Verify-in-the-Graph: Entity Disambiguation Enhancement for Complex Claim Verification with Interactive Graph Representation

• URL: http://arxiv.org/abs/2505.22993v1
• Date: Thu, 29 May 2025 02:02:55 GMT
• Title: Verify-in-the-Graph: Entity Disambiguation Enhancement for Complex Claim Verification with Interactive Graph Representation
• Abstract summary: VeGraph operates in three phases: (1) Graph Representation - an input claim is decomposed into structured triplets, forming a graph-based representation that integrates both structured and unstructured information; (2) Entity Disambiguation -VeGraph iteratively interacts with the knowledge base to resolve ambiguous entities within the graph for deeper sub-claim verification; and (3) Verification - remaining triplets are verified to complete the fact-checking process.
• Score: 3.864321514889099
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Claim verification is a long-standing and challenging task that demands not only high accuracy but also explainability of the verification process. This task becomes an emerging research issue in the era of large language models (LLMs) since real-world claims are often complex, featuring intricate semantic structures or obfuscated entities. Traditional approaches typically address this by decomposing claims into sub-claims and querying a knowledge base to resolve hidden or ambiguous entities. However, the absence of effective disambiguation strategies for these entities can compromise the entire verification process. To address these challenges, we propose Verify-in-the-Graph (VeGraph), a novel framework leveraging the reasoning and comprehension abilities of LLM agents. VeGraph operates in three phases: (1) Graph Representation - an input claim is decomposed into structured triplets, forming a graph-based representation that integrates both structured and unstructured information; (2) Entity Disambiguation -VeGraph iteratively interacts with the knowledge base to resolve ambiguous entities within the graph for deeper sub-claim verification; and (3) Verification - remaining triplets are verified to complete the fact-checking process. Experiments using Meta-Llama-3-70B (instruct version) show that VeGraph achieves competitive performance compared to baselines on two benchmarks HoVer and FEVEROUS, effectively addressing claim verification challenges. Our source code and data are available for further exploitation.

Related papers

• VeriGraph: Towards Verifiable Data-Analytic Agents [71.17693022916896]
  LLM-based agents have demonstrated strong capabilities in data-intensive analytical tasks, yet their outputs are rarely verifiable.<n>We propose VeriGraph, a traceable neuro-symbolic reasoning framework that enables agents to construct an explicit heterogeneous evidence directed acyclic graph.<n>Our results suggest that explicit evidence-graph construction is a promising path toward verifiable data-analytic agents.
  arXiv  Detail & Related papers  (2026-06-15T11:50:56Z)
• Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion [38.265175718191735]
  Knowledge Graph Completion aims at predicting missing triplets from incomplete knowledge graphs.<n>Recent Graph Neural Network (GNN)-based methods have achieved remarkable success by performing message passing over query-centered local subgraphs.
  arXiv  Detail & Related papers  (2026-06-04T03:07:22Z)
• GraphReAct: Reasoning and Acting for Multi-step Graph Inference [49.26607058250454]
  GraphReAct is a graph reasoning-acting framework that enables step-by-step inference over graph-structured data.<n>By interleaving reasoning with both retrieval and refinement actions, our framework enables a progressive transition from context expansion to compression.
  arXiv  Detail & Related papers  (2026-05-08T07:09:42Z)
• Agentic GraphRAG: Navigating Unstructured Financial Data with Collaborative AI [0.28647133890966986]
  We present a collaborative agentic GraphRAG framework for expert analysis of commercial registry data.<n>Our approach builds a Neo4j knowledge graph through a three-phase pipeline.<n>A human-in-the-loop dashboard exposes evidence and execution traces to support transparency and auditability.
  arXiv  Detail & Related papers  (2026-04-15T16:16:14Z)
• Can Unified Generation and Understanding Models Maintain Semantic Equivalence Across Different Output Modalities? [61.533560295383786]
  Unified Multimodal Large Language Models (U-MLLMs) integrate understanding and generation within a single architecture.<n>We observe that U-MLLMs fail to maintain semantic equivalence when required to render the same results in the image modality.<n>We introduce VGUBench, a framework to decouple reasoning logic from generation fidelity.
