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Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering

A novel customer service question-answering method that amalgamates RAG with a knowledge graph.

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Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering

By Zhentao Xu, Missy Meine C. Dela Cruz, Matthew Guevara, Tie Wang, M. N. DESHPANDE, Xiaofeng Wang, Zheng Li
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The paper introduces a method that constructs a knowledge graph from historical issues to improve retrieval accuracy and answering quality. It combines retrieval-augmented generation (RAG) with a knowledge graph, preserving intra-issue structure and inter-issue relations.

Empirical assessments show improved performance over baseline methods in key metrics.

Abstract

The paper introduces a method that constructs a knowledge graph from historical issues to improve retrieval accuracy and answering quality. It combines retrieval-augmented generation (RAG) with a knowledge graph, preserving intra-issue structure and inter-issue relations. Empirical assessments show improved performance over baseline methods in key metrics.

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customer service question answeringretrieval-augmented generationknowledge graph constructionintra-issue structure preservationinter-issue relation retentionKnowledge GraphsRetrieval & RAGLarge Language ModelsSemantic Interoperability
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