Learning with Feature Description Logics

Abstract

Abstract. We present a paradigm for efficient learning and inference with relational data using propositional means. The paradigm utilizes description logics and concepts graphs in the service of learning relational models using efficient propositional learning algorithms. We introduce a Feature Description Logic (FDL) -a relational (frame based) language that supports efficient inference, along with a generation function that uses inference with descriptions in the FDL to produce features suitable for use by learning algorithms. These are used within a learning framework that is shown to learn efficiently and accurately relational representations in terms of the FDL descriptions. The paradigm was designed to support learning in domains that are relational but where the amount of data and size of representation learned are very large; we exemplify it here, for clarity, on the classical ILP task of learning family relations. This paradigm provides a natural solution to the problem of learning and representing relational data; it extends and unifies several lines of works in KRR and Machine Learning in ways that provide hope for a coherent usage of learning and reasoning methods in large scale intelligent inference.

AutoSPARQL: Let Users Query Your Knowledge Base

“…It was most widely applied for learning horn clauses, but also in the Semantic Web context based on OWL and description logics [6,11,10,7,5] with predecessors in the early 90s [8]. Those approaches use various techniques like inverse resolution, inverse entailment and commonly refinement operators.…”

What Syntax Can Contribute in the Entailment Task

“…We use a DescriptionLogic inspired language, Extended Feature Description Logic (EFDL), an extension of (Cumby and Roth, 2003). As described there, expressions in the language have an equivalent representation as concept graphs, and we refer to the latter representation here for comprehensibility.…”