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arXiv 1909.08191cs.AIcs.CLcs.DL
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基于知识图谱嵌入空间语义查询的学术数据探索

Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space

SOKENDAI (The Graduate University for Advanced Studies)（后藤大学（高级研究研究生大学））；National Institute of Informatics（信息研究院）

Hung Nghiep Tran, Atsuhiro Takasu

备注：
TPDL 2019; remove details from the appendix for official dataset publication later
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AI总结：

本文提出利用知识图谱嵌入空间的语义结构，通过定义嵌入向量间的代数语义查询，构建学术数据探索通用框架，并发现新的可解决任务。

摘要：

开放科学的趋势促成了多个开放学术数据集的出现，这些数据集包含数百万篇论文和作者。有效管理、探索和利用如此庞大且复杂的数据集具有挑战性。近年来，知识图谱已成为表示异构实体及其关系的通用数据格式。知识图谱可以通过知识图谱嵌入方法进行建模，这些方法将实体和关系表示为语义空间中的嵌入向量，然后对这些嵌入向量之间的交互进行建模。然而，知识图谱嵌入空间中的语义结构尚未得到充分研究，因此知识图谱嵌入方法通常仅用于知识图谱补全，而未被用于数据表示和分析。在本文中，我们提出基于研究较为成熟的词嵌入空间来分析这些语义结构，并利用它们来支持数据探索。我们还定义了语义查询，即知识图谱嵌入空间中嵌入向量之间的代数运算，用于解决原始数据集上实体之间的相似性和类比等查询。随后，我们设计了一个通过语义查询进行数据探索的通用框架，并讨论了针对一些传统学术数据探索任务的解决方案。我们还提出了一些新的有趣任务，这些任务可以基于嵌入空间奇特的语义结构来解决。

英文摘要：

The trends of open science have enabled several open scholarly datasets which include millions of papers and authors. Managing, exploring, and utilizing such large and complicated datasets effectively are challenging. In recent years, the knowledge graph has emerged as a universal data format for representing knowledge about heterogeneous entities and their relationships. The knowledge graph can be modeled by knowledge graph embedding methods, which represent entities and relations as embedding vectors in semantic space, then model the interactions between these embedding vectors. However, the semantic structures in the knowledge graph embedding space are not well-studied, thus knowledge graph embedding methods are usually only used for knowledge graph completion but not data representation and analysis. In this paper, we propose to analyze these semantic structures based on the well-studied word embedding space and use them to support data exploration. We also define the semantic queries, which are algebraic operations between the embedding vectors in the knowledge graph embedding space, to solve queries such as similarity and analogy between the entities on the original datasets. We then design a general framework for data exploration by semantic queries and discuss the solution to some traditional scholarly data exploration tasks. We also propose some new interesting tasks that can be solved based on the uncanny semantic structures of the embedding space.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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