to use it for quering Wikidata or DBpedia
  • get the item in DBpedia using the OWL ontology with that specific Wikidata QID ?db_item owl:sameAs ?wd_item
  • define properties that are of interested as [VALUES](https://www.wikidata.org/wiki/Wikidata:SPARQL_tutorial#VALUES) and give them a name, e.g. relations_db
  • get the resulting value

Examples for federated queries

Magnus Hirscheld partners

Time for an example. Let’s search for unmarried partners of the famous German sexologist Magnus Hirschfeld in Factgrid, Wikidata and DBpedia. The query behind this link looks up the specific properties for unmarried partners in all three sources and delivers the name as well as an image. The technical parts included as comments.

As you can see, there are three lines. One is empty, it’s the first data source, Factgrid, that has nothing to offer for that request. The second row is from DBpedia, because the _db columns are filled, the first is from Wikidata (_wd) .

So there are two partners in those three data sources: Karl Giese in DBpedia and Li Shiu Tong in Wikidata. Both are correct, but neither Wikidata nor DBpedia have all information. Just by combining the sources we get the full picture.

A note on property Labels: I don’t know yet how to include both property labels from two sources (Factgrid and Wikidata), because both rely on the PREFIX wikibase: <http://wikiba.se/ontology#>.

Get all persons that are mentioned in a Factgrid item’s Wikipedia article and show their image

Like before, the example is Magnus Hirschfeld.

Link to the query

A tool to make mass comparisons

Relying on these query mechanisms I built a tool to compare Factgrid and Wikipedia statements in bulk. I make use of Factgrids P343, that offer the translation between Factgrid and Wikidata properties. In the first iteration of the app, it only works for properties relating to people.

The goal is to find missing relations on both Factgrid and Wikidata and create triples automatically that can be imported to Wikidata or Factgrid, including a source and timestamp.

For example, Factgrid’s Property for unmarried Partner is P117 and it’s corresponding Wikidata property P451 is entered on Factgrid. This let’s us search for all Factgrid Items that have a Wikidata QID and compare the partners on Factgrid with the partners on Wikidata.

As of today, June 27, 2022, there are six relations that are both in Factgrid as well as Wikidata. For example, that Lida Gustava Heymann was Anita Augspurg’s partner can be found on Factgrid and Wikidata.

Seven statements are only in Factgrid, but not in Wikidata, e.g. that Amalie Zephyrine von Salm-Kyrburg’s partner was Alexandre François Marie de Beauharnais. Since both, Salm-Kyrburg and de Beauharnais have a Wikidata QID in Factgrid, we can automatically build the import statements for Wikidata Quickstatements Tool. You get those import statements when you click “Download data for Wikidata import”. Copy the content of the downloaded file and paste it into the Quickstatement Tool. Besides the statement, it includes a source and timestamp.

It works the other way, too. There are three unmarried partnerships of Factgrid items in Wikidata, that Factgrid has not covered. One might want to have those statements in Factgrid as well and with downloading the file and importing it with Factgrids Quickstatements Tool it can be easily included in Factgrid’s data.

But this only works for items that already exist in Factgrid. You can detect them, when the column value looks like Factgrid, Wikidata. If there’s only value = Wikidata then it cannot be imported straightahead, because it only exists so far only in Wikidata. This is the case for Anne Louise Germaine de Staël’s partner Louis Marie de Narbonne-Lara in row 1. So even we the tool detects three partnerships in Wikidata missing in Factgrid, only two can be imported quickly.

In contrast to the tables showing the statements in both data sources and the statements only in Factgrid, there are no labels, just links for the values. It would be possible to fetch them from Wikidata, but it would take quite a time load them. In favor of loading time this information is missing.

It might be the case that you are only interesed in a certain subset of Factgrid items. Then you can add a filter in the text field on the left sidebar.

Try it yourself

The link to the app is apps.katharinabrunner.de/compare-factgrid-wikidata/. Try it yourself, I am looking forward to your feedback!

Soon, I want to extend the app such that it works on all other properties as well that allow a comparison between Factgrid and Wikidata.

Want to know more about federated queries?

The technical documentation of Wikidata is excellent and offers many, many examples. A starting point for federated queries can be found here.

Posted on

Imagine a Graph Query Helper for Graph Databases

[Link für Deutsche Übersetzung]

FactGrid is a graph database. If you run searches in such a database you should rather not think of a resource filled with interrelated tables (of people, places, organizations, documents…) – but of something more spatial, more geometric, more graphic.

