Automatic Concept Extraction Based on Semantic Graphs From Big Data in Smart City
Author | Qiu, Jing |
Author | Chai, Yuhan |
Author | Tian, Zhihong |
Author | Du, Xiaojiang |
Author | Guizani, Mohsen |
Available date | 2020-08-18T08:34:15Z |
Publication Date | 2019 |
Publication Name | IEEE Transactions on Computational Social Systems |
Resource | Scopus |
ISSN | 2329924X |
Abstract | With the rapid development of smart cities, various types of sensors can rapidly collect a large amount of data, and it becomes increasingly important to discover effective knowledge and process information from massive amounts of data. Currently, in the field of knowledge engineering, knowledge graphs, especially domain knowledge graphs, play important roles and become the infrastructure of Internet knowledge-driven intelligent applications. Domain concept extraction is critical to the construction of domain knowledge graphs. Although there have been some works that have extracted concepts, semantic information has not been fully used. However, the excellent concept extraction results can be obtained by making full use of semantic information. In this article, a novel concept extraction method, Semantic Graph-Based Concept Extraction (SGCCE), is proposed. First, the similarities between terms are calculated using the word co-occurrence, the LDA topic model and Word2Vec. Then, a semantic graph of terms is constructed based on the similarities between the terms. Finally, according to the semantic graph of the terms, community detection algorithms are used to divide the terms into different communities where each community acts as a concept. In the experiments, we compare the concept extraction results that are obtained by different community detection algorithms to analyze the different semantic graphs. The experimental results show the effectiveness of our proposed method. This method can effectively use semantic information, and the results of the concept extraction are better from domain big data in smart cities. IEEE |
Sponsor | This work was supported in part by the National Key Research and Development Plan under Grant 2018YFB0803504 and Grant 2018YEB1004003, in part by the Guangdong Province Key Research and Development Plan under Grant 2019B010137004, in part by the National Natural Science Foundation of China under Grant 61871140, Grant 61872100, Grant 61572153, and Grant U1636215, and in part by the Peng Cheng Laboratory Project of Guangdong Province under Grant PCL2018KP004. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | Big Data Clustering algorithms Data mining Detection algorithms Frequency-domain analysis Knowledge discovery Semantics Smart cities text analysis text mining. |
Type | Article |
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