An index-based algorithm for fast on-line query processing of latent semantic analysis.
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 qu...
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doaj-acaf3c46c8564c54b6904c88e4a233aa2020-11-24T21:35:36ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01125e017752310.1371/journal.pone.0177523An index-based algorithm for fast on-line query processing of latent semantic analysis.Mingxi ZhangPohan LiWei WangLatent 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.http://europepmc.org/articles/PMC5433746?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mingxi Zhang Pohan Li Wei Wang |
spellingShingle |
Mingxi Zhang Pohan Li Wei Wang An index-based algorithm for fast on-line query processing of latent semantic analysis. PLoS ONE |
author_facet |
Mingxi Zhang Pohan Li Wei Wang |
author_sort |
Mingxi Zhang |
title |
An index-based algorithm for fast on-line query processing of latent semantic analysis. |
title_short |
An index-based algorithm for fast on-line query processing of latent semantic analysis. |
title_full |
An index-based algorithm for fast on-line query processing of latent semantic analysis. |
title_fullStr |
An index-based algorithm for fast on-line query processing of latent semantic analysis. |
title_full_unstemmed |
An index-based algorithm for fast on-line query processing of latent semantic analysis. |
title_sort |
index-based algorithm for fast on-line query processing of latent semantic analysis. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2017-01-01 |
description |
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. |
url |
http://europepmc.org/articles/PMC5433746?pdf=render |
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