Functional Subsumption in Feature Description Logic

Braz, Rodrigo de Salvo; Roth, Dan

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https://hdl.handle.net/2142/10802 Copy

Description

Title

Functional Subsumption in Feature Description Logic

Author(s)

• Braz, Rodrigo de Salvo
• Roth, Dan

Issue Date

2004-04

Keyword(s)

Machine Learning

Abstract

Most machine learning algorithms rely on examples represented propositionally as feature vectors. However, most data in real applications is structured and better described by sets of objects with attributes and relations between them. Typically, ad-hoc methods have been used to convert such data to feature vectors, taking, in many cases, a significant amount of the computation. Propositionalization becomes more principled if generating features from structured data is done using a formal, domain independent language that describes feature types to be extracted. This language must have limited expressivity in order to be useful while some inference procedures on it are still tractable. In this chapter we present Feature Description Logic (FDL), proposed by Cumby & Roth, where feature extraction is viewed as an inference process (subsumption). We also present an extension to FDL we call Functional FDL. FDL is ultimately based on the unification of object attributes and relations between objects in order to detect feature types in examples. Functional subsumption provides further abstraction by using unification modulo a Boolean function representing similarity between attributes and relations. This greatly improves flexibility in practical situations by accomodating variations in attributes values and relation names, incorporating background knowledge (e.g., typos, number and gender, synomyms etc). We define the semantics of Functional subsumption and how to adapt the regular subsumption algorithm for implementing it.

Type of Resource

text

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http://hdl.handle.net/2142/10802

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