Relatedness and TBox-Driven Rule Learning in Large Knowledge Bases


  • Giuseppe Pirrò Sapienza University of Rome



We present RARL, an approach to discover rules of the form bodyhead in large knowledge bases (KBs) that typically include a set of terminological facts (TBox) and a set of TBox-compliant assertional facts (ABox). RARL's main intuition is to learn rules by leveraging TBox-information and the semantic relatedness between the predicate(s) in the atoms of the body and the predicate in the head. RARL uses an efficient relatedness-driven TBox traversal algorithm, which given an input rule head, generates the set of most semantically related candidate rule bodies. Then, rule confidence is computed in the ABox based on a set of positive and negative examples. Decoupling candidate generation and rule quality assessment offers greater flexibility than previous work.




How to Cite

Pirrò, G. (2020). Relatedness and TBox-Driven Rule Learning in Large Knowledge Bases. Proceedings of the AAAI Conference on Artificial Intelligence, 34(03), 2975-2982.



AAAI Technical Track: Knowledge Representation and Reasoning