TechNet: Technology Semantic Network Based on Patent Data
This provides a foundational infrastructure for engineering and technology applications, complementing existing semantic databases, though it is incremental as it applies known NLP and embedding methods to patent data.
The authors tackled the problem of building a large-scale semantic network for technology by mining the complete U.S. patent database from 1976, resulting in TechNet, a public resource that covers elemental concepts across all technology domains and their semantic associations for applications like engineering knowledge discovery and design support.
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.