DocumentCode
1842803
Title
Linked data based semantic similarity and data mining
Author
Sheng, Hao ; Chen, Huajun ; Yu, Tong ; Feng, Yelei
Author_Institution
Coll. of Comput. Sci., Zhejiang Univ., Hangzhou, China
fYear
2010
fDate
4-6 Aug. 2010
Firstpage
104
Lastpage
108
Abstract
As a part of the Semantic Web, Linked data is used to connect and share related data on the Web. Compared with traditional Web documents, it has following advantages: more structural; easily understood by humans; describing the things rather than documents or pages; stronger associations. For these reasons, it is more suitable for information search and data mining. In this paper, we proposed a novel approach for semantic similarity between linked data based on lexical taxonomy and corpus statistics. Our approach has been empirically tested by the linked data of Traditional Chinese Medicine (TCM). The experimental results show a good performance in finding and recommending similar herbs in TCM.
Keywords
data mining; semantic Web; corpus statistic; data mining; data sharing; information search; lexical taxonomy; linked data based semantic; semantic web; Data mining; Databases; Diseases; Epilepsy; Frequency measurement; Semantics; Taxonomy; Data Mining; Linked Data; Semantic Similarity; Semantic Web;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Reuse and Integration (IRI), 2010 IEEE International Conference on
Conference_Location
Las Vegas, NV
Print_ISBN
978-1-4244-8097-5
Type
conf
DOI
10.1109/IRI.2010.5558957
Filename
5558957
Link To Document