How to Predict It: Inductive Prediction by Analogy Using Taxonomic Information

Takashi lshikawa, Takao Terano

This paper presents a novel machine learning technique in a logic programming environment: Inductive Prediction by Analogy (IPA). IPA learns the description a target predicate similar to a source predicate from examples of the target predicate. Akey feature of IPAis that it uses analogies to constrain the space of hypotheses using taxonomic information represented by first-order predicate logic. Typical problems addressed by IPA are to decide whether a given ground atom is valid or not, when no concept descriptions for the goal are available in a knowledge base. This is attained by the steps: 1) recognitlon of candidate analogous source, 2) elaboration of an analogical mapping between source and target domains, 3) evaluation of mapping and inferences to given examples of the target predicate, and 4) consolidation of the outcome of the analogy. IPA can be applied to a wide variety of problems including classification problems in inductive learning. An experimental system of IPA is implemented in Prolog in order to use it as a knowledge acquisition tool for knowledge-based systems. The effectiveness of the technique is validated by a real world problem in molecular biology: the function prediction of proteins from their amino acid sequences.


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