Think of your own knowledge. You will not be able to give a table of all the names that have a meaning in your knowledge, or of all the places related to these names. Our knowledge is more like a web of interrelated objects. Nicolaus Copernicus? He is the man who wrote De revolutionibus. What else do you know? Maybe that he was born in Thorn, Polish Toruń, and that he studied at the Universities of Padua and Bolognia. I at least do not immediately know much more about the author who brought about the “Copernican Revolution”. That, of course, is an object that rings many more bells, with all the connections to other items of knowledge it has in my knowledge. I can add that these two universities were good places to study those subjects that were to become the natural sciences – but that again is knowledge on these objects, not on Copernicus, knowldge that got stuck in my knowledge as it added some more colour to my knowledge about Copernicus, the person. Think of interrelated objects hanging together in the wider mesh of your knowledge – of objects that link to each other like atoms in a molecule.

…an object with links to two other objects? That could be someone linked to her two parents. The graph would not look different if that was another person with his two daughters, or Copernicus with links to the two universities mentioned. Well, Copernicus studied at four universities, to be precise – but that is not the problem.

The problem is that the molecular model does not carry particularly well as it puts all the differences into the atoms, hence the various colours in images and the different connectivities of atoms in the typical three dimensional tool kits. In a database like FactGrid all the objects are structurally completely identical. They all are just “Items”: meaningless points, “nodes”, under Q-numbers counted up from 1 to infinity. The various and very specific Properties between the objects make all the differences in a graph database: “Fathers” are in FactGrid Items that have P141 “father” properties referring to them; mothers have P142 Properties linking from other items towards them.

In a triple-based database (which breaks down all knowledge into three-part statements) we will need no more than two sorts of components: You can take spheres for the objects of our knowledge, the “Items”, and arrows for the links that run between them – arrows as we have to express directions in the various statements.

Those who studied at the University of Jena have P160 “educating institution” statements leading from their Items to the University of Jena Item Q21880. This is the SPARQL script (see this link to see what it does):

SELECT ?Item ?ItemLabel WHERE {
   SERVICE wikibase:label { bd:serviceParam wikibase:language “[AUTO_LANGUAGE],en”. }
   ?Item wdt:P160 wd:Q21880.}


SPARQL is a wonderfully versatile language to send searches through graph databases but it is impossible to script even this most simple query without handbook knowledge. What is worse: You will need additional knowledge of our database to know that Jena’s University has this the Q-number Q21880 and that students must have P160 statements on them that will link to this University with the Q21880 indetifier.

The Wikimedia Query Helper is the coolest gadget as soon as you understand what a “Filter” can do for you in your query. Once you realise that this is the input field that will need the university in your specific query you can start to type “Univ…” and the autocomplete will lead you to the Q-number you are looking for. Select the Item you are interested in and the tool will already propose the “who studied here?” Property P160 as this is the most used Property leading to Q21880. It is fair to assume you are looking for people who studied at this university.

You can now ask for more information about these students as far as they are found on their Items, such as the dates of birth and death with both places in separate columns, and the names of their fathers and mothers. This is a search that uses the Query Helper:


And this is where the present Query Helper will leave you. The coordinate locations of the places of birth are on their respective Items (not on the student Items which you have been exploring so far). You need these coordinates to get a map representation, but the Query Helper does not show you how to extend your search into the related objects, nor does it show you how to bring qualifiers into your list (like the matriculation begin and end dates stated with many of the P160 links). It is also difficult to switch to reverse questions. You already know the person and now you want to know more about him, while you are still asked to use a filter…

One should have a graphic – a visual – query editor on a graph database

This is what the open question looks like: Who studied where? I put numbers in the circles to designate table columns.

If you are only interested in Jena University students, you should be able to specify that right on the university’s Item. Click into its sphere and type “University of Jena” into the circle:

You can now expand the query as you wish with clicks into the objects or the arrows, for example by asking for the “fathers” (P141) of these sutudents, who will appear in column 3 (this script):

And it will now be easy to get more information from the fathers – like which schools and universities did the fathers attend, again P160 (script link)?

One could also formulate the short-circuit question to get all the students who studied in Jena just as their fathers had done before:

I gave the arrows in different colours because they are the components that make all the difference in objects. You want to spot identical questions and similar objects in your searches.

Optional / Mandatory

Perhaps a simple exclamation mark on the Property arrows would be enough to mark statements that shall work as filters.

