Knowledge graph entities from text
US12242808B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | May 6, 2022 |
| Grant date | Mar 4, 2025 |
| Priority date | — |
| Expiry date | Feb 17, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/022
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Example methods and systems are directed to generating knowledge graph entities from text. Natural language text is received as input and processed using named entity recognition (NER), part of speech (POS) recognition, and business object recognition (BOR). The outputs of the NER, POS, and BOR processes are combined to generate knowledge entity triples comprising two entities and a relationship between them. Keywords are extracted from the text using NER to generate a set of entities. A node in a knowledge graph is created for at least some of the entities. A POS tagger identifies verbs in the text, generating a set of verbs. Relational verbs (e.g., “talk to” or “communicated with”) are detected and used to create edges in the knowledge graph. The knowledge graph may be converted back to natural language text using a trained machine learning model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.