Abstract
Entity alignment (EA) aims to find equivalent entities between two Knowledge Graphs. Existing embedding-based EA methods usually encode entities as embeddings, triples as embeddings' constraint and learn to align the embeddings. The structural and side information are usually utilized via embedding propagation, aggregation or interaction. However, the details of the underlying logical inference steps among the alignment process are usually omitted, resulting in inadequate inference process. In this paper, we introduce P-NAL, an entity alignment method that captures two types of logical inference paths with Non-Axiomatic Logic (NAL). Type 1 is the bridge-like inference path between to-be-aligned entity pairs, consisting of two relation/attribute triples and a similarity sentence between the other two entities. Type 2 links the entity pair by their embeddings. P-NAL iteratively aligns entities and relations by integrating the conclusions of the inference paths. Moreover, our method is logically interpretable and extensible due to the expressiveness of NAL. Our proposed method is suitable for various EA settings. Experimental results show that our method outperforms state-of-the-art methods in terms of Hits@1, achieving 0.98+ on all three datasets of DBP15K with both supervised and unsupervised settings. To our knowledge, we present the first in-depth analysis of entity alignment's basic principles from a unified logical perspective.
Abstract (translated)
实体对齐（EA）旨在在两个知识图之间找到等价的实体。现有的基于嵌入的EA方法通常将实体编码为嵌入，关系/属性为嵌入约束，并学会对齐嵌入。通常，结构性和侧信息通过嵌入传播、聚合或交互来利用。然而，在对齐过程中，通常会忽略对逻辑推理步骤的详细说明，导致推理过程不充分。在本文中，我们介绍了P-NAL，一种名为非直观逻辑（NAL）的实体对齐方法，可以捕捉两种逻辑推理路径。类型1是一种桥式推理路径，由两个关系/属性三元组和另外两个实体之间的相似句子组成。类型2通过实体之间的嵌入将实体对链接起来。P-NAL通过整合推理路径的结论来逐步对实体和关系进行对齐。此外，由于NAL的表述力，我们的方法具有逻辑可解释性和可扩展性。我们提出的方法适用于各种知识图对齐设置。实验结果表明，我们的方法在Hits@1方面优于最先进的现有方法，在所有三个人工标注数据集的监督和无监督设置下均实现了0.98+。据我们所知，这是从统一逻辑角度对实体对齐基本原则的第一次深入分析。
URL
https://arxiv.org/abs/2404.11968
