Machine Learning
Theory Completion Using Inverse Entailment
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
PRISM: a language for symbolic-statistical modeling
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
ProbLog: a probabilistic prolog and its application in link discovery
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Machine learning in ecosystem informatics and sustainability
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Evaluating abductive hypotheses using an EM algorithm on BDDs
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Does multi-clause learning help in real-world applications?
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
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In this paper we demonstrate that machine learning (using Abductive ILP) can generate plausible and testable food webs from ecological data. In this approach, unlike previous applications of Abductive ILP, the abductive predicate ‘eats' is entirely undefined before the start of the learning. We also explore a new approach, called Hypothesis Frequency Estimation (HFE), for estimating probabilities for hypothetical ‘eats' facts based on their frequency of occurrence when randomly sampling the hypothesis space. The results of cross-validation tests suggest that the trophic networks with probabilities have higher predictive accuracies compared to the networks without probabilities. The proposed trophic networks have been examined by domain experts and comparison with the literature shows that many of the links are corroborated by the literature. In particular, links ascribed with high frequency are shown to correspond well with those having multiple references in the literature. In some cases novel high frequency links are suggested, which could be tested.