TechNet: Technology Semantic Network Based on Patent Data

Serhad Sarica, Jianxi Luo, Kristin L. Wood
<span title="">2019</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="" style="color: black;">Expert systems with applications</a> </i> &nbsp;
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
more &raquo; ... ng 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1016/j.eswa.2019.112995</a> <a target="_blank" rel="external noopener" href="">fatcat:mrc6a2ujynezzd2hk2t5zs6xvi</a> </span>
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