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

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. Conclusion...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Dimitra Alexopoulou, Thomas Wächter, Laura Pickersgill, Cecilia Eyre, Michael Schroeder
• Keywords: BMCBI 2008, Ontology, Relevant Terms, Term Recognition