Functional Subsumption in Feature Description Logic Rodrigo de Salvo Braz1 and Dan Roth2 1
[email protected] 2
[email protected] Department of Computer Science University of Illinois at Urbana-Champaign 201 N. Goodwin Urbana, IL 61801-2302 Summary. Most machine learning algorithms rely on examples represented propo- sitionally 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. Propositionaliza- tion 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 or- der to detect feature types in examples. Functional subsumption provides further abstraction by using unification modulo a Boolean function representing similar- ity between attributes and relations. This greatly improves flexibility in practical situations by accomodating variations in attributes values and relation names, in- corporating 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.