Normalizing biomedical terms by minimizing ambiguity and variability

Background: One of the difficulties in mapping biomedical named entities, e.g. genes, proteins, chemicals and diseases, to their concept identifiers stems from the potential variability of the terms. Soft string matching is a possible solution to the problem, but its inherent heavy computational cost discourages its use when the dictionaries are large or when real time processing is required. A less computationally demanding approach is to normalize the terms by using heuristic rules, which enables us to look up a dictionary in a constant time regardless of its size. The development of good heuristic rules, however, requires extensive knowledge of the terminology in question and thus is the bottleneck of the normalization approach. Results: We present a novel framework for discovering a list of normalization rules from a dictionary in a fully automated manner. The rules are discovered in such a way that they minimize the ambiguity and variability of the terms in the dictionary. We eva...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Yoshimasa Tsuruoka, John McNaught, Sophia Ananiadou
• Keywords: Biomedical Named Entities, BMCBI 2008, Heuristic Rules, Second International Symposium