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When working with a corpus of documents (e.g. in the context of a digital library), various access services are offered such as browsing, keyword search, and faceted search. Recently, Retrieval Augmented Generation (RAG) approaches have been proposed that can leverage LLMs to offer Question Answering (QA) services while addressing the hallucination problem of LLMs. In this direction, this paper investigates an approach for offering QA over document corpora and related Knowledge Graphs that exploits LLMs, RAG, and RAG enhanced with information from Knowledge Graphs. To address the challenge of black-box interaction, we present an interactive system called SemanticRAG. which enables users to ask questions, view the answer generated by each method, and obtain the provenance of each answer. We showcase the feasibility and value of this approach by deploying it over a corpus of scientific papers collected by the FAO UN for ecosystem restoration. Online Demo: https://demos.isl.ics.forth.gr/SemanticRAG/ .

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Interactive and Provenance-Aware Search and QA over Documents Using LLMs, RAG and Knowledge Graph Verbalization

  • 会议论文

  • 发表日期：

    2026年

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  • Iordanis Sapidis,
  • Valantis Zervos,
  • Michalis Mountantonakis,
  • Yannis Tzitzikas

摘要

When working with a corpus of documents (e.g. in the context of a digital library), various access services are offered such as browsing, keyword search, and faceted search. Recently, Retrieval Augmented Generation (RAG) approaches have been proposed that can leverage LLMs to offer Question Answering (QA) services while addressing the hallucination problem of LLMs. In this direction, this paper investigates an approach for offering QA over document corpora and related Knowledge Graphs that exploits LLMs, RAG, and RAG enhanced with information from Knowledge Graphs. To address the challenge of black-box interaction, we present an interactive system called SemanticRAG. which enables users to ask questions, view the answer generated by each method, and obtain the provenance of each answer. We showcase the feasibility and value of this approach by deploying it over a corpus of scientific papers collected by the FAO UN for ecosystem restoration. Online Demo: https://demos.isl.ics.forth.gr/SemanticRAG/ .