Tag Archives: Linked Data

SEMANTiCS 2019 Interview: Katja Hose

Today’s post features an interview with our DBpedia Day keynote speaker Katja Hose, a Professor of Computer Science at Aalborg University, Denmark. In this Interview, Katja talks about increasing the reliability of Knowledge Graph Access as well as her expectations for SEMANTiCS 2019

Prior to joining Aalborg University, Katja was a postdoc at the Max Planck Institute for Informatics in Saarbrücken. She received her doctoral degree in Computer Science from Ilmenau University of Technology in Germany.

Can you tell us something about your research focus?

The most important focus of my research has been querying the Web of Data, in particular, efficient query processing over distributed knowledge graphs and Linked Data. This includes indexing, source selection, and efficient query execution. Unfortunately, it happens all too often that the services needed to access remote knowledge graphs are temporarily not available, for instance, because a software component crashed. Hence, we are currently developing a decentralized architecture for knowledge sharing that will make access to knowledge graphs a reliable service, which I believe is the key to a wider acceptance and usage of this technology.

How do you personally contribute to the advancement of semantic technologies?

I contribute by doing research, advancing the state of the art, and applying semantic technologies to practical use cases.  The most important achievements so far have been our works on indexing and federated query processing, and we have only recently published our first work on a decentralized architecture for sharing and querying semantic data. I have also been using semantic technologies in other contexts, such as data warehousing, fact-checking, sustainability assessment, and rule mining over knowledge bases.

Overall, I believe the greatest ideas and advancements come when trying to apply semantic technologies to real-world use cases and problems, and that is what I will keep on doing.

Which trends and challenges do you see for linked data and the semantic web?

The goal and the idea behind Linked Data and the Semantic Web is the second-best invention after the Internet. But unlike the Internet, Linked Data and the Semantic Web are only slowly being adopted by a broader community and by industry.

I think part of the reason is that from a company’s point of view, there are not many incentives and added benefit of broadly sharing the achievements. Some companies are simply reluctant to openly share their results and experiences in the hope of retaining an advantage over their competitors. I believe that if these success stories were shared more openly, and this is the trend we are witnessing right now, more companies will see the potential for their own problems and find new exciting use cases.

Another particular challenge, which we will have to overcome, is that it is currently still far too difficult to obtain and maintain an overview of what data is available and formulate a query as a non-expert in SPARQL and the particular domain… and of course, there is the challenge that accessing these datasets is not always reliable.

As artificial intelligence becomes more and more important, what is your vision of AI?

AI and machine learning are indeed becoming more and more important. I do believe that these technologies will bring us a huge step ahead. The process has already begun. But we also need to be aware that we are currently in the middle of a big hype where everybody wants to use AI and machine learning – although many people actually do not truly understand what it is and if it is actually the best solution to their problems. It reminds me a bit of the old saying “if the only tool you have is a hammer, then every problem looks like a nail”. Only time will tell us which problems truly require machine learning, and I am very curious to find out which solutions will prevail.

However, the current state of the art is still very far away from the AI systems that we all know from Science Fiction. Existing systems operate like black boxes on well-defined problems and lack true intelligence and understanding of the meaning of the data. I believe that the key to making these systems trustworthy and truly intelligent will be their ability to explain their decisions and their interpretation of the data in a transparent way.

What are your expectations about Semantics 2019 in Karlsruhe?

First and foremost, I am looking forward to meeting a broad range of people interested in semantic technologies. In particular, I would like to get in touch with industry-based research and to be exposed 

The End

We like to thank Katje Hose for her insights and are happy to have her as one of our keynote speakers.

Visit SEMANTiCS 2019 in Karlsruhe, Sep 9-12 and get your tickets for our community meeting here. We are looking forward to meeting you during DBpedia Day.

Yours DBpedia Association

A year with DBpedia – Retrospective Part Two

Retrospective Part II. Welcome to the second part of our journey around the world with DBpedia. This time we are taking you to Greece, Germany, to Australia and finally France.

Let the travels begin.

Welcome to Thessaloniki, Greece & ESWC

DBpedians from the Portuguese Chapter presented their research results during ESWC 2018 in Thessaloniki, Greece.  the team around Diego Moussalem developed a demo to extend MAG  to support Entity Linking in 40 different languages. A special focus was put on low-resources languages such as Ukrainian, Greek, Hungarian, Croatian, Portuguese, Japanese and Korean. The demo relies on online web services which allow for an easy access to (their) entity linking approaches. Furthermore, it can disambiguate against DBpedia and Wikidata. Currently, MAG is used in diverse projects and has been used largely by the Semantic Web community. Check the demo via http://bit.ly/2RWgQ2M. Further information about the development can be found in a research paper, available here

 

Welcome back to Leipzig Germany

With our new credo “connecting data is about linking people and organizations”, halfway through 2018, we finalized our concept of the DBpedia Databus. This global DBpedia platform aims at sharing the efforts of OKG governance, collaboration, and curation to maximize societal value and develop a linked data economy.

