Discovering Semantic Relationships for Knowledgebase

Author

Chen, Junpeng ; Liu, Juan ; Yu, Wei

Author_Institution

Sch. of Comput., Wuhan Univ., Wuhan, China

Volume

1

fYear

2008

fDate

6-8 Oct. 2008

Firstpage

242

Lastpage

247

Abstract

Discovering the semantic relationships in knowledgebase is critical in information processing and knowledge management. Previous studies of discovering semantic relationships are mainly based on the information extraction using manually annotated training set and predefined semantic relationship patterns. In this paper, we propose a new method to automatically discover the semantic relationships between two concepts in knowledgebase through text classifying and information filtering. The documents related to the two concepts in knowledgebase are at first classified into different taxonomies and the connecting terms capturing the semantic relationships between the two concepts are extracted. The experimental results show that our method has provided an efficient and effective way for the automatic discovering of semantic relationships for information management.

Keywords

classification; information filtering; text analysis; automatic semantic relationship discovery; document classification; information extraction; information filtering; information management; information processing; knowledge base; knowledge management; taxonomies; text classification; Data mining; Information filtering; Information management; Information processing; Joining processes; Knowledge management; Magnetic flux leakage; Management training; Ontologies; Taxonomy;

fLanguage

English

Publisher

ieee

Conference_Titel

Pervasive Computing and Applications, 2008. ICPCA 2008. Third International Conference on

Conference_Location

Alexandria

Print_ISBN

978-1-4244-2020-9

Electronic_ISBN

978-1-4244-2021-6

Type

conf

DOI

10.1109/ICPCA.2008.4783585

Filename

4783585