Over the past few years, knowledge bases (KBs) like DBPedia, Freebase, and YAGO have accumulated a massive amount of knowledge from web data. Despite their seemingly large size, however, individual KBs often lack comprehensive information on any given domain. For example, over 70 percent of people on Freebase lack information on place of birth. For this reason, the complementary nature across different KBs motivates their integration through a process of aligning instances. Meanwhile, since application-level machine systems, such as medical diagnosis, have heavily relied on KBs, it is necessary to provide users with trustworthy reasons why the alignment decisions are made. To address this problem, we propose a new paradigm, explainable instance alignment (XINA), which provides user-understandable explanations for alignment decisions. Specifically, given an alignment candidate, XINA replaces existing scalar representation of an aggregated score, by decision- and explanation-vector spaces for machine decision and user understanding, respectively. To validate XINA, we perform extensive experiments on real-world KBs and show that XINA achieves comparable performance with state-of-the-arts, even with far less human effort.

中文翻译：

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XINA：使用支配关系的可解释实例对齐

在过去几年中，DBPedia、Freebase 和 YAGO 等知识库 (KB) 从网络数据中积累了大量知识。然而，尽管它们看起来很大，但单个知识库通常缺乏关于任何给定域的全面信息。例如，Freebase 上超过 70% 的人缺乏出生地信息。出于这个原因，不同知识库之间的互补性通过对齐实例的过程激发了它们的集成。同时，由于医疗诊断等应用级机器系统严重依赖知识库，因此有必要为用户提供可信赖的对齐决策理由。为了解决这个问题，我们提出了一种新的范式，可解释的实例对齐（XINA），它为对齐决策提供了用户可以理解的解释。具体来说，给定一个对齐候选者，XINA 分别用决策和解释向量空间替换聚合分数的现有标量表示，用于机器决策和用户理解。为了验证 XINA，我们对真实世界的知识库进行了大量实验，并表明 XINA 实现了与最先进技术相当的性能，即使人力少得多。