Abstract
AbstractRelational autocompletion is the problem of automatically filling out some missing values in multi-relational data. We tackle this problem within the probabilistic logic programming framework of Distributional Clauses (DCs), which supports both discrete and continuous probability distributions. Within this framework, we introduce DiceML – an approach to learn both the structure and the parameters of DC programs from relational data (with possibly missing data). To realize this, DiceML integrates statistical modeling and DCs with rule learning. The distinguishing features of DiceML are that it (1) tackles autocompletion in relational data, (2) learns DCs extended with statistical models, (3) deals with both discrete and continuous distributions, (4) can exploit background knowledge, and (5) uses an expectation–maximization-based (EM) algorithm to cope with missing data. The empirical results show the promise of the approach, even when there is missing data.
Publisher
Cambridge University Press (CUP)
Subject
Artificial Intelligence,Computational Theory and Mathematics,Hardware and Architecture,Theoretical Computer Science,Software
Reference71 articles.
1. Induction of logic programs: FOIL and related systems
2. Estimating the Dimension of a Model
3. Speichert, S. and Belle, V. 2018. Learning probabilistic logic programs in continuous domains. arXiv preprint arXiv:1807.05527,.
4. Kersting, K. , Natarajan, S. and Poole, D. 2011. Statistical relational AI: Logic, probability and computation. In Proceedings of the 11th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR’11), 1–9.