TechNet: Technology Semantic Network Based on Patent Data

TL;DR

This paper introduces TechNet, a large-scale semantic network of technology concepts derived from patent data, aimed at enhancing engineering knowledge discovery, search, and AI-driven innovation.

Contribution

The paper presents a novel large-scale semantic network of technology concepts built from patent data using NLP and word embeddings, supporting various engineering applications.

Findings

TechNet covers all technology domains with meaningful semantic associations.

It effectively retrieves relevant technology terms and their relationships.

The network is publicly accessible via an online interface and APIs.

Abstract

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.