Terminologies for text-mining; an experiment in the lipoprotein metabolism domain.

@article{citeulike:2784622, 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 (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.}, address = {Biotechnology Center, Technische Universit\"{a}t Dresden, Dresden, D-01062, Germany. dimitra.alexopoulou@biotec.tu-dresden.de}, author = {Alexopoulou, D. and W\"{a}chter, T. and Pickersgill, L. and Eyre, C. and Schroeder, M. }, citeulike-article-id = {2784622}, doi = {10.1186/1471-2105-9-S4-S2}, issn = {1471-2105}, journal = {BMC bioinformatics}, posted-at = {2008-05-16 05:29:50}, priority = {2}, title = {Terminologies for text-mining; an experiment in the lipoprotein metabolism domain.}, url = {http://dx.doi.org/10.1186/1471-2105-9-S4-S2}, volume = {9 Suppl 4}, year = {2008} }

TY - JOUR ID - citeulike:2784622 L3 - citeulike-article-id:2784622 TI - Terminologies for text-mining; an experiment in the lipoprotein metabolism domain. JF - BMC bioinformatics VL - 9 Suppl 4 AD - Biotechnology Center, Technische Universität Dresden, Dresden, D-01062, Germany. dimitra.alexopoulou@biotec.tu-dresden.de SN - 1471-2105 N2 - 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 (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. AU - Alexopoulou, D AU - Wächter, T AU - Pickersgill, L AU - Eyre, C AU - Schroeder, M PY - 2008/// UR - http://dx.doi.org/10.1186/1471-2105-9-S4-S2 ER -