Knowledge Integration and Linked Data Technologies

What kind of smart applications can we build, if we were able to integrate all available knowledge, data and language resources in a meaningful way? While Turing's imitation game is exciting, we are focussing on the actual knowledge engineering to build information machines that enable humans to perform more efficiently in their tasks. To achieve this goal, we believe the following two prerequisites must be met:

  1. Knowledge and Data must be rendered discoverable and then transformed, linked, enriched and integrated homogeneously in a huge semantic knowledge graph (DBpedia)
  2. Language Technologies must be on the one hand leveraged to understand, categorize and structure available textual content in all its forms. On the other hand, language technology must assist in building adequate interfaces that allow humans to interact effectively with data and information via discovery, querying and reorganization.

Research in this group focusses on contextualising data and ontologies as well as capturing deep linguistic knowledge to improve machine understanding. Besides research, we are taking the extra effort to undertake transfer and innovation projects and support industrial applications such as in the JURION use case.

Main contact: Dr.-Ing. Sebastian Hellmann

Research Areas

  • Data Engineering
  • Data Integration
  • Data-driven Artificial Intelligence
  • DBpedia
  • Knowledge Engineering
  • Language Technology

Projects

  • 5. Leipziger Semantic Web Tag (LSWT2013)Von Big Data zu Smart Data
  • ALIGNEDAligned, Quality-centric Software and Data Engineering
  • DBpediaQuerying Wikipedia like a Semantic Database
  • DBpediaDQUser-driven quality evaluation of DBpedia
  • DBpediaDQCrowdCrowdsourcing DBpedia Quality Assessment
  • DL-Learnera tool for supervised Machine Learning in OWL and Description Logics
  • Dockerizing Linked DataKnowledge Base Shipping to the Linked Open Data Cloud
  • FREMEOpen Framework of E-services for Multilingual and Semantic Enrichment of Digital Content
  • LIDERLinked Data as an enabler of cross-media and multilingual content analytics for enterprises across Europe
  • LOD2Creating Knowledge out of Interlinked Data
  • MEX VocabularyA Light-Weight Interchange Format for Machine Learning Experiments
  • MMoOnThe Multilingual Morpheme Ontology and Language Inventories
  • Navigation-induced Knowledge Engineering by Examplea light-weight methodology for low-cost knowledge engineering by a massive user base
  • NIF4OGGDNatural Language Interchange Format for Open German Governmental Data
  • NLP Interchange Format (NIF)an RDF/OWL-based format that allows to combine and chain several NLP tools in a flexible, light-weight way
  • NLP2RDFConverting NLP tool output to RDF
  • RDFUnitan RDF Unit-Testing suite
  • Smart Data WebCreation of an industry knowledge base for the German industry.
  • TripleCheckMateCrowdsourcing the evaluation of Linked Data

Publications

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News

SANSA 0.2 (Semantic Analytics Stack) Released ( 2017-06-13T18:18:28+02:00 by Prof. Dr. Jens Lehmann)

2017-06-13T18:18:28+02:00 by Prof. Dr. Jens Lehmann

The AKSW and Smart Data Analytics groups are happy to announce SANSA 0.2 – the second release of the Scalable Semantic Analytics Stack. Read more about "SANSA 0.2 (Semantic Analytics Stack) Released"

AKSW at ESWC 2017 ( 2017-06-12T10:53:35+02:00 Christopher Schulz)

2017-06-12T10:53:35+02:00 Christopher Schulz

Hello Community! The ESWC 2017 just ended and we give a short report of the course at the conference, especially regarding the AKSW-Group. Our members Dr. Muhammad Saleem, Dr. Mohamed Ahmed Sherif, Claus Stadler, Michael Röder, Prof. Dr. Read more about "AKSW at ESWC 2017"

Four papers accepted at WI 2017 ( 2017-06-10T15:01:31+02:00 Christopher Schulz)

2017-06-10T15:01:31+02:00 Christopher Schulz

Hello Community! We proudly announce that The International Conference on Web Intelligence (WI) accepted four papers by our group. The WI takes place in Leipzig between the 23th – 26th of August. Read more about "Four papers accepted at WI 2017"

AKSW Colloquium, 29.05.2017, Addressing open Machine Translation problems with Linked Data. ( 2017-05-26T13:51:11+02:00 by Diego Moussallem)

2017-05-26T13:51:11+02:00 by Diego Moussallem

At the AKSW Colloquium, on Monday 29th of May 2017, 3 PM, Diego Moussallem will present two papers related to his topic. First paper titled “Using BabelNet to Improve OOV Coverage in SMT” of Du et al. Read more about "AKSW Colloquium, 29.05.2017, Addressing open Machine Translation problems with Linked Data."

SML-Bench 0.2 Released ( 2017-05-11T13:01:45+02:00 by Patrick Westphal)

2017-05-11T13:01:45+02:00 by Patrick Westphal

Dear all, we are happy to announce the 0.2 release of SML-Bench, our Structured Machine Learning benchmark framework. SML-Bench provides full benchmarking scenarios for inductive supervised machine learning covering different knowledge representation languages like OWL and Prolog. Read more about "SML-Bench 0.2 Released"