时态知识完成增强型自监督实体配准

IF 2.3 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Teng Fu, Gang Zhou
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引用次数: 0

摘要

时态图实体对齐的目的是在不同的时态知识图(TKG)中找到等效的实体对。主要方法主要利用时间感知和关系感知方法进行嵌入和对齐。然而,TKG 中长尾实体的存在仍然限制了对齐的准确性,因为有限的邻域信息可能会限制获得高质量嵌入的可用邻域信息,从而影响实体在表示空间中对齐的效率。此外,以往的研究大多是有监督的,对齐严重依赖种子标签,这限制了它们在资源有限的场景中的适用性。为了应对这些挑战,我们提出了时态知识补全增强型自监督实体对齐(TSEA)。我们认为,有了高质量的嵌入,就能以自我监督的方式对齐实体。为此,TSEA 由两个模块组成:一个图完成模块,用于预测长尾实体的缺失链接。通过改进的图,TSEA 进一步整合了自监督实体对齐模块,以实现无监督对齐。在广泛采用的基准上进行的实验结果表明,与最近几种基线方法相比,TSEA 的性能有所提高。额外的消减实验进一步证实了所提模块的功效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Temporal knowledge completion enhanced self-supervised entity alignment

Temporal knowledge completion enhanced self-supervised entity alignment

Temporal graph entity alignment aims at finding the equivalent entity pairs across different temporal knowledge graphs (TKGs). Primarily methods mainly utilize a time-aware and relationship-aware approach to embed and align. However, the existence of long-tail entities in TKGs still restricts the accuracy of alignment, as the limited neighborhood information may restrict the available neighborhood information for obtaining high-quality embeddings, and hence would impact the efficiency of entity alignment in representation space. Moreover, most previous researches are supervised, with heavy dependence on seed labels for alignment, restricting their applicability in scenarios with limited resources. To tackle these challenges, we propose a Temporal Knowledge Completion enhanced Self-supervised Entity Alignment (TSEA). We argue that, with high-quality embeddings, the entities would be aligned in a self-supervised manner. To this end, TSEA is constituted of two modules: A graph completion module to predict the missing links for the long-tailed entities. With the improved graph, TSEA further incorporates a self-supervised entity alignment module to achieve unsupervised alignment. Experimental results on widely adopted benchmarks demonstrate improved performance compared to several recent baseline methods. Additional ablation experiments further corroborate the efficacy of the proposed modules.

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来源期刊
Journal of Intelligent Information Systems
Journal of Intelligent Information Systems 工程技术-计算机:人工智能
CiteScore
7.20
自引率
11.80%
发文量
72
审稿时长
6-12 weeks
期刊介绍: The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems. These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to: discover knowledge from large data collections, provide cooperative support to users in complex query formulation and refinement, access, retrieve, store and manage large collections of multimedia data and knowledge, integrate information from multiple heterogeneous data and knowledge sources, and reason about information under uncertain conditions. Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces. The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.
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