SAO Semantic Information Identification for Text Mining

• Published in: International journal of computational intelligence systems Vol. 10; no. 1; pp. 593 - 604
• Main Authors: Yang, Chao, Zhu, Donghua, Wang, Xuefeng
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.01.2017; Springer Nature B.V; Springer
• Subjects: Bibliometrics; Computational Intelligence; Data mining; Domains; Identification methods; Research Article; Scientometrics; Semantic Analysis; Subject-Action-Object; Technology Intelligence; Topic Model; Computational Intelligence; Semantic Analysis; Topic Model; Technology Intelligence; Subject-Action-Object
• ISSN: 1875-6891; 1875-6883; 1875-6883
• DOI: 10.2991/ijcis.2017.10.1.40

A Subject-Action-Object (SAO) is a triple structure which can be used to both describe topics in detail and explore the relationship between them. SAO analysis has become popular in bibliometrics, however there are two challenges in the identification of SAO structures: low relevance of SAOs to domain topics; and synonyms in SAO. These problems make the identification of SAO greatly dependent upon domain experts, limiting the further usage of SAO and influencing further the mining of SAO characteristics. This paper proposes a parse tree-based SAO identification method that includes (1) a model to identify the core components (candidate terms for subject & object) of SAO structures, where term clumping processes and co-word analysis are involved; (2) a parse tree-based hierarchical SAO extraction model to divide entire SAO structures into a collection of simpler sub-tasks for separate subject, action, and object identification; and (3) an SAO weighting model to rank SAO structures for result selection. The proposed method is applied to publications in the Journal of Scientometrics (SCIM), to identify and rank significant SAO structures. Our experiment results demonstrate the validity and feasibility of the proposed method.