With this new strategy, we wanted to meet some DBpedia enthusiasts of the German DBpedia Community. Fortunately, the LSWT (Leipzig Semantic Web Tag) 2018 hosted in Leipzig, home to the DBpedia Association proofed to be the right opportunity.  It was the perfect platform to exchange with researchers, industry and other organizations about current developments and future application of the DBpedia Databus. Apart from hosting a hands-on DBpedia workshop for newbies we also organized a well-received WebID -Tutorial. Finally,  the event gave us the opportunity to position the new DBpedia Databus as a global open knowledge network that aims at providing unified and global access to knowledge (graphs).

Welcome down under – Melbourne Australia

Further research results that rely on DBpedia were presented during ACL2018, in Melbourne, Australia, July 15th to 20th, 2018. The core of the research was DBpedia data, based on the WebNLG corpus, a challenge where participants automatically converted non-linguistic data from the Semantic Web into a textual format. Later on, the data was used to train a neural network model for generating referring expressions of a given entity. For example, if Jane Doe is a person’s official name, the referring expression of that person would be “Jane”, “Ms Doe”, “J. Doe”, or  “the blonde woman from the USA” etc.

If you want to dig deeper but missed ACL this year, the paper is available here.

 

Welcome to Lyon, France

In July the DBpedia Association travelled to France. With the organizational support of Thomas Riechert (HTWK, InfAI) and Inria, we finally met the French DBpedia Community in person and presented the DBpedia Databus. Additionally, we got to meet the French DBpedia Chapter, researchers and developers around Oscar Rodríguez Rocha and Catherine Faron Zucker.  They presented current research results revolving around an approach to automate the generation of educational quizzes from DBpedia. They wanted to provide a useful tool to be applied in the French educational system, that:

  • helps to test and evaluate the knowledge acquired by learners and…
  • supports lifelong learning on various topics or subjects. 

The French DBpedia team followed a 4-step approach:

  1. Quizzes are first formalized with Semantic Web standards: questions are represented as SPARQL queries and answers as RDF graphs.
  2. Natural language questions, answers and distractors are generated from this formalization.
  3. We defined different strategies to extract multiple choice questions, correct answers and distractors from DBpedia.
  4. We defined a measure of the information content of the elements of an ontology, and of the set of questions contained in a quiz.

Oscar R. Rocha and Catherine F. Zucker also published a paper explaining the detailed approach to automatically generate quizzes from DBpedia according to official French educational standards. 

 

 

Thank you to all DBpedia enthusiasts that we met during our journey. A big thanks to

With this journey from Europe to Australia and back we provided you with insights into research based on DBpedia as well as a glimpse into the French DBpedia Chapter. In our final part of the journey coming up next week, we will take you to Vienna,  San Francisco and London.  In the meantime, stay tuned and visit our Twitter channel or subscribe to our DBpedia Newsletter.

 

Have a great week.

Yours DBpedia Association

The DBpedia Databus – transforming Linked Data into a networked data economy

Working with data is hard and repetitive. That is why we are more than happy to announce the launch of the alpha version of our DBpedia Databus, a way that simplifies working with data. 

We have studied the data network for already 10 years and we conclude that organizations with open data are struggling to work together properly. Even though they could and should collaborate, they are hindered by technical and organizational barriers. They duplicate work on the same data. On the other hand, companies selling data cannot do so in a scalable way. The consumers are left empty-handed and trapped between the choice of inferior open data or buying from a jungle-like market. 

We need to rethink the incentives for linking data

Vision

We envision a hub, where everybody uploads data. In that hub, useful operations like versioning, cleaning, transformation, mapping, linking, merging, hosting are done automagically on a central communication system, the bus, and then again dispersed in a decentral network to the consumers and applications.  On the Databus, data flows from data producers through the platform to the consumers (left to right), any errors or feedback flows in the opposite direction and reaches the data source to provide a continuous integration service and improves the data at the source.

The DBpedia Databus is a platform that allows exchanging, curating and accessing data between multiple stakeholders. Any data entering the bus will be versioned, cleaned, mapped, linked and its licenses and provenance tracked. Hosting in multiple formats will be provided to access the data either as dump download or as API.

Publishing data on the Databus means connecting and comparing your data to the network

If you are grinding your teeth about how to publish data on the web, you can just use the Databus to do so. Data loaded on the bus will be highly visible, available and queryable. You should think of it as a service:

  • Visibility guarantees, that your citations and reputation goes up.
  • Besides a web download, we can also provide a Linked Data interface, SPARQL-endpoint, Lookup (autocomplete) or other means of availability (like AWS or Docker images).
  • Any distribution we are doing will funnel feedback and collaboration opportunities your way to improve your dataset and your internal data quality.
  • You will receive an enriched dataset, which is connected and complemented with any other available data (see the same folder names in data and fusion folders).

