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المؤلفQiu, Jing
المؤلفChai, Yuhan
المؤلفTian, Zhihong
المؤلفDu, Xiaojiang
المؤلفGuizani, Mohsen
تاريخ الإتاحة2020-08-18T08:34:15Z
تاريخ النشر2019
اسم المنشورIEEE Transactions on Computational Social Systems
المصدرScopus
الرقم المعياري الدولي للكتاب2329924X
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/TCSS.2019.2946181
معرّف المصادر الموحدhttp://hdl.handle.net/10576/15572
الملخصWith 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
راعي المشروعThis 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.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعBig Data
Clustering algorithms
Data mining
Detection algorithms
Frequency-domain analysis
Knowledge discovery
Semantics
Smart cities
text analysis
text mining.
العنوانAutomatic Concept Extraction Based on Semantic Graphs From Big Data in Smart City
النوعArticle


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