Automatic Concept Extraction Based on Semantic Graphs From Big Data in Smart City - Qiu, Jing (IEEE Xplore, 2020)
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.