US 2026/0300772 A1Published application

Retrieval-augmented generation method and apparatus based on semantic-enhanced knowledge graph

AssigneeHangzhou Dianzi University

InventorsXiaoliang XU, Kang YANG

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Abstract

A retrieval-augmented generation method and apparatus based on a semantic-enhanced knowledge graph are provided. The method includes constructing a semantic-enhanced knowledge graph and a retrieval strategy based on the semantic-enhanced knowledge graph, where a retrieval result includes a first context or a second context. The first context is determined according to entity information and inter-entity relational information in a subgraph, and the second context is determined according to the entity information and the inter-entity relational information in the subgraph and relevant entity information. An answer is obtained according to the first context or the second context. This method enhances detail information of entities and semantic information of knowledge graphs, thereby generating a more accurate answer. Because this method extracts specific information required for a specific entity as needed, rather than extracting specified entity information during construction of the knowledge graph, the method is universal.

Claims9

Dependent claims

A retrieval-augmented generation method based on a semantic-enhanced knowledge graph, comprising:

acquiring an original text document;
constructing a semantic-enhanced knowledge graph according to entity information and inter-entity relational information extracted from the original text document, wherein the semantic-enhanced knowledge graph comprises an entity set, an inter-entity relationship set, a relevant sentence set, and a mapping relation set, wherein the relevant sentence set refers to a set of sentences relevant to entities and inter-entity relationships, and the mapping relation set refers to a set of mapping relations between sentences in the original text document and target objects, wherein the target objects are entity nodes and relationship edges in the semantic-enhanced knowledge graph;
obtaining a subgraph according to an explicit entity set and the semantic-enhanced knowledge graph;
and
determining, by using large language models (LLMs), whether entity information and inter-entity relational information in the subgraph are sufficient to answer a user question, to obtain a first determination result:
in responding to the first determination result indicating that the entity information and the inter-entity relational information in the subgraph are sufficient to answer the user question, using the entity information and the inter-entity relational information in the subgraph as a first context, and constructing a first prompt according to the first context and the user question;
and generating, based on the first prompt by using the LLMs, an answer corresponding to the user question;
and
in responding to the first determination result indicating that the entity information and the inter-entity relational information in the subgraph are not sufficient to answer the user question, extracting relevant entity information from a hybrid relevant sentence set, using the relevant entity information and the entity information and the inter-entity relational information in the subgraph as a second context, and constructing a second prompt according to the second context and the user question, wherein the hybrid relevant sentence set is a set of sentences in the subgraph relevant to hybrid entities and inter-hybrid entity relationships;
and generating, based on the second prompt by using the LLMs, an answer corresponding to the user question.

7 more elements

A retrieval-augmented generation apparatus based on a semantic-enhanced knowledge graph, comprising:

a document obtaining module, configured to obtain an original text document;
a semantic-enhanced knowledge graph construction module, configured to construct a semantic-enhanced knowledge graph according to entity information and inter-entity relational information extracted from the original text document, wherein the semantic-enhanced knowledge graph comprises an entity set, an inter-entity relationship set, a relevant sentence set, and a mapping relation set, wherein the relevant sentence set refers to a set of sentences relevant to entities and inter-entity relationships, and the mapping relation set refers to a set of mapping relations between sentences in the original text document and target objects, wherein the target objects are entity nodes and relationship edges in the semantic-enhanced knowledge graph;
a subgraph construction module, configured to obtain a subgraph according to an explicit entity set and the semantic-enhanced knowledge graph;
and
a determination module, configured to:
determine, by using large language models (LLMs), whether entity information and inter-entity relational information in the subgraph are sufficient to answer a user question, to obtain a first determination result:
in responding to the first determination result indicating that entity information and the inter-entity relational information in the subgraph are sufficient to answer the user question, use the entity information and the inter-entity relational information in the subgraph as a first context, and construct a first prompt according to the first context and the user question;
and generate, based on the first prompt by using the LLMs, an answer corresponding to the user question;
and in responding to the first determination result indicating that the entity information and the inter-entity relational information in the subgraph are not sufficient to answer the user question, extract relevant entity information from a hybrid relevant sentence set, use the relevant entity information and the entity information and the inter-entity relational information in the subgraph as a second context, and construct a second prompt according to the second context and the user question;
and generate, based on the second prompt by using the LLMs, an answer corresponding to the user question, wherein the hybrid relevant sentence set is a set of sentences in the subgraph relevant to hybrid entities and inter-hybrid entity relationships.

7 more elements

Dependencies are inferred from the claim text.