DocumentCode :
2501579
Title :
A statistical approach for semantic relation extraction
Author :
Imsombut, Aurawan
Author_Institution :
Fac. of Inf. Technol., Dhurakij Pundit Univ., Bangkok, Thailand
fYear :
2009
fDate :
20-22 Oct. 2009
Firstpage :
54
Lastpage :
58
Abstract :
Semantic relations are an important component of ontologies that can support many applications e.g. text mining, question answering, and information extraction. Automatic semantic relation extraction system is a crucial tool that can reduce the bottleneck of knowledge acquisition in the ontologies construction. In this paper, we present a statistical approach for learning the semantic relations between concepts of an ontology in the agricultural domain. The semantic relations are acquired by using verbs to indicate the relations between ontology concepts. The co-occurrences of domain-verbs with their components, which are annotated the concepts, are analyzed by using several statistical methodologies. Moreover, we expand the sets of verb expressing the same semantic relation by using the extracted patterns of concept pairs of the seed verb´s component. Our experiment has been done on a collection of Thai shallow parsed texts in the domain of agriculture. The precision and recall of the presented system is 65% and 82%, respectively.
Keywords :
agriculture; knowledge acquisition; learning (artificial intelligence); ontologies (artificial intelligence); semantic Web; statistical analysis; agricultural domain; domain-verb; information extraction; knowledge acquisition; ontology; question answering; semantic relation extraction; statistical approach; text mining; Agriculture; Association rules; Clustering algorithms; Data mining; Labeling; Natural language processing; Ontologies; Proposals; Space technology; Text mining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Language Processing, 2009. SNLP '09. Eighth International Symposium on
Conference_Location :
Bangkok
Print_ISBN :
978-1-4244-4138-9
Electronic_ISBN :
978-1-4244-4139-6
Type :
conf
DOI :
10.1109/SNLP.2009.5340947
Filename :
5340947
Link To Document :
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