Learning Interpretable Entity Representation in Linked Data
Learning Interpretable Entity Representation in Linked Data
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DOI：
10.1007/978-3-319-98809-2_10
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发表时间：
2018-09
期刊：
影响因子：
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通讯作者：
Takahiro Komamizu
中科院分区：
文献类型：
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作者：
Takahiro Komamizu
关联数据已成为事实记录的宝贵来源。然而，由于其简单的记录表示（即，一组三元组），对于诸如信息检索和数据挖掘的各种应用，需要学习实体的表示。实体表示可以大致分为两类：（1）可解释的表示，和（2）潜在的表示。学习表征的可解释性对于理解两个实体之间的关系很重要，比如为什么它们相似。因此，本文重点研究前一类。现有的方法是基于确定相关字段（即，谓词和相关实体）以构成实体表示。由于物流需要劳动一些人的决定，本文的目的是消除劳动，通过应用图的接近度测量。为此，本文提出了RWRDoc，这是一种基于RWR（重启随机游走）的表示学习方法，它通过整个可达实体的最小表示的加权组合来学习实体的表示。RWR。对不同应用程序（如ad-hoc实体搜索，使用关联数据的推荐系统和实体摘要）的综合实验表明，RWRDoc学习正确的可解释实体表示。
Linked Data has become a valuable source of factual records. However, because of its simple representations of records (i.e., a set of triples), learning representations of entities is required for various applications such as information retrieval and data mining. Entity representations can be roughly classified into two categories; (1) interpretable representations, and (2) latent representations. Interpretability of learned representations is important for understanding relationship between two entities, like why they are similar. Therefore, this paper focuses on the former category. Existing methods are based on heuristics which determine relevantfields(i.e., predicates and related entities) to constitute entity representations. Since the heuristics require laboursome human decisions, this paper aims at removing the labours by applying a graph proximity measurement. To this end, this paper proposes RWRDoc, an RWR (random walk with restart)-based representation learning method which learns representations of entities by weighted combinations of minimal representations of whole reachable entities w.r.t. RWR. Comprehensive experiments on diverse applications (such as ad-hoc entity search, recommender system using Linked Data, and entity summarization) indicate that RWRDoc learns proper interpretable entity representations.