Domain knowledge graph based on semantic triangle and query method

Abstract

The invention discloses a domain knowledge graph based on a semantic triangle and a query method. The graph comprises a concept layer and an entity layer; the concept layer is composed of a set of concepts, and each concept has a unique identification and is represented by a represent word item and a candidate word item set; according to field related knowledge and the concept set of the concept layer, multiple entities corresponding to each concept are instantiated, the entity layer is formed, the entities are extensions of concepts, and each entity has a unique identification and is represented by a represent word item and a candidate word item set; an association relationship established according to the field related knowledge is set between the related concepts; an association relationship established according to the field related knowledge is set between the concept layer and the entity layer; an association relationship established according to the field related knowledge is set between the entities. By means of the method, separation of the concepts and the entities is achieved, knowledge summarization is convenient, different effects of the concepts and the entities in knowledge understanding and application are distinguished, and the query efficiency is improved.

Description

technical field [0001] The invention belongs to the field of artificial intelligence, and in particular relates to a semantic triangle-based domain knowledge map and a query method. Background technique [0002] With the rapid development of Internet technology, human beings have successively experienced the "Web 1.0" era characterized by documents and the "Web 2.0" era characterized by data interconnection and data sharing, and are moving towards "Web 3.0" based on knowledge interconnection. era. Representing an understandable knowledge network in big data has become a major challenge in the modern Internet age. Similarly, the basis of human-computer interaction is to understand the user's speech information, infer the user's intention, and then return the result to the user by the machine. The knowledge graph emerged at the historic moment due to its powerful semantic processing ability and open interconnection ability. It can gather massive and heterogeneous data in the...

AI Technical Summary

Problems solved by technology

However, this representation method will cause confusion between concepts and entities, which will cause obstacles to users' understanding and is not conducive to the understanding of entities.

For specific problems, it is necessary to query in the knowledge graph with massive data and relationships, and the cost is relatively high

The traditional knowledge graph still has the problem of polysemy and polysemy, which makes the semantic network chaotic, which is not conducive to sorting out knowledge from the rich words on the Internet and performing quick queries

Images