RuleHub

N. Ahmadi, Thi-Thuy-Duyen Truong, Le-Hong-Mai Dao, Stefano Ortona, Paolo Papotti
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引用次数: 2

Abstract

Entity-centric knowledge graphs (KGs) are now popular to collect facts about entities. KGs have rich schemas with a large number of different types and predicates to describe the entities and their relationships. On these rich schemas, logical rules are used to represent dependencies between the data elements. While rules are useful in query answering, data curation, and other tasks, they usually do not come with the KGs. Such rules have to be manually defined or discovered with the help of rule mining methods. We believe this rule-collection task should be done collectively to better capitalize our understanding of the data and to avoid redundant work conducted on the same KGs. For this reason, we introduce RuleHub, our extensible corpus of rules for public KGs. RuleHub provides functionalities for the archival and the retrieval of rules to all users, with an extensible architecture that does not constrain the KG or the type of rules supported. We are populating the corpus with thousands of rules from the most popular KGs and report on our experiments on automatically characterizing the quality of a rule with statistical measures.
以实体为中心的知识图(KGs)现在很流行用于收集关于实体的事实。kg具有丰富的模式,其中包含大量不同的类型和谓词,用于描述实体及其关系。在这些富模式上,逻辑规则用于表示数据元素之间的依赖关系。虽然规则在查询回答、数据管理和其他任务中很有用,但它们通常不与kg一起提供,这些规则必须通过规则挖掘方法手动定义或发现。我们认为这个规则收集任务应该集体完成,以更好地利用我们对数据的理解,并避免在相同的KGs上进行冗余的工作。因此,我们引入了RuleHub,我们为公共KGs提供了可扩展的规则库。RuleHub为所有用户提供了规则的存档和检索功能,其可扩展的架构不限制KG或支持的规则类型。我们正在用来自最受欢迎的KGs的数千条规则填充语料库,并报告了我们使用统计度量自动表征规则质量的实验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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