  arXiv  Detail & Related papers  (2026-02-27T06:23:56Z)
• GraphSearch: An Agentic Deep Searching Workflow for Graph Retrieval-Augmented Generation [35.65907480060404]
  textscGraphSearch is a novel agentic deep searching workflow with dual-channel retrieval for GraphRAG.<n>textscGraphSearch consistently improves answer accuracy and generation quality over the traditional strategy.
  arXiv  Detail & Related papers  (2025-09-26T07:45:56Z)
• Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching [61.824094419641575]
  Large Language Models (LLMs) struggle with hallucinations and factual errors in knowledge-intensive scenarios like knowledge graph question answering (KGQA)<n>We attribute this to the semantic gap between structured knowledge graphs (KGs) and unstructured queries, caused by inherent differences in their focuses and structures.<n>Existing methods usually employ resource-intensive, non-scalable reasoning on vanilla KGs, but overlook this gap.<n>We propose a flexible framework, Enrich-on-Graph (EoG), which leverages LLMs' prior knowledge to enrich KGs, bridge the semantic gap between graphs and queries.
  arXiv  Detail & Related papers  (2025-09-25T06:48:52Z)
• RAGulating Compliance: A Multi-Agent Knowledge Graph for Regulatory QA [0.0]
  Regulatory compliance question answering (QA) requires precise, verifiable information.<n>We present a novel multi-agent framework that integrates a Knowledge Graph (KG) of Regulatory triplets with Retrieval-Augmented Generation (RAG)<n>Our hybrid system outperforms conventional methods in complex regulatory queries, ensuring factual correctness with embedded triplets.
  arXiv  Detail & Related papers  (2025-08-13T15:51:05Z)
• AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries? [31.91169297907121]
  AmbiGraph-Eval is a novel benchmark of real-world ambiguous queries paired with expert-verified graph query answers.<n>Our findings reveal a critical gap in ambiguity handling and motivate future work on specialized resolution techniques.
  arXiv  Detail & Related papers  (2025-08-13T09:06:59Z)
• Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering [75.12322966980003]
  Large Language Models (LLMs) have shown strong inductive reasoning ability across various domains.<n>Most existing RAG pipelines rely on unstructured text, limiting interpretability and structured reasoning.<n>Recent studies have explored integrating knowledge graphs with LLMs for knowledge graph question answering.<n>We propose RAPL, a novel framework for efficient and effective graph retrieval in KGQA.
  arXiv  Detail & Related papers  (2025-06-11T12:03:52Z)
• Align-GRAG: Reasoning-Guided Dual Alignment for Graph Retrieval-Augmented Generation [75.9865035064794]
  Large language models (LLMs) have demonstrated remarkable capabilities, but still struggle with issues like hallucinations and outdated information.<n>Retrieval-augmented generation (RAG) addresses these issues by grounding LLM outputs in external knowledge with an Information Retrieval (IR) system.<n>We propose Align-GRAG, a novel reasoning-guided dual alignment framework in post-retrieval phrase.
  arXiv  Detail & Related papers  (2025-05-22T05:15:27Z)
• Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning [62.640169289390535]
  SPLIT-RAG is a multi-agent RAG framework that addresses the limitations with question-driven semantic graph partitioning and collaborative subgraph retrieval.<n>The innovative framework first create Semantic Partitioning of Linked Information, then use the Type-Specialized knowledge base to achieve Multi-Agent RAG.<n>The attribute-aware graph segmentation manages to divide knowledge graphs into semantically coherent subgraphs, ensuring subgraphs align with different query types.<n>A hierarchical merging module resolves inconsistencies across subgraph-derived answers through logical verifications.
