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Natural Language Interface for Goal-Oriented Knowledge Graphs Using Retrieval-Augmented Generation

A paper proposing a natural language interface for goal-oriented knowledge graphs using retrieval-augmented generation.

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Natural Language Interface for Goal-Oriented Knowledge Graphs Using Retrieval-Augmented Generation

By Kosuke Yano, Yoshinobu Kitamura, Kazuhiro Kuwabara
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The authors present a method to enable users to interact with knowledge graphs through natural language queries. They use a retrieval-augmented generation approach, which combines the strengths of both retrieval-based and generation-based methods.

This allows for more accurate and efficient querying of knowledge graphs.

Abstract

The authors present a method to enable users to interact with knowledge graphs through natural language queries. They use a retrieval-augmented generation approach, which combines the strengths of both retrieval-based and generation-based methods. This allows for more accurate and efficient querying of knowledge graphs.

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natural language interfacegoal-oriented knowledge graphsretrieval-augmented generationknowledge graph queryingKnowledge GraphsRetrieval & RAGLarge Language ModelsSemantic Interoperability
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