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.

翻译：知识图谱（KGs）被广泛用于表示结构化知识，但其自动构建——尤其是利用大型语言模型（LLMs）时——常常产生不完整或含有噪声的输出。知识图谱补全（KGC）旨在推断并添加缺失的三元组，但现有方法大多要么依赖忽略语义的结构嵌入，要么依赖忽略图谱结构并需要外部资源的语言模型。本文提出OMNIA，一种为KGC桥接结构推理与语义推理的两阶段方法。该方法首先通过在知识图谱内对语义相关的实体和关系进行聚类来生成候选三元组，随后通过轻量级嵌入过滤和基于LLM的语义验证进行筛选。OMNIA完全在知识图谱内部运行，无需外部资源，并特别针对LLM生成图谱中最常见的隐含语义进行补全。在多个数据集上的大量实验表明，与传统的基于嵌入的模型相比，OMNIA显著提升了F1分数。这些结果凸显了OMNIA的有效性与高效性，其聚类和过滤阶段在保持高质量补全的同时，显著减少了搜索空间和验证成本。