提出三元组全要素匹配方法，解决异构知识图谱融合难题
Full Triple Matcher: Integrating all triple elements between heterogeneous Knowledge Graphs
- 通过标签与三元组双重匹配，融合实体、谓词和上下文信息
- 在OAEI竞赛中表现优于主流系统，跨场景准确率高
- 新增数据集专门评估三元组匹配效果，填补研究空白
知识图谱（KG）是表示和推理结构化信息的强大工具，其核心组成包括模式、身份和上下文。尽管模式和身份匹配在本体与实体匹配研究中已较为成熟，但上下文匹配仍基本未被探索。这尤其关键，因为现实世界中的知识图谱在来源、规模和信息密度上差异显著——而这些因素通常未体现在当前实体匹配方法所依赖的数据集上。因此，现有方法在需要整合多样复杂上下文的场景中可能表现不足。为弥补这一差距，我们提出一种新的知识图谱集成方法，包含标签匹配与三元组匹配两部分。采用字符串处理、模糊匹配和向量相似性技术对实体和谓词标签进行对齐；随后识别传递相似信息的三元组映射，并利用这些映射提升实体匹配精度。该方法在OAEI竞赛中表现具有竞争力，且优于监督式方法，在多种测试用例中均实现高准确率。此外，我们基于基准数据集构建了一个新数据集，以更全面地评估三元组匹配步骤。
原文摘要 · Abstract (English)
Knowledge graphs (KGs) are powerful tools for representing and reasoning over structured information. Their main components include schema, identity, and context. While schema and identity matching are well-established in ontology and entity matching research, context matching remains largely unexplored. This is particularly important because real-world KGs often vary significantly in source, size, and information density - factors not typically represented in the datasets on which current entity matching methods are evaluated. As a result, existing approaches may fall short in scenarios where diverse and complex contexts need to be integrated. To address this gap, we propose a novel KG integration method consisting of label matching and triple matching. We use string manipulation, fuzzy matching, and vector similarity techniques to align entity and predicate labels. Next, we identify mappings between triples that convey comparable information, using these mappings to improve entity-matching accuracy. Our approach demonstrates competitive performance compared to leading systems in the OAEI competition and against supervised methods, achieving high accuracy across diverse test cases. Additionally, we introduce a new dataset derived from the benchmark dataset to evaluate the triple-matching step more comprehensively.
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