DocumentCode :
531603
Title :
Boosting Biomedical Entity Extraction by Using Syntactic Patterns for Semantic Relation Discovery
Author :
Volkova, Svitlana ; Caragea, Doina ; Hsu, William H. ; Drouhard, John ; Fowles, Landon
Author_Institution :
Dept. of Comput. & Inf. Sci., Kansas State Univ., Manhattan, KS, USA
Volume :
1
fYear :
2010
fDate :
Aug. 31 2010-Sept. 3 2010
Firstpage :
272
Lastpage :
278
Abstract :
Biomedical entity extraction from unstructured web documents is an important task that needs to be performed in order to discover knowledge in the veterinary medicine domain. In general, this task can be approached by applying domain specific ontologies, but a review of the literature shows that there is no universal dictionary, or ontology for this domain. To address this issue, we manually construct an ontology for extracting entities such as: animal disease names, viruses and serotypes. We then use an automated ontology expansion approach to extract semantic relationships between concepts. Such relationships include asserted synonymy, hyponymy and causality. Specifically, these relationships are extracted by using a set of syntactic patterns and part-of-speech tagging. The resulting ontology contains richer semantics compared to the manually constructed ontology. We compare our approach for extracting synonyms, hyponyms and other disease related concepts, with an approach where the ontology is expanded using GoogleSets, on the veterinary medicine entity extraction task. Experimental results show that our semantic relationship extraction approach produces a significant increase in precision and recall as compared to the GoogleSets approach.
Keywords :
diseases; document handling; information networks; information retrieval; medical computing; ontologies (artificial intelligence); veterinary medicine; Googlesets approach; asserted synonymy; automated ontology expansion; biomedical entity extraction; part of speech tagging; semantic relation discovery; syntactic pattern; universal dictionary; unstructured Web document; veterinary medicine domain; entity extraction; semantic relation discovery; text mining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on
Conference_Location :
Toronto, ON
Print_ISBN :
978-1-4244-8482-9
Electronic_ISBN :
978-0-7695-4191-4
Type :
conf
DOI :
10.1109/WI-IAT.2010.152
Filename :
5616554
Link To Document :
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