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RuDaS: Synthetic Datasets for Rule Learning and Evaluation Tools
arXiv - CS - Logic in Computer Science Pub Date : 2019-09-16 , DOI: arxiv-1909.07095
Cristina Cornelio; Veronika Thost

Logical rules are a popular knowledge representation language in many domains, representing background knowledge and encoding information that can be derived from given facts in a compact form. However, rule formulation is a complex process that requires deep domain expertise,and is further challenged by today's often large, heterogeneous, and incomplete knowledge graphs. Several approaches for learning rules automatically, given a set of input example facts,have been proposed over time, including, more recently, neural systems. Yet, the area is missing adequate datasets and evaluation approaches: existing datasets often resemble toy examples that neither cover the various kinds of dependencies between rules nor allow for testing scalability. We present a tool for generating different kinds of datasets and for evaluating rule learning systems, including new performance measures.
更新日期:2020-02-13

 

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