OMNIA: Closing the Loop by Leveraging LLMs for Knowledge Graph Completion

• URL: http://arxiv.org/abs/2603.11820v1
• Date: Thu, 12 Mar 2026 11:30:41 GMT
• Title: OMNIA: Closing the Loop by Leveraging LLMs for Knowledge Graph Completion
• Abstract summary: We present OMNIA, a two-stage approach that bridges structural and semantic reasoning for Knowledge Graph Completion.<n>It first generates candidate triples by clustering semantically related entities and relations within the KG, then validates them through lightweight embedding filtering.<n>It significantly improves F1-score compared to traditional embedding-based models.
• Score: 5.1405091437027455
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge Graphs (KGs) are widely used to represent structured knowledge, yet their automatic construction, especially with Large Language Models (LLMs), often results in incomplete or noisy outputs. Knowledge Graph Completion (KGC) aims to infer and add missing triples, but most existing methods either rely on structural embeddings that overlook semantics or language models that ignore the graph's structure and depend on external sources. In this work, we present OMNIA, a two-stage approach that bridges structural and semantic reasoning for KGC. It first generates candidate triples by clustering semantically related entities and relations within the KG, then validates them through lightweight embedding filtering followed by LLM-based semantic validation. OMNIA performs on the internal KG, without external sources, and specifically targets implicit semantics that are most frequent in LLM-generated graphs. Extensive experiments on multiple datasets demonstrate that OMNIA significantly improves F1-score compared to traditional embedding-based models. These results highlight OMNIA's effectiveness and efficiency, as its clustering and filtering stages reduce both search space and validation cost while maintaining high-quality completion.

Related papers

• Detecting Hallucinations for Large Language Model-based Knowledge Graph Reasoning [48.237729567730696]
  We propose LUCID, the first halLUcination deteCtIon method for LLM-based knowleDge graph reasoning frameworks.<n> LUCID jointly leverages LLM attention scores, KG semantics, and structural information.<n> Experiments on nine datasets show that LUCID achieves state of the art performance compared to 15 baselines.
  arXiv  Detail & Related papers  (2026-04-27T12:20:28Z)
• Turning Semantics into Topology: LLM-Driven Attribute Augmentation for Collaborative Filtering [27.20519975467197]
  Topology-Augmented Graph Collaborative Filtering (TAGCF) is a novel framework that transforms semantic knowledge into topological connectivity.<n>To effectively model the heterogeneous relations in this augmented structure, we propose Adaptive Relation-weighted Graph Convolution.
  arXiv  Detail & Related papers  (2026-02-24T17:01:47Z)
• Improving LLM Reasoning with Homophily-aware Structural and Semantic Text-Attributed Graph Compression [55.51959317490934]
  Large language models (LLMs) have demonstrated promising capabilities in Text-Attributed Graph (TAG) understanding.<n>We argue that graphs inherently contain rich structural and semantic information, and that their effective exploitation can unlock potential gains in LLMs reasoning performance.<n>We propose Homophily-aware Structural and Semantic Compression for LLMs (HS2C), a framework centered on exploiting graph homophily.
  arXiv  Detail & Related papers  (2026-01-13T03:35:18Z)
• 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)
• GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models [59.72897499248909]
  We propose a novel graph retriever trained end-to-end with Large Language Models (LLMs)<n>Within the extracted subgraph, structural knowledge and semantic features are encoded via soft tokens and the verbalized graph, respectively, which are infused into the LLM together.<n>Our approach consistently achieves state-of-the-art performance, validating the strength of joint graph-LLM optimization for complex reasoning tasks.
  arXiv  Detail & Related papers  (2025-09-20T02:38:00Z)
• 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)
• KG-CF: Knowledge Graph Completion with Context Filtering under the Guidance of Large Language Models [55.39134076436266]
  KG-CF is a framework tailored for ranking-based knowledge graph completion tasks.<n> KG-CF leverages LLMs' reasoning abilities to filter out irrelevant contexts, achieving superior results on real-world datasets.
  arXiv  Detail & Related papers  (2025-01-06T01:52:15Z)
• How to Make LLMs Strong Node Classifiers? [70.14063765424012]
  Language Models (LMs) are challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs)<n>We propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the-art (SOTA) GNNs on node classification tasks.
  arXiv  Detail & Related papers  (2024-10-03T08:27:54Z)
• Exploring the Potential of Large Language Models for Heterophilic Graphs [38.79574338268997]
  We propose a two-stage framework for modeling heterophilic graphs using large language models (LLMs)<n>In the first stage, we fine-tune the LLM to better identify homophilic and heterophilic edges based on the textual content of their nodes.<n>In the second stage, we adaptively manage message propagation in GNNs for different edge types based on node features, structures, and heterophilic or homophilic characteristics.
  arXiv  Detail & Related papers  (2024-08-26T09:29:56Z)
• Bridging LLMs and KGs without Fine-Tuning: Intermediate Probing Meets Subgraph-Aware Entity Descriptions [49.36683223327633]
  Large Language Models (LLMs) encapsulate extensive world knowledge and exhibit powerful context modeling capabilities.<n>We propose a novel framework that synergizes the strengths of LLMs with robust knowledge representation to enable effective and efficient KGC.<n>We achieve a 47% relative improvement over previous methods based on non-fine-tuned LLMs and, to our knowledge, are the first to achieve classification performance comparable to fine-tuned LLMs.
  arXiv  Detail & Related papers  (2024-08-13T10:15:55Z)

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.