AceKG：一个面向学术数据采掘的大规模知识图谱
AceKG: A Large-scale Knowledge Graph for Academic Data Mining
摘要：
学术领域现有的知识图谱存在多关系信息不足、名称不明确、数据格式不适合大规模机器处理等问题。本文介绍了AceKG，一种新的学术领域的大型KG。AceKG不仅提供干净的学术信息，还为研究人员提供了一个大规模的基准数据集，以进行具有挑战性的数据采掘项目，包括链接预测，社区发现和学者分类。具体来说，AceKG基于一致的本体描述了31.3亿个三元组的学术事实，包括论文、作者、研究领域、地点和机构的必要属性，以及它们之间的关系。为了丰富所提出的知识图谱，我们还执行实体对齐与现有的数据库和基于规则的推理。基于AceKG，我们进行了三个典型的学术数据采掘任务的实验，并评估了几个国家的最先进的知识嵌入和网络表示学习方法的基准数据集从AceKG。最后，我们讨论了几个有前途的研究方向，受益于AceKG。
英文摘要：
Most existing knowledge graphs (KGs) in academic domains suffer from problems of insufficient multi-relational information, name ambiguity and improper data format for large-scale machine processing. In this paper, we present AceKG, a new large-scale KG in academic domain. AceKG not only provides clean academic information, but also offers a large-scale benchmark dataset for researchers to conduct challenging data mining projects including link prediction, community detection and scholar classification. Specifically, AceKG describes 3.13 billion triples of academic facts based on a consistent ontology, including necessary properties of papers, authors, fields of study, venues and institutes, as well as the relations among them. To enrich the proposed knowledge graph, we also perform entity alignment with existing databases and rule-based inference. Based on AceKG, we conduct experiments of three typical academic data mining tasks and evaluate several state-of- the-art knowledge embedding and network representation learning approaches on the benchmark datasets built from AceKG. Finally, we discuss several promising research directions that benefit from AceKG.
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