Enhancing Patent Retrieval using Text and Knowledge Graph Embeddings: A Technical Note

TL;DR

This paper introduces a novel patent retrieval method that combines text and knowledge graph embeddings to improve the relevance and ranking of patents, leveraging Sentence-BERT and TransE for multi-faceted representations.

Contribution

The paper proposes a new patent embedding approach that synthesizes text, citation, and inventor information using advanced NLP and knowledge graph techniques, enhancing patent retrieval accuracy.

Findings

Concatenated embeddings improve patent relevance representation.

Mean cosine similarity effectively ranks related patents.

Method applied successfully to product family and inventor portfolios.

Abstract

Patent retrieval influences several applications within engineering design research, education, and practice as well as applications that concern innovation, intellectual property, and knowledge management etc. In this article, we propose a method to retrieve patents relevant to an initial set of patents, by synthesizing state-of-the-art techniques among natural language processing and knowledge graph embedding. Our method involves a patent embedding that captures text, citation, and inventor information, which individually represent different facets of knowledge communicated through a patent document. We obtain text embeddings using Sentence-BERT applied to titles and abstracts. We obtain citation and inventor embeddings through TransE that is trained using the corresponding knowledge graphs. We identify using a classification task that the concatenation of text, citation, and inventor embeddings offers a plausible representation of a patent. While the proposed patent embedding could be used to associate a pair of patents, we observe using a recall task that multiple initial patents could be associated with a target patent using mean cosine similarity, which could then be utilized to rank all target patents and retrieve the most relevant ones. We apply the proposed patent retrieval method to a set of patents corresponding to a product family and an inventor's portfolio.