Machine Learning and Ontology Engineering

The MOLE group focuses on combining Semantic Web and supervised Machine Learning technologies. The goal is to improve both quality and quantity of available knowledge by extracting, analysing, enriching and linking existing data. To make obtained results readily available for use in other applications, the group also provides several established open source tools, frameworks and demonstrators.

Research Areas

  • Creating knowledge bases from weakly structured data
  • Quality assurance and enhancement in ontologies
  • Semi-automatic instance matching
  • Supervised Machine Learning in OWL/RDF knowledge bases

Projects

  • AskNowAskNow is a Question Answering (QA) system for RDF datasets.
  • AskNowAskNow is a Question Answering (QA) system for RDF datasets.
  • ASSESSAutomatic Self Assessment
  • AutoSPARQLConvert a natural language expression to a SPARQL query
  • BDEBig Data Europe
  • BIGBig Data Public Private Forum
  • 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
  • DIESELDistributed Search in Large Enterprise Data
  • DL-Learnera tool for supervised Machine Learning in OWL and Description Logics
  • FaceteJavaScript SPARQL-based Faceted Search Library and Browsing Widgets
  • FTSRDF Version of the Financial Transparency System of the European Commission
  • GEISERVon Sensordaten zu internetbasierten Geo-Services
  • GeoKnowMaking the Web an Exploratory for Geospatial Knowledge
  • GeoLiftSpatial mapping framework for enriching RDF datasets with Geo-spatial information
  • GHOPublishing and Interlinking the Global Health Observatory Dataset
  • GOLDGenerating Ontologies from Linked Data
  • HAWKHybrid Question Answering over Linked Data
  • HOBBITHolistic Benchmarking of Big Linked Data
  • JassaJAvascript Suite for Sparql Access
  • jena-sparql-apiA Java library featuring tools for transparently boosting SPARQL query execution.
  • LATCLOD Around-the-Clock
  • LIMESLInk discovery framework for MEtric Spaces
  • LinkedGeoDataadds a spatial dimension to the Web of Data
  • LinkedIdiomsA Multilingual Linked Idioms Data Set
  • LinkedSpendinggovernment spendings from all over the world as Linked Data
  • LinkingLODinterlinking knowledge bases
  • 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.
  • QAMELQuestion Answering on Mobil Devices
  • 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
  • REXWeb-Scale Extension of RDF Knowledge Bases
  • SAIM(Semi-)Automatic Instance Matcher
  • SAKEWith RDF and Machine Learning Getting Results Faster
  • SANSA-StackOpen source platform for distributed data processing for RDF large-scale datasets
  • SemanticQurana Multilingual Resource for Natural-Language Processing
  • SemMap
  • SLIPOScalable Linking and Integration of Big POI data
  • 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
  • USPatentsPublishing and Interlinking the USPTO Patent Data
  • VeriLinksverifying links in an arbitrary linkset

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"