SkILL - a Stochastic Inductive Logic Learner
This work addresses the need for efficient probabilistic inductive logic programming in domains with inherent uncertainty, such as medicine and bioinformatics, representing an incremental advance in statistical relational learning.
The paper tackles the problem of learning First Order Logic theories from probabilistic annotated data in domains like medicine and bioinformatics, introducing SkILL, which addresses efficiency issues with a novel search strategy and performs as well as deterministic learners while incorporating probabilistic knowledge.
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.