Identifying latent semantics in high-dimensional web data

Ajit Kumar, Sanjeev Maskara, Jau Min Wong, I-Jen Chiang

研究成果: 書貢獻/報告類型會議貢獻

摘要

Search engines have become an indispensable tool for obtaining rele-vant information on the Web. The search engine often generates a large number of results, including several irrelevant items that obscure the comprehension of the generated results. Therefore, the search engines need to be enhanced to dis-cover the latent semantics in high-dimensional web data. This paper purports to explain a novel framework, including its implementation and evaluation. To discover the latent semantics in high-dimensional web data, we proposed a framework named Latent Semantic Manifold (LSM). LSM is a mixture model based on the concepts of topology and probability. The framework can find the latent semantics in web data and represent them in homogeneous groups. The framework will be evaluated by experiments. The LSM framework outper-formed compared to other frameworks. In addition, we deployed the framework to develop a tool. The tool was deployed for two years at two places - library and one biomedical engineering laboratory of Taiwan. The tool assisted the re-searchers to do semantic searches of the PubMed database. LSM framework evaluation and deployment suggest that the framework could be used to en-hance the functionalities of currently available search engines by discovering latent semantics in high-dimensional web data.
原文英語
主出版物標題CEUR Workshop Proceedings
發行者CEUR-WS
1114
出版狀態已發佈 - 2013
事件6th International Workshop on Semantic Web Applications and Tools for Life Sciences, SWAT4LS 2013 - Edinburgh, 英国
持續時間: 十二月 10 2013十二月 10 2013

其他

其他6th International Workshop on Semantic Web Applications and Tools for Life Sciences, SWAT4LS 2013
國家英国
城市Edinburgh
期間12/10/1312/10/13

ASJC Scopus subject areas

  • Computer Science(all)

指紋 深入研究「Identifying latent semantics in high-dimensional web data」主題。共同形成了獨特的指紋。

引用此