Category Archives: Project

ImageSnippets and DBpedia

 by Margaret Warren 

The following post introduces to you ImageSnippets and how this tool profits from the use of DBpedia.

ImageSnippets – A Tool for Image Curation

For over two decades, ImageSnippets has been evolving as an ontology and data-driven framework for image annotation research. Representing the informal knowledge people have about the context and provenance of images as RDF/linked data is challenging, but it has also been an enlightening and engaging journey in not only applying formal semantic web theory to building image graphs but also to weave together our interests with what others have been doing in the field of semantic annotation and knowledge graph building over these many years. 

DBpedia provides the entities for our RDF descriptions

Since the beginning, we have always made use of DBpedia and other publicly available datasets to provide the entities for use in our RDF descriptions.  Though ImageSnippets can be used to build special vocabularies around niche domains, our primary research is around relation ontology building and we prefer to avoid the creation of new entities unless we absolutely can not find them through any other service.

When we first went live with our basic system in 2013, we began hand-building tens of thousands of triples using terms primarily from DBpedia (the core of the linked data cloud.) While there would often be an overlap of terms with other datasets – almost a case of too many choices – we formed a best practice of preferentially using DBpedia terms as often as possible, because they gave us the most utility for reasoning using the SKOS concepts built into the DBpedia service. We have also made extensive use of DBpedia Spotlight for named-entity extraction.

How to combine DBpedia & Wikidata and make it useful for ImageSnippets

But the addition of the Wikidata Query Service over the past 18 months or so has now given us an even more unique challenge: how to work with both! Since DBpedia and Wikidata both have class relationships that we can reason from, we found ourselves in a position to be able to examine both DBpedia and Wikidata in concert with each other through the use of mapping techniques between the two datasets.

How it works: ImageSnippets & DBpedia

When an image is saved, we build inference graphs over results from both DBpedia and Wikidata. These graphs can be revealed with simple SPARQL queries at our endpoint and queries from subclasses, taxons and SKOS concepts can find image results in our custom search tool.  We have also just recently added a pathfinder utility – highly useful for semantic explainability as it will return the precise path of connections from an originating source entity to the target entity that was used in our custom image search.

Sometimes a query will produce very unintuitive results, and the pathfinder tool enables us to quickly locate semantic errors which lead to clearly erroneous misclassifications (for example, a search for the Wikidata subclass of ‘communication medium’ reveals images of restaurants and hotels because of misclassifications in Wikidata.) In this way we can quickly troubleshoot the results of queries, using the images as visual cues to explore the accuracy of the semantic modelling in both datasets.


We are very excited with the new directions that we feel can come of our knitting together of the two knowledge graphs through the use of our visual interface and believe there is a great potential for ImageSnippets to serve a more complex role in cleaning and aligning the two datasets, using the images as our guides.

A big thank you to Margaret Warren for providing some insights into her work at ImageSnippets.

Yours,

DBpedia Association

GlobalFactSync and WikiDataCon2019

We will be spending the next three days in Berlin at WikidataCon 2019, the conference for open data enthusiasts. From October 24th till 26th we will be presenting the latest developments and first results of our work in the GlobalFactSyncRE-Project. 

Short Project Intro

Funded by the Wikimedia Foundation, the project started in June 2019 and has two goals:

  • Answer the following questions:
    • How is data edited in Wikipedia and Wikidata?
    • Where does it come from?
    • How can we synchronize it globally?
  • Build an information system to synchronize facts between all Wikipedia language-editions, Wikidata, DBpedia and eventually multiple external sources, while also providing respective references. 

In order to help Wikipedians to maintain their infoboxes, check for factual correctness, and also improve data in Wikidata, we use data from Wikipedia infoboxes of different languages, Wikidata, and DBpedia and fuse them into our PreFusion dataset (in JSON-LD). More information on the fusion process, which is the engine behind GFS, can be found in the FlexiFusion paper.

Can’t join the conference or want to find out more about GlobalFactSync?

No problem, the poster we are presenting at the conference is currently available here and will soon be available here. Additionally, why not go through our project timeline, follow up on our progress so far and find out what’s coming up next.

In case you have specific questions regarding GlobalfactSync or even some helpful feedback just ping us via dbpedia@infai.org. We also have our new DBpedia Forum, home to the DBpedia Comunity, which just waits for you to initialize a discussion around GlobalFactSync. Why not start it now?

For general DBpedia news and updates follow us on Twitter.

…And if you are in Berlin at WikiDataCon2019 stop by our poster and talk to our developers. They are looking forward to vital exchanges with you.

All the best

yours,


DBpedia Association


Global Fact Sync – Synchronizing Wikidata & Wikipedia’s infoboxes

How is data edited in Wikipedia/Wikidata? Where does it come from? And how can we synchronize it globally?  

The GlobalFactSync (GFS) Project — funded by the Wikimedia Foundation — started in June 2019 and has two goals:

  • Answer the above-mentioned three questions.
  • Build an information system to synchronize facts between all Wikipedia language-editions and Wikidata. 

Now we are seven weeks into the project (10+ more months to go) and we are releasing our first prototypes to gather feedback. 

