RuleHub

By Naser Ahmadi, Thi-Thuy-Duyen Truong, Lê H. Dao, Stefano Ortona, Paolo Papotti. 2020

10 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Management Science and Operations Research, Natural Language Processing

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Citations in the corpus

RuleBERT: Teaching Soft Rules to Pre-Trained Language Models (2021, methodology): “We rely on existing corpora of declarative Horn rules mined from large RDF knowledge bases (KBs) (Galárraga et al., 2015; Ortona et al., 2018; Ahmadi et al., 2020).”