Enriching WordNet to Index and Retrieve Semantic Information

  • Angioni Manuela
  • Demontis Roberto
  • Tuveri Franco

The work illustrated in this paper is part of the DART search engine. Its main goal is to index and retrieve information both in a generic and in a specific context where documents can be mapped or not on ontologies, vocabularies and thesauri. To achieve this goal, a semantic analysis process on structured and unstructured parts of documents is performed. The unstructured parts need a linguistic analysis and a semantic interpretation performed by means of Natural Language Processing (NLP) techniques, while the structured parts need a specific parser. Semantic keys are extracted from documents starting from the semantic net of WordNet and enriching it of new nodes, links and attributes.


Conceptual Mapping Natural Language Processing Distribute Hash Table Word Sense Disambiguation Open Geospatial Consortium 
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 Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.CRS4 - Center for Advanced Studies, Research and Development in SardiniaPulaItaly

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