Identifying Semantic in High-Dimensional Web Data Using Latent Semantic Manifold

Ajit Kumar, Sanjeev Maskara, I-Jen Chiang

Research output: Contribution to journalArticlepeer-review


Latent Semantic Analysis involves natural language processing techniques for analyzing relation- ships between a set of documents and the terms they contain, by producing a set of concepts (related to the documents and terms) called semantic topics. These semantic topics assist search engine users by providing leads to the more relevant document. We develope a novel algorithm called Latent Semantic Manifold (LSM) that can identify the semantic topics in the high-dimen- sional web data. The LSM algorithm is established upon the concepts of topology and probability. Asearch tool is also developed using the LSM algorithm. This search tool is deployed for two years at two sites in Taiwan: 1) Taipei Medical University Library, Taipei, and 2) Biomedical Engineering Laboratory, Institute of Biomedical Engineering, National Taiwan University, Taipei. We evaluate the effectiveness and efficiency of the LSM algorithm by comparing with other contemporary algorithms. The results show that the LSM algorithm outperforms compared with others. This algorithm can be used to enhance the functionality of currently available search engines.
Original languageEnglish
Pages (from-to)136-152
JournalJournal of Data Analysis and Information Processing
Issue number4
Publication statusPublished - 2015


  • Latent Semantic Manifold
  • Conditional Random Field
  • Hidden Markov Model
  • Graph-Based Tree-Width Decomposition


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