Qualifiers

Qualifying statements are a bright Wikibase invention. Any primary triple can become the object of specific, qualifying statements. That is basically the relative clause we need in such a language (for instance if we have a person who studied at four universities and we want to say from when to when on each case). If we want to keep the graphic repertoire lean, we could simply link the qualifying statements to the Properties – for example, to get two separate columns for the begin and end dates of a specific university matriculation:

Opening the toolbox

The toolbox had been open in these various searches. I used it so far to state where a specific Item had a specific value attached to it. We would use this toolbox for all the more complex visualisations. Imagine you want to get the religious backgrounds of all known Illuminati in a bubble chart. Ask for the Items that have a P91 membership statement connected to the Illuminati, Q10677. Then ask for their religious backgrounds. If you want a bubble chart you need a count of hits on each religion and denomination:

The toolbox should also be the place to create time frames. You could here specify ranges on data you have requested.

Just a thought…

A Postscript on how to use the right and left mouse buttons in the query builder

Visual scripting might be actually quite easy. With the left mouse button you create your first circle. It will come with a question mark in it.

Click into this circle with the left mouse button, and you can put a value into this circle, a label; it will replace the question mark.

Use your right hand mouse button to get a visual context menu from his point. It will come in the form of grey options to select. Two arrows are leading away from your Item, two are leading towards it. Each time you get an open offer with question marks to replace (or to leave there) and two specific arrows that will give you ideas of what is happening here:

With the left mouse button you can select the direction into which you want to move, the selected arrow and circle will switch to colour, the other three arrows will disappear. You are now free to continue with a click into the next Item or Property of your interest. Just as in the current Query Helper, you will always get a preview of 20 table rows, that will give you an idea of the results you are about to get on your search.

Seen only later…

Posted on

In einer Graphdatenbank müsste man eigentlich auch graphisch suchen können

[Link for English translation]

Das FactGrid ist eine Graphdatenbank. Das heißt, dass man sich die Datenlage in einer solchen Ressource besser nicht in Form von fünf oder zehn großen, aufeinander verweisenden Tabellen (zu Personen, Orten, Organisationen und Dokumenten etwa) vorstellt.

Das Wissen besteht in einer solchen Datenbank aus Wissensgegenständen – in Wikibase-Instanzen heißen sie „Items“ – und den Beziehungen zwischen ihnen, den „Properties“, sprich Eigenschaften, die diese Gegenstände an andere (oder auch an historische Daten, Links, Bild-Dateien oder Geokoordinaten binden).

Eine solche räumlich vernetzte Beziehung zwischen zwei Gegenständen kann man mit jedem Molekülbaukasten basteln. Hier ein Objekt mit Beziehungen zu zwei anderen. Das kann eine Person (die rote Kugel) sein mit Verbindung zu ihren Eltern (den beiden blauen Kugeln). Strukturell sieht das Gefüge aber nicht anders aus, wenn zu einer Person deren zwei Kindern erfasst sind, oder zwei Universitäten, an denen sie studierte.

Das Molekülmodell trägt nicht besonders gut. In einer Datenbank wie dem FactGrid sind alle Objekte vollkommen gleichartig. Sie alle sind monotone „Items“, die unter Q-Nummern hochgezählt werden. Erst die Aussagen zu ihnen bringen Unterschiede ins Spiel. Ein Vater ist jemand im FactGrid, wenn auf ihn von wo anders eine P141 „Vater“-Property verweist, auf „Mütter“ verweisen dagegen P142-Verbindungen.

In einer Tripel basierten Datenbank (die alles Wissen in dreigliedrige Aussagen zergliedert) genügen zwei Sorten von Bausteinen, etwa Kugeln für die Wissensgegenstände und, weil hier eben Bezugsrichtungen wichtig werden, Pfeile für die Verbindungen zwischen ihnen.

Alle Personen, die an der Universität Jena studierten, findet man, wenn man danach fragt, von welchen Items aus eine Aussage zur „ausbildenden Institution“ (P160) – auf das Item der „Universität Jena“ (Q21880) verweist. So (ausführbares Link) sieht die SPARQL-Suchanfrage aus, und die kann nun niemand so einfach „skripten“:

SELECT ?item ?itemLabel WHERE {
   SERVICE wikibase:label { bd:serviceParam wikibase:language “[AUTO_LANGUAGE],en”. }
   ?item wdt:P160 wd:Q21880.}

Wieso dies alles genau so zu schreiben ist, kann man ohne Handbuch nicht wissen, und man kann ohne Kenntnis der Datenbank auch nicht wissen, welche Q-Nummer man für die Jenaer Universität und welche P-Nummer man für die Aussage „hat hier studiert“ braucht.

