Abstract
Latent Semantic Analysis (LSA) is widely used for finding the documents whose semantic is similar to the query of keywords. Although LSA yield promising similar results, the existing LSA algorithms involve lots of unnecessary operations in similarity computation and candidate check during on-line query processing, which is expensive in terms of time cost and cannot efficiently response the query request especially when the dataset becomes large. In this paper, we study the efficiency problem of on-line query processing for LSA towards efficiently searching the similar documents to a given query. We rewrite the similarity equation of LSA combined with an intermediate value called partial similarity that is stored in a designed index called partial index. For reducing the searching space, we give an approximate form of similarity equation, and then develop an efficient algorithm for building partial index, which skips the partial similarities lower than a given threshold θ. Based on partial index, we develop an efficient algorithm called ILSA for supporting fast on-line query processing. The given query is transformed into a pseudo document vector, and the similarities between query and candidate documents are computed by accumulating the partial similarities obtained from the index nodes corresponds to non-zero entries in the pseudo document vector. Compared to the LSA algorithm, ILSA reduces the time cost of on-line query processing by pruning the candidate documents that are not promising and skipping the operations that make little contribution to similarity scores. Extensive experiments through comparison with LSA have been done, which demonstrate the efficiency and effectiveness of our proposed algorithm.
Highlights
Many real data sets could be grouped as documents, including as web pages, literature and product profiles
During the on-line query processing, the input terms are firstly transform into a query vector of pseudo document, and Latent Semantic Analysis (LSA) uses cosine coefficient to compute the similarity between query vector and the low-dimension vector corresponds to each document over the decomposition result of matrix C
This paper introduced an index-based query processing algorithm Index-based LSA (ILSA) for efficiently finding similar documents in large document datasets
Summary
Many real data sets could be grouped as documents, including as web pages, literature and product profiles. With such data sets becoming massive and diverse, there is a need for designing algorithmic tools and developing applications to discover the underlying relationship from the data. Consider an example of the document search in a dataset, even though a document is on precisely the same topic to a input query of keywords, it may not be searched when its contained terms are different to the input keywords.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
