# Engineering Knowledge Graph from Patent Database

arXiv: https://arxiv.org/abs/2106.06739 · HTML: https://mlsanity.com/paper/2106.06739/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 76 references that are in the map.
- First posted on arXiv 12 Jun 2021.

## Builds on (most cited first, 7 of 7)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks](https://mlsanity.com/paper/1905.08865/) (2019)
- [Data-Driven Design-by-Analogy: State of the Art and Future Directions](https://mlsanity.com/paper/2106.01592/) (2021)
- [TechNet: Technology Semantic Network Based on Patent Data](https://mlsanity.com/paper/1906.00411/) (2019)
- [Semantic Networks for Engineering Design: A Survey](https://mlsanity.com/paper/2012.07060/) (2020)
- [Design Knowledge Representation with Technology Semantic Network](https://mlsanity.com/paper/2012.15518/) (2020)

## Built on by (most cited first, 18 of 18)

- [Generative Transformers for Design Concept Generation](https://mlsanity.com/paper/2211.03468/) (2022)
- [Natural Language Processing in the Patent Domain: A Survey](https://mlsanity.com/paper/2403.04105/) (2024)
- [Natural Language Processing in-and-for Design Research](https://mlsanity.com/paper/2111.13827/) (2021)
- [Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers](https://mlsanity.com/paper/2212.13196/) (2022)
- [Deep Learning for Technical Document Classification](https://mlsanity.com/paper/2106.14269/) (2021)
- [Toward Artificial Empathy for Human-Centered Design: A Framework](https://mlsanity.com/paper/2303.10583/) (2023)
- [Opportunities for Large Language Models and Discourse in Engineering Design](https://mlsanity.com/paper/2306.09169/) (2023)
- [Patent Data for Engineering Design: A Critical Review and Future Directions](https://mlsanity.com/paper/2111.08500/) (2021)
- [Retrieval Augmented Generation using Engineering Design Knowledge](https://mlsanity.com/paper/2307.06985/) (2023)
- [Material Prediction for Design Automation Using Graph Representation Learning](https://mlsanity.com/paper/2209.12793/) (2022)
- [Beyond Scaling: Predicting Patent Approval with Domain-specific Fine-grained Claim Dependency Graph](https://mlsanity.com/paper/2404.14372/) (2024)
- [The Innovation Paradox: Concept Space Expansion with Diminishing Originality and the Promise of Creative AI](https://mlsanity.com/paper/2303.13300/) (2023)
- [Building A Knowledge Graph to Enrich ChatGPT Responses in Manufacturing Service Discovery](https://mlsanity.com/paper/2404.06571/) (2024)
- [A Few-Shot Approach for Relation Extraction Domain Adaptation using Large Language Models](https://mlsanity.com/paper/2408.02377/) (2024)
- [Linguistic and Structural Basis of Engineering Design Knowledge](https://mlsanity.com/paper/2312.06355/) (2023)
- [Tripl`etoile: Extraction of Knowledge from Microblogging Text](https://mlsanity.com/paper/2408.14908/) (2024)
- [A Hybrid Ai Framework For Strategic Patent Portfolio Pruning: Integrating Learning To-Rank And Market Need Analysis For Technology Transfer Optimization](https://mlsanity.com/paper/2509.00958/) (2025)
- [From Regulatory Approvals to Patents: Cross-Domain Linking for Cardiovascular Device Traceability](https://mlsanity.com/paper/2606.28353/) (2026)

## Abstract (arXiv)

We propose a large, scalable engineering knowledge graph, comprising sets of (entity, relationship, entity) triples that are real-world engineering facts found in the patent database. We apply a set of rules based on the syntactic and lexical properties of claims in a patent document to extract facts. We aggregate these facts within each patent document and integrate the aggregated sets of facts across the patent database to obtain the engineering knowledge graph. Such a knowledge graph is expected to support inference, reasoning, and recalling in various engineering tasks. The knowledge graph has a greater size and coverage in comparison with the previously used knowledge graphs and semantic networks in the engineering literature.