Techniques for building a knowledge graph in limited knowledge domains

Abstract

Techniques disclosed herein relate generally to constructing a customized knowledge graph. In one embodiment, entities and relations among entities are extracted from a user dataset based on certain rules to generate a seed graph. Large-scale knowledge graphs are then traversed using a finite state machine to identify candidate entities and / or relations to add to the seed graph. A priority function is used to select entities and / or relations from the candidate entities and / or relations. The selected entities and / or relations are then added to the seed graph to generate the customized knowledgegraph.

Description

[0001] Cross References to Related Applications [0002] This application claims U.S. Provisional Patent Application No. 62 / 765,005, filed August 16, 2018, entitled "Techniques For Building AKnowledge Graph In Limited Knowledge Domains," and filed August 15, 2019, entitled "TECHNIQUES FOR BUILDING A KNOWLEDGE GRAPH INLIMITED KNOWLEDGE DOMAINS", the entire disclosure of which application, including any appendices, is hereby incorporated by reference for all purposes. Background technique [0003] A knowledge graph organizes information in a structured manner by explicitly describing the relationships between entities. A knowledge graph typically represents a knowledge base using a directed graph with labeled edges (representing relationships) between nodes or vertices (representing entities). Knowledge graphs can be used, for example, in search, question answering, conversational interfaces (eg, chatbots), recommendation systems, and the like. There are some large-scale and c...

AI Technical Summary

Problems solved by technology

Creating an accurate knowledge graph for a specific knowledge domain is a challenging task, partly due to the subtlety and ambiguity of natural language and the limited knowledge in a specific domain due to the often small number of samples in user datasets

Images