Prof. Dr. Jens Lehmann

Prof. Dr. Jens Lehmann
Depiction of Prof. Dr. Jens Lehmann
Address Work
Römerstraße 164, Room A109, 53117 Bonn
Email Office
Phone Work
+49-341-97-32260
Fax Work

@Google Scholar Homepage

Current Projects

  • AskNowAskNow is a Question Answering (QA) system for RDF datasets.
  • AutoSPARQLConvert a natural language expression to a SPARQL query
  • BDEBig Data Europe
  • CubeQAQuestion Answering on Statistical Linked Data
  • DBpediaQuerying Wikipedia like a Semantic Database
  • DBpediaDQUser-driven quality evaluation of DBpedia
  • DBpediaDQCrowdCrowdsourcing DBpedia Quality Assessment
  • DEERRDF Data Extraction and Enrichment Framework
  • DeFactoDeep Fact Validation
  • DEQADeep Web Extraction for Question Answering
  • DL-Learnera tool for supervised Machine Learning in OWL and Description Logics
  • FTSRDF Version of the Financial Transparency System of the European Commission
  • GeoKnowMaking the Web an Exploratory for Geospatial Knowledge
  • GeoLiftSpatial mapping framework for enriching RDF datasets with Geo-spatial information
  • GOLDGenerating Ontologies from Linked Data
  • LIMESLInk discovery framework for MEtric Spaces
  • LinkedGeoDataadds a spatial dimension to the Web of Data
  • LOD2Creating Knowledge out of Interlinked Data
  • LODStatsa statement-stream-based approach for gathering comprehensive statistics about RDF datasets
  • MEX VocabularyA Light-Weight Interchange Format for Machine Learning Experiments
  • NIF4OGGDNatural Language Interchange Format for Open German Governmental Data
  • NLP2RDFConverting NLP tool output to RDF
  • OREA tool for the enrichment, repair and validation of OWL based knowledge bases.
  • QROWDThe power of the Qrowd combines with RDF
  • RDFUnitan RDF Unit-Testing suite
  • ReDD-ObservatoryUsing the Web of Data for Evaluating the Research-Disease Disparity
  • SAIM(Semi-)Automatic Instance Matcher
  • SemMap
  • SML-BenchA Benchmark for Symbolic Supervised Machine Learning from Expressive Structured Data
  • SPARQL2NLconverting SPARQL queries to natural language
  • SparqlAnalyticsI Know What You Did Last Query
  • Sparqlifya SPARQL-SQL rewriter
  • TripleCheckMateCrowdsourcing the evaluation of Linked Data
  • VeriLinksverifying links in an arbitrary linkset

Past Projects

  • LATCLOD Around-the-Clock
  • LE4SWRegional Technology Platform of Social Semantic Collaboration
  • OntoWiki.euSocial Semantic Collaboration for EKM, E-Learning & E-Tourism
  • SoftWikiSemantics- and Community-Based Requirements Engineering

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"