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Terminologies for text-mining; an experiment in the lipoprotein metabolism domain Alexopoulou D; Wachter T; Pickersgill L; Eyre C; Schroeder MBMC Bioinformatics 2008[Apr]; 9 Suppl 4 (Suppl 4): S2BACKGROUND: 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 (ATR) 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. 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 ATR results as input and following the guidelines we described. AVAILABILITY: The TFIDF term recognition is available as Web Service, described at http://gopubmed4.biotec.tu-dresden.de/IdavollWebService/services/CandidateTermGeneratorService?wsdl.|*Database Management Systems[MESH]|*Natural Language Processing[MESH]|*Periodicals as Topic[MESH]|*Terminology as Topic[MESH]|Algorithms[MESH]|Databases, Factual[MESH]|Lipoproteins/*classification/*metabolism[MESH]|Semantics[MESH]|Software[MESH] |