How – Synchronization vs Consensus

We follow an absolute “Human(s)-in-the-loop” approach when we talk about synchronization. The final decision whether to synchronize a value or not should rest with a human editor who understands consensus and the implications. There will be no automatic imports. Our focus is to drastically reduce the time to research all references for individual facts.

A trivial example to illustrate our reasoning is the release date of the single “Boys Don’t Cry” (March 16th, 1989) in the English, Japanese, and French Wikipedia, Wikidata and finally in the external open database MusicBrainz.  A human editor might need 15-30 minutes finding and opening all different sources, while our current prototype can spot differences and display them in 5 seconds.

We already had our first successful edit where a Wikipedia editor fixed the discrepancy with our prototype: “I’ve updated Wikidata so that all five sources are in agreement.” We are now working on the following tasks:

  • Scaling the system to all infoboxes, Wikidata and selected external databases (see below on the difficulties there)
  • Making the system:
    •  “live” without stale information
    • “reliable” with less technical errors when extracting and indexing data
    • “better referenced” by not only synchronizing facts but also references 

Contributions and Feedback

To ensure that GlobalFactSync will serve and help the Wikiverse we encourage everyone to try our data and micro-services and leave us some feedback, either on our Meta-Wiki page or via email. In the following 10+ months, we intend to improve and build upon these initial results. At the same time, these microservices are available to every developer to exploit it and hack useful applications. The most promising contributions will be rewarded and receive the book “Engineering Agile Big-Data Systems”. Please post feedback or any tool or GUI here. In case you need changes to be made to the API, please let us know, too.
For the ambitious future developers among you, we have some budget left that we will dedicate to an internship. In order to apply, just mention it in your feedback post. 

Finally, to talk to us and other GlobalfactSync-Users you may want to visit WikidataCon and Wikimania, where we will present the latest developments and the progress of our project. 

Data, APIs & Microservices (Technical prototypes) 

Data Processing and Infobox Extraction

For GlobalFactSync we use data from Wikipedia infoboxes of different languages, as well as Wikidata, and DBpedia and fuse them to receive one big, consolidated dataset – a PreFusion dataset (in JSON-LD). More information on the fusion process, which is the engine behind GFS, can be found in the FlexiFusion paper. One of our next steps is to integrate MusicBrainz into this process as an external dataset. We hope to implement even more such external datasets to increase the amount of available information and references. 

First microservices 

We deployed a set of microservices to show the current state of our toolchain.

  • [Initial User Interface] The GlobalFactSync UI prototype (available at http://global.dbpedia.org) shows all extracted information available for one entity for different sources. It can be used to analyze the factual consensus between different Wikipedia articles for the same thing. Example: Look at the variety of population counts for Grimma.
  • [Reference Data Download] We ran the Reference Extraction Service over 10 Wikipedia languages. Download dumps here.
  • [ID service] Last but not least, we offer the Global ID Resolution Service. It ties together all available identifiers for one thing (i.e. at the moment all DBpedia/Wikipedia and Wikidata identifiers – MusicBrainz coming soon…) and shows their stable DBpedia Global ID. 

Finding sync targets

In order to test out our algorithms, we started by looking at various groups of subjects, our so-called sync targets. Based on the different subjects a set of problems were identified with varying layers of complexity:

  • identity check/check for ambiguity — Are we talking about the same entity? 
  • fixed vs. varying property — Some properties vary depending on nationality (e.g., release dates), or point in time (e.g., population count).
  • reference — Depending on the entity’s identity check and the property’s fixed or varying state the reference might vary. Also, for some targets, no query-able online reference might be available.
  • normalization/conversion of values — Depending on language/nationality of the article properties can have varying units (e.g., currency, metric vs imperial system).

The check for ambiguity is the most crucial step to ensure that the infoboxes that are being compared do refer to the same entity. We found, instances where the Wikipedia page and the infobox shown on that page were presenting information about different subjects (e.g., see here).

Examples

As a good sync target to start with the group ‘NBA players’ was identified. There are no ambiguity issues, it is a clearly defined group of persons, and the amount of varying properties is very limited. Information seems to be derived from mainly two web sites (nba.com and basketball-reference.com) and normalization is only a minor issue. ‘Video games’ also proved to be an easy sync target, with the main problem being varying properties such as different release dates for different platforms (Microsoft Windows, Linux, MacOS X, XBox) and different regions (NA vs EU).

More difficult topics, such as ‘cars’, ’music albums’, and ‘music singles’ showed more potential for ambiguity as well as property variability. A major concern we found was Wikipedia pages that contain multiple infoboxes (often seen for pages referring to a certain type of car, such as this one). Reference and fact extraction can be done for each infobox, but currently, we run into trouble once we fuse this data. 

Further information about sync targets and their challenges can be found on our Meta-Wiki discussion page, where Wikipedians that deal with infoboxes on a regular basis can also share their insights on the matter. Some issues were also found regarding the mapping of properties. In order to make GlobalFactSync as applicable as possible, we rely on the DBpedia community to help us improve the mappings. If you are interested in participating, we will connect with you at http://mappings.dbpedia.org and in the DBpedia forum.  

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