Der Wikimedia Abfragehelfer (ist da bereits ein massiver Gewinn. Wenn einem klar ist, was man erreichen kann, wenn man zuerst „filtert“ und dann bestimmt, was einen an einzelnen Aussagen zu den herausgefilterten Objekten interessiert, kommt man mit dem Abfragehelfer erheblich viel weiter. In das Filterfeld kann man etwa „Uni Jena“ eingeben, ohne die Q-Nummer zu kennen. Der Autocomplete lenkt einen beim Eintippen komfortabel. Der Abfragehelfer ahnt bereits, dass einem interessiert, wer hier studierte – das ist die Property, die am häufigsten auf die Uni Jena verweist, sie kommt als erster Property-Vorschlag.

Wenn man nun mehr zu den herausgefilterten Studenten wissen will, kann man von deren jeweiligen Items Aussagen beziehen – etwa die Geburtsdaten, die Geburtsorte, die Sterbedaten und Sterbeorte, Väter und Mütter:

Es ist dies aber auch schon der Punkt, an der Abfragehelfer die Waffen streckt. Wenn man wissen will, wo die Orte liegen (um sie auf eine Landkarte zu spiegeln), muss man durch die Orte hindurch fragen, denn auf deren Items liegen die Geokoordinaten und hier hilft einem der Abfragehelfer nicht mehr weiter.

Es ist ebenso wenig möglich, im Abfragehelfer einen Qualifier hinzuzusetzen, um etwa den Studienbeginn mit abzufragen. Auch die einfache Umkehr der Fragen ist nicht vorgesehen: Ich kenne eine bestimmte Person und will wissen, wo sie von wann bis wann studierte.

Eigentlich sollte zur Graphdatenbank ein Visual Editor gehören…

Man müsste Graphdatenbank mit Skizzen der Beziehungen zwischen den Objekten befragen können. Hier die banalste Frage nach Allen, die überhaupt irgendeine Ausbildungseinrichtung besuchten. Wer waren sie, und welche Einrichtungen waren das?

Wenn uns nur Studenten der Uni Jena interessieren, sollten wir das für die zweite Kugel notieren können. Man tippt in den Kreis oder stellt es mit dem Werkzeugkasten klar: der zweite Gegenstand in diesem Spiel soll die Uni Jena sein:

Man kann jede solche Anfrage nun beliebig erweitern etwa, indem man von den Studenten aus die Frage nach deren Vätern (P141) stellt, sie sollen hier in Tabellenspalte 3 gelistet werden:

Und man könnte nun sehr einfach den Schritt tun, der mit dem aktuellen Abfragehelfer so leicht nicht mehr zu machen ist: die nächste Frage an die Väter ansetzen. Von welchen (wieder P160) Institutionen wurden diese Väter eigentlich ausgebildet?

Man könnte die Frage auch kurzschließen, um zu erfassen, welche Studenten genau wie ihre Väter in Jena studierten:

Ich gab den Dreiecken verschiedene Farben, um sichtbar zu machen, wenn im Gefüge dieselben Fragen an verschiedenen Stellen gestellt werden (und damit strukturell ähnliche Gegenstände anspielen).

Optional / Verpflichtend

Vielleicht würde man in den Property-Dreiecken mit einem Ausrufezeichen notieren, wenn eine Aussage nicht optional, sondern verpflichtend für alle Funde gelten soll.

Qualifier

Qualifier müssten in der Visualisierung gar nicht viel komplexer sein. Hier wird jeweils ein einzelnes Statement zum Gegenstand neuer Statements. Wenn wir das graphische Repertoire schlank halten wollen, könnten wir die hinzukommenden Aussagen einfach an die vermittelnde Property binden – etwa, um bei den Studenten in zwei eigenen Spalten zu notieren, was die Qualifier P49 und P50 zu deren jeweiligem Studienbeginn und -Ende an dieser Uni notieren:

Filter und gezielte Darstellungen

Ich ließ in den letzten Suchen bereits den aufgeklappten Werkzeugkasten mitlaufen. Der nun sehr viel schlanker Befunde weiterverarbeiten. Eine Suche könnte etwa bei den Mitgliedern (P91) des Illuminatenordens (Q10677) erfassen, welchen religiösen Hintergründen (P172) sie entstammten. Bei einer Statistik, etwa einer Bubble Chart, würden wir die Zahl der einzelnen Treffer wissen wollen:

