首份系统梳理知识图谱结构与嵌入模型关系的综述
A Survey on Knowledge Graph Structure and Knowledge Graph Embeddings
- 首次全面分析嵌入模型对知识图谱结构的响应机制
- 揭示图结构是影响模型性能和偏差的关键因素
- 适合对知识图谱、链接预测感兴趣的研宄者参考
知识图谱（KG）及其机器学习对应物——知识图谱嵌入模型（KGEMs）在众多学术与应用领域中得到广泛应用。尤其在链接预测任务中，KGEMs通过已有事实推断新知识。尽管该方法在诸多实际场景中表现优异，但其对不同知识图谱结构的响应机制仍不清晰。近期研究指出，图结构可能成为重要偏差来源，并部分决定模型整体性能。本文旨在填补这一空白，据作者所知，这是首个系统梳理文献中已建立的知识图谱嵌入模型与图结构之间关系的综合性综述。期望本工作能激发更多相关研究，推动对知识图谱、嵌入模型及链接预测任务的更全面理解。
原文摘要 · Abstract (English)
Knowledge Graphs (KGs) and their machine learning counterpart, Knowledge Graph Embedding Models (KGEMs), have seen ever-increasing use in a wide variety of academic and applied settings. In particular, KGEMs are typically applied to KGs to solve the link prediction task; i.e. to predict new facts in the domain of a KG based on existing, observed facts. While this approach has been shown substantial power in many end-use cases, it remains incompletely characterised in terms of how KGEMs react differently to KG structure. This is of particular concern in light of recent studies showing that KG structure can be a significant source of bias as well as partially determinant of overall KGEM performance. This paper seeks to address this gap in the state-of-the-art. This paper provides, to the authors' knowledge, the first comprehensive survey exploring established relationships of Knowledge Graph Embedding Models and Graph structure in the literature. It is the hope of the authors that this work will inspire further studies in this area, and contribute to a more holistic understanding of KGs, KGEMs, and the link prediction task.
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