# TechNet: Technology Semantic Network Based on Patent Data

arXiv: https://arxiv.org/abs/1906.00411 · HTML: https://mlsanity.com/paper/1906.00411/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2019.

## Built on by (most cited first, 14 of 14)

- [PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT](https://mlsanity.com/paper/2103.11933/) (2021)
- [Natural Language Processing in the Patent Domain: A Survey](https://mlsanity.com/paper/2403.04105/) (2024)
- [Engineering Knowledge Graph from Patent Database](https://mlsanity.com/paper/2106.06739/) (2021)
- [Natural Language Processing in-and-for Design Research](https://mlsanity.com/paper/2111.13827/) (2021)
- [Deep Learning for Technical Document Classification](https://mlsanity.com/paper/2106.14269/) (2021)
- [SkillNER: Mining and Mapping Soft Skills from any Text](https://mlsanity.com/paper/2101.11431/) (2021)
- [Opportunities for Large Language Models and Discourse in Engineering Design](https://mlsanity.com/paper/2306.09169/) (2023)
- [Generative Design Ideation: A Natural Language Generation Approach](https://mlsanity.com/paper/2204.09658/) (2022)
- [Material Prediction for Design Automation Using Graph Representation Learning](https://mlsanity.com/paper/2209.12793/) (2022)
- [The Innovation Paradox: Concept Space Expansion with Diminishing Originality and the Promise of Creative AI](https://mlsanity.com/paper/2303.13300/) (2023)
- [PatentMiner: Patent Vacancy Mining via Context-enhanced and Knowledge-guided Graph Attention](https://mlsanity.com/paper/2107.04880/) (2021)
- [WikiLink: an encyclopedia-based semantic network for design innovation](https://mlsanity.com/paper/2208.14349/) (2022)
- [Using a Large Language Model to generate a Design Structure Matrix](https://mlsanity.com/paper/2312.04134/) (2023)
- [Stop Words for Processing Software Engineering Documents: Do they Matter?](https://mlsanity.com/paper/2303.10439/) (2023)

## Abstract (arXiv)

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.