Denkbar nicht minder, dass man bei Zeitangaben Zeitfenster notieren kann, Werte die größer oder kleiner als angegeben sein müssen, um Befunde ins zeitspezifische Bild zu bringen. Spätestens bei solchen Suchen wird allen, die da schon einmal mit SPARQL hantierten und Aussagen verschachtelten klarer, dass der visuelle Query Editor sehr viel intuitiver und auch sehr viel viel schlanker erfassen würde, was einen bei einer Suche interessiert. Man würde damit spielen können, sich an Befunde herantasten können. Man würde es lernen, in den Datenstrukturen zu denken.

Mal so zum Nachdenken…

PS. Rechte und linke Maustaste – wie man im Visual Editor arbeitet

Wie würde man im Visual Editor seine Suchanfragen schreiben? Vielleicht ganz einfach: Mit der linken Maustaste setzt man einen Kreis mit Fragezeichen darinnen.

Klicke ich mit der linken Maustaste in diesen Kreis, kann ich dort etwas hineinschreiben und das Fragezeichen durch Text ersetzen.

Klicke ich mit der rechten Maustaste in den Kreis, scheinen grau vier Erweiterungsoptionen auf: Zwei Pfeile gehen von meinem Kreis weg, zwei Pfeile führen zu ihm hin. Jedes Mal gibt es zwei offene Angebote mit lediglich einem Fragezeichen darin, und zwei Angebote (zur Erklärung, was hier geschieht), bei denen Muster-Text gegeben ist:

Mit der linken Maustaste kann ich die Richtung meiner Wahl anklicken, diese erscheint jetzt farbig, die anderen drei bislang grauen Pfeile werden damit unsichtbar. Ich kann nun fortfahren und Fragezeichen (von Properties oder Items) durch Text ersetzen, oder auf einen Pfeil oder Kreis klicken und mir mögliche Erweiterungen von hier aus anzeigen lassen.

Wie im aktuellen Abfragehelfer erhalte ich immer eine Vorschau von 20 Zeilen Tabelle, mit der ich sehe, was ich hier soeben getan habe.

Posted on

How to map itineraries on FactGrid — and Robinson Crusoe’s eight voyages

William Taylor’s typesetter stumbled over the date which Robinson Crusoe’s manuscript was spelling out for his page 46: 1659, “the same Day eight Year that I went from my Father and Mother at Hull“. Either this was a mistake or he had been wrong with the date that was now stated on page 7. There Crusoe was claiming that he had left his parents in 1661. Everyone was in a hurry, so he left two blanks for further clarification, the readers would be able to insert the right date once that was clearer.

First edition of Robinson Crusoe, 1719, omitted dates on p. 46.

The Errata at the end eventually settled the question: It should have been 1651 on page 7. No one had the nerves to replace 16 octavo pages for the correct reading.

The book that was to become a bestseller within less than two weeks was packed with historical detail. If indeed the author was born on the 30th of September 1632, he had to be a man of 86 years by now (DeFoe was in his late 50s, just by the way). Crusoe’s eight voyages came with numerous internal dates and even with geographic coordinates where the author had to spot his island for instance.

The itineraries are a good test ground for visualised searches on FactGrid.

Q219323 is our item for the volume as it was published on the 25h of April 1719. To avoid the interlacing of information I generated an item for the book, an item for Crusoe, the man (Q230282) eight items for his voyages (Q393587 to Q393594) and even an item for Crusoe’s island. All these items have specific context statements on them that allow to single them out as a rather fictional subject matter. Creating the individual items has, at the same moment, the charm to allow the use of all the properties we have created for “real” things.

James Heald and Bruno Belhoste provided the script I am employing in the following searches. Ignore the script’s complexity. The nice thing about this script is that you can modify it to run it on your own Q-numbers. This jpg shows you where you will have to list your items:

SPARQL script to bring Robinson Crusoes first eight voyages onto a map

No need to copy the script by hand. This is the search: Crusoe’s first eight voyages, that opens the Query Service with this very script. Press the blue button and you get the map of all eight journeys in one picture. Replace the Q-numbers and you will have your own itineraries on the map.

The script is looking into the items listed in lines 7-14. On these items it is looking for P296 statements where the person or group of travellers were staying. The query goes from there into these places to collect the various geographic coordinates. The visualisation will connect these with lines in the sequence of dates that should be given on the various stays, whether as dates of departure (P50), of arrivals (P49) or just as dates (P106) — all three properties are taken into account in line 16. Take a look into the item for the first voyage to see how you have to inform your item(s).

