Abstract
Latent Semantic Analysis involves natural language processing techniques for analyzing relationships 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-dimensional 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.
Highlights
In the traditional approach to data gathering, we collect data on a few well-chosen variables, and manually perform various tasks, such as finding relevant information, analyzing them, making decisions, and so on [1].How to cite this paper: Kumar, A., Maskara, S. and Chiang, I.-J. (2015) Identifying Semantic in High-Dimensional Web Data Using Latent Semantic Manifold
This paper aims to explain the Latent Semantic Manifold algorithm, its deployment, and performance evaluation
The proposed Latent Semantic Manifold (LSM) algorithm is based upon the concepts of probability and topology, which identifies the latentsemantic in data
Summary
In the traditional approach to data gathering, we collect data on a few well-chosen variables, and manually perform various tasks, such as finding relevant information, analyzing them, making decisions, and so on [1].How to cite this paper: Kumar, A., Maskara, S. and Chiang, I.-J. (2015) Identifying Semantic in High-Dimensional Web Data Using Latent Semantic Manifold. In the traditional approach to data gathering, we collect data on a few well-chosen variables, and manually perform various tasks, such as finding relevant information, analyzing them, making decisions, and so on [1]. (2015) Identifying Semantic in High-Dimensional Web Data Using Latent Semantic Manifold. In this high-tech era, the high volumes of data are generated with high velocity from a variety of resources ( known as 3 V—Volume, Velocity, and Variety) [2] [3]. Gigantic repositories that include data, texts, and media have rapidly grown during recent years [5]-[9]. Several huge repositories are freely available for the public use on the World Wide Web causing another problem—the relevant information is buried in the irrelevant ones
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