  arXiv  Detail & Related papers  (2025-05-20T06:44:34Z)
• Knowledge Graph Completion with Relation-Aware Anchor Enhancement [50.50944396454757]
  We propose a relation-aware anchor enhanced knowledge graph completion method (RAA-KGC)<n>We first generate anchor entities within the relation-aware neighborhood of the head entity.<n>Then, by pulling the query embedding towards the neighborhoods of the anchors, it is tuned to be more discriminative for target entity matching.
  arXiv  Detail & Related papers  (2025-04-08T15:22:08Z)
• A Graph-based Verification Framework for Fact-Checking [25.875698681028794]
  We propose a graph-based framework, GraphFC, for fact-checking.<n>Based on this core idea, we propose a graph-based framework, GraphFC, for fact-checking.
  arXiv  Detail & Related papers  (2025-03-10T13:02:29Z)
• GraphCheck: Multi-Path Fact-Checking with Entity-Relationship Graphs [5.10832476049103]
  GraphCheck is a novel framework that converts claims into entity-relationship graphs for comprehensive verification.<n>We introduce DP-GraphCheck, a two-stage variant that improves performance by incorporating direct prompting as an initial filtering step.<n>Our two-stage framework generalizes well to other fact-checking pipelines, demonstrating its versatility.
  arXiv  Detail & Related papers  (2025-02-28T07:06:19Z)
• Unsupervised Robust Cross-Lingual Entity Alignment via Neighbor Triple Matching with Entity and Relation Texts [17.477542644785483]
  Cross-lingual entity alignment (EA) enables the integration of multiple knowledge graphs (KGs) across different languages.<n>EA pipeline that jointly performs entity-level and Relation-level Alignment by neighbor triple matching strategy.
  arXiv  Detail & Related papers  (2024-07-22T12:25:48Z)
• LaSagnA: Language-based Segmentation Assistant for Complex Queries [39.620806493454616]
  Large Language Models for Vision (vLLMs) generate detailed perceptual outcomes, including bounding boxes and masks. In this study, we acknowledge that the main cause of these problems is the insufficient complexity of training queries. We present three novel strategies to effectively handle the challenges arising from the direct integration of the proposed format.
  arXiv  Detail & Related papers  (2024-04-12T14:40:45Z)
• DocTr: Document Transformer for Structured Information Extraction in Documents [36.1145541816468]
  We present a new formulation for structured information extraction from visually rich documents. It aims to address the limitations of existing IOB tagging or graph-based formulations. We represent an entity as an anchor word and a bounding box, and represent entity linking as the association between anchor words.
  arXiv  Detail & Related papers  (2023-07-16T02:59:30Z)
• ExpressivE: A Spatio-Functional Embedding For Knowledge Graph Completion [78.8942067357231]
  ExpressivE embeds pairs of entities as points and relations as hyper-parallelograms in the virtual triple space. We show that ExpressivE is competitive with state-of-the-art KGEs and even significantly outperforms them on W18RR.
  arXiv  Detail & Related papers  (2022-06-08T23:34:39Z)
• FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations [114.94628499698096]
  We propose FactGraph, a method that decomposes the document and the summary into structured meaning representations (MRs) MRs describe core semantic concepts and their relations, aggregating the main content in both document and summary in a canonical form, and reducing data sparsity. Experiments on different benchmarks for evaluating factuality show that FactGraph outperforms previous approaches by up to 15%.
  arXiv  Detail & Related papers  (2022-04-13T16:45:33Z)
• GERE: Generative Evidence Retrieval for Fact Verification [57.78768817972026]
  We propose GERE, the first system that retrieves evidences in a generative fashion. The experimental results on the FEVER dataset show that GERE achieves significant improvements over the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-04-12T03:49:35Z)
• Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion [53.31911669146451]
  Human-curated knowledge graphs provide critical supportive information to various natural language processing tasks. These graphs are usually incomplete, urging auto-completion of them. graph embedding approaches, e.g., TransE, learn structured knowledge via representing graph elements into dense embeddings. textual encoding approaches, e.g., KG-BERT, resort to graph triple's text and triple-level contextualized representations.
  arXiv  Detail & Related papers  (2020-04-30T13:50:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.