Screenshot FactGrid item with dates

And this is the picture which the query will create:


All eight voyages of Robinson Crusoe’s volume 1 in one picture.

You can change the colours: The information for them is on each item (where you will find a map link with the colour in plain text).

Where do I get the code to embed such an image on web pages?

The picture above was not a jpg. You could zoom in and even edit the SPARQL query that generated the visualisation. Whenever you perform a search on FactGrid you cannot only download the data, but you can also get the query as embedded code:

Screenshot, where the embedded code is to be found.

And if your author does not give any dates, just the different places one by one? — like Gil Blas (Q390697) in his history? No problem. You can sort the locations with P499 statements one by one or create any kind of superior sort string as I did in the following Gil Blas search. I am using here the book’s segmentation: 1-1-14 is volume 1, book 1, chapter 14. Replace the P49, P50, P106 date properties for the P499 (number) or P101 (sort string) query to get the respective series of events.

The visualisation is, as I see it right now (on 4 February 2022), incomplete. I am still reading these books slowly whenever I have nothing better to do, and Gil Blas has just left Madrid. I choose this example as it demonstrates the advantage of embedded code.


Tracking Gil Blas of Santillana.

The embedded visualisations always give their pictures as the database provides them the moment the page is opened. The windows are communicating with the database — what is even better: you can communicate with the database right here on this WordPress blog, as the each of the embedded images comes with a menue (on its right hand side, hover over it) that gives you direct access to the SPARQL search engine. Correct or add data on the database (you need an account to do that) and the new information will appear wherever an embedded visualisation will be checking the database — today or over the next years.

More than aesthetics…

One would love to give such visualisations on old maps. click the following link for the visualisation of Crusoe’s eight voyages on https://mappingwriting.com/. My Guinea has moved from the present Republic of Guinea to the early-18th-century location as given on the maps DeFoe could get in London:

“Negroland and Guinea with the European Settlements, Explaining what belongs to England, Holland, Denmark, etc”. By H. Moll Geographer (Printed and sold by T. Bowles next ye Chapter House in St. Pauls Church yard, & I. Bowles at ye Black Horse in Cornhill, 1729, orig. published in 1727)

A line from DeFoe’s London to this Guinea has the smell of modern air traffic. Crusoe’s captains were travelled along the coast lines wherever they could. Yet following their practices is ugly as we do not have these routes in the machine. We are connecting locations, and here I have already interpolated two costal places and two groups of islands to avoid the straight trans-African passage without a conformation from the book.


Crusoes first two journeys.

The nice thing about creating all this on FactGrid is that you can add any amount of further information on these stays and journeys, like the page references, connections to other items or in depth information on places. So lots of things to play with and to do with your own data on FactGrid.

See also

  • “Art collectors and the Holocaust: itineraries from birth to death”, at Open Art Data… Linking Databases To Detect Looted Art, Dec 19, 2021 https://www.openartdata.org/2021/12/art-collectors-and-holocaust.html
    • Posted on

    Filling a Wikibase instance with millions of data

    As more and more Wikibase instances are cropping up we are seeing attempts to start them with masses of data from already existing data bases that want to switch to the new software.

    Experimenting I tried to find a faster way to insert a huge amount of items into a Wikibase instance. I have not been able to insert more than two or three statements per second using the ‘official’ tools, such as QuickStatements or the WDI library.

    Therefore, I am inserting the data directly into the MySQL database used by Wikibase.

    The process consists of these steps:

    • generate the data for an item in JSON
    • determine the next Q number and update the JSON item data accordingly
    • insert data into the various database tables

    However, if you do this without a transaction it is still terrible slow. In my setup only 120 items per minute. However, if I wrap the inserts into a transaction I was able to insert 33,000 items/minute.

    Steps to run the experiment

      mysql:
    image: mariadb:10.3
    restart: unless-stopped
    ports:
      - "3306:3306"
    volumes:
    • Start the containers: docker-compose up and wait until you see lines ending like:
    [main] INFO  o.w.q.r.t.change.RecentChangesPoller - Got no real changes
[main] INFO  org.wikidata.query.rdf.tool.Updater - Sleeping for 10 secs

    For me it took a minute to insert 100 items without a transaction and 25 seconds to insert 10,000 items with a transaction.

    first published at https://github.com/jze/wikibase-insert/