Scalability in Ontology Instance Matching of Large Semantic Knowledge base

被引：0

作者：

Aono, Masaki ^{[1
]}

Seddiqui, Md. Hanif ^{[2
]}

机构：

[1] Toyohashi Univ Technol, Dept Informat Sci, 1-1 Hibarigaoka,Tempaku Cho, Toyohashi, Aichi, Japan

[2] Toyohashi Univ Technol, Dept Elect & Comp Engn, Toyohashi, Aichi, Japan

来源：

PROCEEDINGS OF THE 9TH WSEAS INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, KNOWLEDGE ENGINEERING AND DATA BASES | 2010年

关键词：

Semantic Knowledge base; Ontology Instance Matching; Classification;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The rapid growth of heterogeneous sources of massive ontology instances raises a scalability issue in ontology instance matching of semantic knowledge bases. In this paper, we propose an efficient method of instance matching by considering secondary classification of monotonic large instances to achieve scalability. We use a taxonomy of the ACM's Computing Classification System (CCS) for secondary classification of large set of instances from a version of DBLP and Rexa. Then we apply our ontology instance matching to achieve the interoperability in a faster and efficient way. The experiment and evaluation depict the effectiveness and scalability of our modified algorithm for ontology instance matching.

引用

页码：378 / +

页数：2

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