A Comparison of Knowledge Extraction Tools for the Semantic Web

  • Aldo Gangemi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7882)


In the last years, basic NLP tasks: NER, WSD, relation extraction, etc. have been configured for Semantic Web tasks including ontology learning, linked data population, entity resolution, NL querying to linked data, etc. Some assessment of the state of art of existing Knowledge Extraction (KE) tools when applied to the Semantic Web is then desirable. In this paper we describe a landscape analysis of several tools, either conceived specifically for KE on the Semantic Web, or adaptable to it, or even acting as aggregators of extracted data from other tools. Our aim is to assess the currently available capabilities against a rich palette of ontology design constructs, focusing specifically on the actual semantic reusability of KE output.


Natural Language Processing Word Sense Disambiguation Entity Recognition Relation Extraction Knowledge Extraction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Aldo Gangemi
    • 1
    • 2
  1. 1.LIPNUniversité Paris13-CNRS-SorbonneCitéFrance
  2. 2.STLabISTC-CNRRomeItaly

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