  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.
