TechNet: Technology semantic network based on patent data

• Published in: Expert systems with applications Vol. 142; p. 112995
• Main Authors: Sarica, Serhad, Luo, Jianxi, Wood, Kristin L.
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.03.2020; Elsevier BV
• Subjects: Algorithms; Artificial intelligence; Design engineering; Domains; Knowledge discovery; Knowledge representation; Natural language processing; Semantics; Technology semantic network; Word embedding; Knowledge discovery; Word embedding; Technology semantic network; Knowledge representation
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2019.112995

•Technology Semantic Network of >4 million terms is constructed using patent data•Mining patent texts improves the coverage of technology & engineering terms•Context-aware word embedding models allow retrieval of unique relational knowledge•Technology Semantic Network may provide an infrastructure for intelligent systems The growing developments in general semantic networks, knowledge graphs and ontology databases have motivated us to build a large-scale comprehensive semantic network of technology-related data for engineering knowledge discovery, technology search and retrieval, and artificial intelligence for engineering design and innovation. Specially, we constructed a technology semantic network (TechNet) that covers the elemental concepts in all domains of technology and their semantic associations by mining the complete U.S. patent database from 1976. To derive the TechNet, natural language processing techniques were utilized to extract terms from massive patent texts and recent word embedding algorithms were employed to vectorize such terms and establish their semantic relationships. We report and evaluate the TechNet for retrieving terms and their pairwise relevance that is meaningful from a technology and engineering design perspective. The TechNet may serve as an infrastructure to support a wide range of applications, e.g., technical text summaries, search query predictions, relational knowledge discovery, and design ideation support, in the context of engineering and technology, and complement or enrich existing semantic databases. To enable such applications, the TechNet is made public via an online interface and APIs for public users to retrieve technology-related terms and their relevancies.