 How it works at the moment

Integration of data is easy with the Databus. We have been integrating and loading additional datasets alongside DBpedia for the world to query. Popular datasets are ICD10 (medical data) and organizations and persons. We are still in an initial state, but we already loaded 10 datasets (6 from DBpedia, 4 external) on the bus using these phases:

  1.  Acquisition: data is downloaded from the source and logged in.
  2. Conversion: data is converted to N-Triples and cleaned (Syntax parsing, datatype validation, and SHACL).
  3. Mapping: the vocabulary is mapped on the DBpedia Ontology and converted (We have been doing this for Wikipedia’s Infoboxes and Wikidata, but now we do it for other datasets as well).
  4. Linking: Links are mainly collected from the sources, cleaned and enriched.
  5. IDying: All entities found are given a new Databus ID for tracking.
  6.  Clustering: ID’s are merged onto clusters using one of the Databus ID’s as cluster representative.
  7. Data Comparison: Each dataset is compared with all other datasets. We have an algorithm that decides on the best value, but the main goal here is transparency, i.e. to see which data value was chosen and how it compares to the other sources.
  8. A main knowledge graph fused from all the sources, i.e. a transparent aggregate.
  9. For each source, we are producing a local fused version called the “Databus Complement”. This is a major feedback mechanism for all data providers, where they can see what data they are missing, what data differs in other sources and what links are available for their IDs.
  10. You can compare all data via a web service.

Contact us via dbpedia@infai.org if you would like to have additional datasets integrated and maintained alongside DBpedia.

From your point of view

Data Sellers

If you are selling data, the Databus provides numerous opportunities for you. You can link your offering to the open entities in the Databus. This allows consumers to discover your services better by showing it with each request.

Data Consumers

Open data on the Databus will be a commodity. We are greatly downing the cost of understanding the data, retrieving and reformatting it. We are constantly extending ways of using the data and are willing to implement any formats and APIs you need. If you are lacking a certain kind of data, we can also scout for it and load it onto the Databus.

Is it free?

Maintaining the Databus is a lot of work and servers incurring a high cost. As a rule of thumb, we are providing everything for free that we can afford to provide for free. DBpedia was providing everything for free in the past, but this is not a healthy model, as we can neither maintain quality properly nor grow.

On the Databus everything is provided “As is” without any guarantees or warranty. Improvements can be done by the volunteer community. The DBpedia Association will provide a business interface to allow guarantees, major improvements, stable maintenance, and hosting.

License

Final databases are licensed under ODC-By. This covers our work on recomposition of data. Each fact is individually licensed, e.g. Wikipedia abstracts are CC-BY-SA, some are CC-BY-NC, some are copyrighted. This means that data is available for research, informational and educational purposes. We recommend to contact us for any professional use of the data (clearing) so we can guarantee that legal matters are handled correctly. Otherwise, professional use is at own risk.

Current Statistics

The Databus data is available at http://downloads.dbpedia.org/databus/ ordered into three main folders:

  • Data: the data that is loaded on the Databus at the moment
  • Global: a folder that contains provenance data and the mappings to the new IDs
  • Fusion: the output of the Databus

Most notably you can find:

Update 21/8/2019: Data is now available via the Databus: https://databus.dbpedia.org/dbpedia/prefusion  , but without external datasets, full paper here.

  • Provenance mapping of the new ids in global/persistence-core/cluster-iri-provenance-ntriples/<http://downloads.dbpedia.org/databus/global/persistence-core/cluster-iri-provenance-ntriples/> and global/persistence-core/global-ids-ntriples/<http://downloads.dbpedia.org/databus/global/persistence-core/global-ids-ntriples/>
  • The final fused version for the core: fusion/core/fused/<http://downloads.dbpedia.org/databus/fusion/core/fused/>
  • A detailed JSON-LD file for data comparison: fusion/core/json/<http://downloads.dbpedia.org/databus/fusion/core/json/>
  • Complements, i.e. the enriched Dutch DBpedia Version: fusion/core/nl.dbpedia.org/<http://downloads.dbpedia.org/databus/fusion/core/nl.dbpedia.org/>

(Note that the file and folder structure are still subject to change)

Sources

Upcoming Developments

Data market
  • build your own data inventory and merchandise your data via Linked Data or via secure named graphs in the DBpedia SPARQL Endpoint (WebID + TLS + OpenLink’s  Virtuoso database)
DBpedia Marketplace
  • Offer your Linked Data tools, services, products
  • Incubate new research into products
  • Example: Support for RDFUnit (https://github.com/AKSW/RDFUnit created by the SHACL editor), assistance with SHACL writing and deployment of the open-source software

DBpedia and the Databus will transform Linked Data into a networked data economy

For any questions or inquiries related to the new DBpedia Databus, please contact us via dbpedia@infai.org

Yours,

DBpedia Association