Efficient Operations in Feature Terms Using Constraint Programming

Feature Terms are a generalization of first-order terms that have been introduced in theoretical computer science in order to formalize object-oriented capabilities of declarative languages, and which have been recently received increased attention for their usefulness in structured machine learning applications. The main obstacle with feature terms (as well as other formal representation languages like Horn clauses or Description Logics) is that the basic operations like subsumption have a very high computational cost. In this paper we model subsumption as constraint programming (CP), allowing us to solve those operations in a more efficient way than using traditional methods.