Extracting Top-k Company Acquisition Relations From the Web

Jie Zhao, Jianfei Wang, Jia Yang and Peiquan Jin
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Jie Zhao: School of Business, Anhui University, Hefei, China
Jianfei Wang: School of Business, Anhui University, Hefei, China
Jia Yang: University of Science and Technology of China, Hefei, China
Peiquan Jin: University of Science and Technology of China, Hefei, China

International Journal on Semantic Web and Information Systems (IJSWIS), 2017, vol. 13, issue 4, 27-41

Abstract: Company acquisition relation reflects a company's development intent and competitive strategies, which is an important type of enterprise competitive intelligence. In the traditional environment, the acquisition of competitive intelligence mainly relies on newspapers, internal reports, and so on, but the rapid development of the Web introduces a new way to extract company acquisition relation. In this paper, the authors study the problem of extracting company acquisition relation from huge amounts of Web pages, and propose a novel algorithm for company acquisition relation extraction. The authors' algorithm considers the tense feature of Web content and classification technology of semantic strength when extracting company acquisition relation from Web pages. It first determines the tense of each sentence in a Web page, which is then applied in sentences classification so as to evaluate the semantic strength of the candidate sentences in describing company acquisition relation. After that, the authors rank the candidate acquisition relations and return the top-k company acquisition relation. They run experiments on 6144 pages crawled through Google, and measure the performance of their algorithm under different metrics. The experimental results show that the algorithm is effective in determining the tense of sentences as well as the company acquisition relation.

Date: 2017
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