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

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
  • DBpedia NIF DatasetOpen, Large-Scale and Multilingual Knowledge Extraction Corpus
  • DBpediaDQUser-driven quality evaluation of DBpedia
  • DBpediaDQCrowdCrowdsourcing DBpedia Quality Assessment
  • DL-Learnera tool for supervised Machine Learning in OWL and Description Logics
  • 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
  • N3 - CollectionN3 - A Collection of Datasets for Named Entity Recognition and Disambiguation in the NLP Interchange Format
  • Navigation-induced Knowledge Engineering by Examplea light-weight methodology for low-cost knowledge engineering by a massive user base
  • Neural SPARQL MachinesTranslating natural language into machine language for data access.
  • 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

DBpedia Tutorial @ Knowledge Graph Conference 2021 ( 2021-04-09T13:20:50+02:00 by Julia Holze)

2021-04-09T13:20:50+02:00 by Julia Holze

On May 4, 2021 we will organize a tutorial at the Knowledge Graph Conference (KGC) 2021. Read more about "DBpedia Tutorial @ Knowledge Graph Conference 2021"

DBpedia @ Google Summer of Code program 2021 ( 2021-03-15T09:41:22+01:00 by Julia Holze)

2021-03-15T09:41:22+01:00 by Julia Holze

DBpedia, one of InfAI’s community projects, will participate in the Google Summer of Code (GSoC) program for the 10th time. The GsoC program has the goal to bring students from all over the globe into open source software development. Read more about "DBpedia @ Google Summer of Code program 2021"

DBpedia’s New Website ( 2021-01-28T12:42:40+01:00 by Julia Holze)

2021-01-28T12:42:40+01:00 by Julia Holze

We are proud to announce the completion of the new DBpedia website. Read more about "DBpedia’s New Website"

SANSA 0.7.1 (Semantic Analytics Stack) Released ( 2020-01-17T09:52:41+01:00 by Prof. Dr. Jens Lehmann)

2020-01-17T09:52:41+01:00 by Prof. Dr. Jens Lehmann

We are happy to announce SANSA 0.7.1 – the seventh release of the Scalable Semantic Analytics Stack. SANSA employs distributed computing via Apache Spark and Flink in order to allow scalable machine learning, inference and querying capabilities for large knowledge graphs. Read more about "SANSA 0.7.1 (Semantic Analytics Stack) Released"

More Complete Resultset Retrieval from Large Heterogeneous RDF Sources ( 2019-12-05T15:46:09+01:00 Andre Valdestilhas)

2019-12-05T15:46:09+01:00 Andre Valdestilhas

Over recent years, the Web of Data has grown significantly. Various interfaces such as LOD Stats, LOD Laundromat and SPARQL endpoints provide access to hundreds of thousands of RDF datasets, representing billions of facts. Read more about "More Complete Resultset Retrieval from Large Heterogeneous RDF Sources"