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Speeding up operations on feature terms using constraint programming and variable symmetry
Journal article   Peer reviewed

Speeding up operations on feature terms using constraint programming and variable symmetry

Santiago Ontañón and Pedro Meseguer
Artificial intelligence, v 220, pp 104-120
Mar 2015
DOI: https://doi.org/10.1016/j.artint.2014.11.010

Abstract

Constraint programing Feature terms Inductive logic programming Structured machine learning Symmetries
Feature terms are a generalization of first-order terms which have recently received increased attention for their usefulness in structured machine learning, natural language processing and other artificial intelligence applications. One of the main obstacles for their wide usage is that, when set-valued features are allowed, their basic operations (subsumption, unification, and antiunification) have a very high computational cost. We present a Constraint Programming formulation of these operations, which in some cases provides orders of magnitude speed-ups with respect to the standard approaches. In addition, exploiting several symmetries – that often appear in feature terms databases – causes substantial additional savings. We provide experimental results of the benefits of this approach.

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3 citations in Web of Science
3 citations in Scopus

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Domestic collaboration
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Web of Science research areas
Computer Science, Artificial Intelligence
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