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Ontology learning with text mining: Two use cases in lipoprotein metabolism and toxicology

Authors Dimitra Alexopoulou, Thomas Wächter, Laura Pickersgill, Cecilia Eyre, Michael Schroeder



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Dimitra Alexopoulou
Thomas Wächter
Laura Pickersgill
Cecilia Eyre
Michael Schroeder

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Dimitra Alexopoulou, Thomas Wächter, Laura Pickersgill, Cecilia Eyre, and Michael Schroeder. Ontology learning with text mining: Two use cases in lipoprotein metabolism and toxicology. In Ontologies and Text Mining for Life Sciences : Current Status and Future Perspectives. Dagstuhl Seminar Proceedings, Volume 8131, p. 1, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2008)
https://doi.org/10.4230/DagSemProc.08131.12

Abstract

Background: The engineering of ontologies, especially with a view to a text-mining use, is still a new research field. There does not yet exist a well-defined theory and technology for ontology construction. Many of the ontology design steps remain manual and are based on personal experience and intuition. However, there exist a few efforts on automatic construction of ontologies in the form of extracted lists of terms and relations between them. Results: We share experience acquired during the manual development of a lipoprotein metabolism ontology (LMO) to be used for text-mining. We compare the manually created ontology terms with the automatically derived terminology from four different automatic term recognition methods. The top 50 predicted terms contain up to 89% relevant terms. For the top 1000 terms the best method still generates 51% relevant terms. In a corpus of 3066 documents 53% of LMO terms are contained and 38% can be generated with one of the methods. Secondly we present a use case for ontology-based search for toxicological methods. Conclusions: Given high precision, automatic methods can help decrease development time and provide significant support for the identification of domain-specific vocabulary. The coverage of the domain vocabulary depends strongly on the underlying documents. Ontology development for text mining should be performed in a semi-automatic way; taking automatic term recognition results as input. Availability: The automatic term recognition method is available as web service, described at http://gopubmed4.biotec.tu- dresden.de/IdavollWebService/services/CandidateTermGeneratorService?wsdl
Keywords
  • Automatic Term Recognition
  • Ontology Learning
  • Lipoprotein Metabolism

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