TechNet: Technology semantic network based on patent data

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, we made the TechNet public via an online interface and APIs for public users to retrieve technologyrelated terms and their relevancies.

Stopwords in technical language processing

“…Via this procedure of repeating the phrase detection process with decreasing threshold values of T phrase , we identified phrases that appear more frequently in the first step using the higher threshold value, e.g., “autonomous vehicle”, and discovered phrases that are comparatively less frequent in the second step using the lower threshold value, e.g., “autonomous vehicle platooning”. In this study, we used the best performing thresholds (5, 2.5) found in a previous study [ 31 ].…”

“…This list, compared to our previous study, which identified a list of stopwords [ 31 ] (see S2 Table ) by manually reading 1,000 randomly selected sentences from the same patent text corpus, includes 26 new uninformative stopwords that the previous list did not cover. In the meantime, we also found the previous list contains other 25 stopwords, which are still deemed qualified stopwords in this study.…”

“…However, the technical language used in engineering or technical texts is different from layman languages and may use stopwords that are less prevalent in layperson languages. When it comes to engineering or technical text analysis, researchers and engineers either just adopt the readily available generic stopwords lists for removal [ 1 – 4 ], leaving many domain-related uninformative and repetitive terms in the data or identify additional stopwords in a manual, ad hoc or heuristic manner [ 7 , 31 – 33 ]. There exist no standard stopwords list for technical language processing applications.…”

Semantic Networks for Engineering Design: A Survey

“…B-Link (Shi et al, 2017) was developed using unsupervised learning by applying probability and velocity network analysis to correlate concepts retrieved from academic papers and design blogs. TechNet (Sarica et al, 2020) was derived using NLP techniques to extract terms from massive technical patent texts, as well as recent word embedding algorithms (i.e. word2vec and GloVe) to vectorise the terms and establish the semantic relations in the vector space.…”

“…These engineering design studies generally rely on common-sense knowledge bases, such as WordNet and ConceptNet, or language models not trained specifically for engineering. In fact, the engineers' perception of technical terms is biased and represented better by knowledge bases specifically trained on technological knowledge (Sarica et al, 2020). The growing uses of such public semantic network databases in the engineering design literature and methodological developments have motivated the development of the semantic networks based on engineering data.…”