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New framework XoG enhances LLM question answering over incomplete knowledge graphs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework called Explore-on-Graph (XoG) designed to improve question answering over incomplete knowledge graphs. XoG addresses the issue of missing facts in knowledge graphs by learning to recover reasoning paths from the graph structure itself, rather than relying on the LLM to generate potentially hallucinated information. The framework integrates entity-relation statistics with KG embeddings to identify plausible missing entities and uses the LLM as a semantic selector. Experiments on various benchmarks demonstrate XoG's effectiveness, showing it remains competitive on complete graphs and outperforms other methods when faced with missing data, while also reducing LLM token consumption. AI

IMPACT This research could lead to more robust and efficient LLM-based question-answering systems by improving their ability to handle incomplete data.

RANK_REASON The cluster describes a new research paper detailing a novel framework for knowledge graph question answering. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework XoG enhances LLM question answering over incomplete knowledge graphs

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The cluster describes a new research paper detailing a novel framework for knowledge graph question answering. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ola El Khatib, Djellel Difallah ·

    Explore-on-Graph: Hybrid Embedding-LLM Reasoning for Knowledge Graph Question Answering under Incompleteness

    arXiv:2609.39786v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly combined with knowledge graphs (KGs) to ground reasoning in structured evidence. However, most LLM-based KGQA methods rely on traversing existing graph edges and become unreliable when r…

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