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Ontology learning with text mining: Two use cases in lipoprotein metabolism and toxicology Alexopoulou, Dimitra Wächter, Thomas Pickersgill, Laura Eyre, Cecilia Schroeder, Michael Automatic Term Recognition Ontology Learning Lipoprotein Metabolism 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 Schloss Dagstuhl – Leibniz-Zentrum für Informatik Dimitra Alexopoulou and Thomas Wächter and Laura Pickersgill and Cecilia Eyre and Michael Schroeder 2008 Is Part Of Dagstuhl Seminar Proceedings, Volume 8131, Ontologies and Text Mining for Life Sciences : Current Status and Future Perspectives (2008) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagSemProc.08131.12 urn:nbn:de:0030-drops-15063 https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08131.12 eng https://creativecommons.org/licenses/by/4.0/legalcode