SkILL - a Stochastic Inductive Logic Learner

TL;DR

SkILL is a novel stochastic inductive logic learner that effectively extracts probabilistic first-order logic theories from uncertain data, demonstrating competitive performance across synthetic, biological, and medical datasets.

Contribution

Introduces SkILL, a stochastic ILP system with an efficient search strategy, capable of handling probabilistic data and producing logic theories in various domains.

Findings

SkILL performs comparably to deterministic ILP learners.

It successfully incorporates probabilistic knowledge.

Demonstrates effectiveness in medical and biological datasets.

Abstract

Probabilistic Inductive Logic Programming (PILP) is a rel- atively unexplored area of Statistical Relational Learning which extends classic Inductive Logic Programming (ILP). This work introduces SkILL, a Stochastic Inductive Logic Learner, which takes probabilistic annotated data and produces First Order Logic theories. Data in several domains such as medicine and bioinformatics have an inherent degree of uncer- tainty, that can be used to produce models closer to reality. SkILL can not only use this type of probabilistic data to extract non-trivial knowl- edge from databases, but it also addresses efficiency issues by introducing a novel, efficient and effective search strategy to guide the search in PILP environments. The capabilities of SkILL are demonstrated in three dif- ferent datasets: (i) a synthetic toy example used to validate the system, (ii) a probabilistic adaptation of a well-known biological metabolism ap- plication, and (iii) a real world medical dataset in the breast cancer domain. Results show that SkILL can perform as well as a deterministic ILP learner, while also being able to incorporate probabilistic knowledge that would otherwise not be considered.