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
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;
Conference_Titel :
Information Reuse and Integration (IRI), 2010 IEEE International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-1-4244-8097-5
DOI :
10.1109/IRI.2010.5558957
