An Efficient Parallelized Ontology Network-Based Semantic Similarity Measure for Big Biomedical Document Clustering

Semantic mining is always a challenge for big biomedical text data. Ontology has been widely proved and used to extract semantic information. However, the process of ontology-based semantic similarity calculation is so complex that it cannot measure the similarity for big text data. To solve this pr...

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Autores principales:	Meijing Li, Tianjie Chen, Keun Ho Ryu, Cheng Hao Jin
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/81dfa9c1b4484390b03555bc8184cf6a
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id	oai:doaj.org-article:81dfa9c1b4484390b03555bc8184cf6a
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spelling	oai:doaj.org-article:81dfa9c1b4484390b03555bc8184cf6a2021-11-22T01:11:10ZAn Efficient Parallelized Ontology Network-Based Semantic Similarity Measure for Big Biomedical Document Clustering1748-671810.1155/2021/7937573https://doaj.org/article/81dfa9c1b4484390b03555bc8184cf6a2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7937573https://doaj.org/toc/1748-6718Semantic mining is always a challenge for big biomedical text data. Ontology has been widely proved and used to extract semantic information. However, the process of ontology-based semantic similarity calculation is so complex that it cannot measure the similarity for big text data. To solve this problem, we propose a parallelized semantic similarity measurement method based on Hadoop MapReduce for big text data. At first, we preprocess and extract the semantic features from documents. Then, we calculate the document semantic similarity based on ontology network structure under MapReduce framework. Finally, based on the generated semantic document similarity, document clusters are generated via clustering algorithms. To validate the effectiveness, we use two kinds of open datasets. The experimental results show that the traditional methods can hardly work for more than ten thousand biomedical documents. The proposed method keeps efficient and accurate for big dataset and is of high parallelism and scalability.Meijing LiTianjie ChenKeun Ho RyuCheng Hao JinHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7ENComputational and Mathematical Methods in Medicine, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Meijing Li Tianjie Chen Keun Ho Ryu Cheng Hao Jin An Efficient Parallelized Ontology Network-Based Semantic Similarity Measure for Big Biomedical Document Clustering
description	Semantic mining is always a challenge for big biomedical text data. Ontology has been widely proved and used to extract semantic information. However, the process of ontology-based semantic similarity calculation is so complex that it cannot measure the similarity for big text data. To solve this problem, we propose a parallelized semantic similarity measurement method based on Hadoop MapReduce for big text data. At first, we preprocess and extract the semantic features from documents. Then, we calculate the document semantic similarity based on ontology network structure under MapReduce framework. Finally, based on the generated semantic document similarity, document clusters are generated via clustering algorithms. To validate the effectiveness, we use two kinds of open datasets. The experimental results show that the traditional methods can hardly work for more than ten thousand biomedical documents. The proposed method keeps efficient and accurate for big dataset and is of high parallelism and scalability.
format	article
author	Meijing Li Tianjie Chen Keun Ho Ryu Cheng Hao Jin
author_facet	Meijing Li Tianjie Chen Keun Ho Ryu Cheng Hao Jin
author_sort	Meijing Li
title	An Efficient Parallelized Ontology Network-Based Semantic Similarity Measure for Big Biomedical Document Clustering
title_short	An Efficient Parallelized Ontology Network-Based Semantic Similarity Measure for Big Biomedical Document Clustering
title_full	An Efficient Parallelized Ontology Network-Based Semantic Similarity Measure for Big Biomedical Document Clustering
title_fullStr	An Efficient Parallelized Ontology Network-Based Semantic Similarity Measure for Big Biomedical Document Clustering
title_full_unstemmed	An Efficient Parallelized Ontology Network-Based Semantic Similarity Measure for Big Biomedical Document Clustering
title_sort	efficient parallelized ontology network-based semantic similarity measure for big biomedical document clustering
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/81dfa9c1b4484390b03555bc8184cf6a
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An Efficient Parallelized Ontology Network-Based Semantic Similarity Measure for Big Biomedical Document Clustering

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