Intelligent Extraction of a Knowledge Ontology From Global Patents: The Case of Smart Retailing Technology Mining

Intelligent Extraction of a Knowledge Ontology From Global Patents: The Case of Smart Retailing Technology Mining

Amy J. C. Trappey (National Tsing Hua University, Taiwan), Charles V. Trappey (National Chiao Tung University, Taiwan) and Ai-Che Chang (Shih Hsin University, Taiwan)
Copyright: © 2020 |Pages: 20
DOI: 10.4018/IJSWIS.2020100104
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Abstract

The growth of global patents increased over the last decade as enterprises and inventors sought greater protection of their intellectual property (IP) rights. Global patents represent state-of-the-art knowledge for given domains. This research develops a hierarchical Latent Dirichlet Allocation (LDA)-based approach as a computational intelligent method to discover topics and form a top-down ontology, a semantic schema, representing the collective patent knowledge. To validate the knowledge extraction, 1,546 smart retailing patents collected from the Derwent Innovation platform from 2011 and 2016 are used to build the domain ontology schema. The patent set focuses on in-use, globally established, and non-disputed IP covering payment, user experience, and information integration for smart retailing. The clustering and LDA-based ontology system automatically build the knowledge map, which identifies the technology trends and the technology gaps enabling the development of competitive R&D and management strategies.
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2. Literature Review

The literature related patent and technology mining using clustering, topic modeling, and ontologies, are the focus of the review and discussion. Other methodologies in the literature are not covered to keep the review in focus.

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