Moving from Textual Relations to Ontologized Relations.

Abstract

There has been recent research in open-ended information extraction from text that finds relational triples of the form (arg1, relation phrase, arg2), where the relation phrase is a text string that expresses a relation between two arbitrary noun phrases. While such a relational triple is a good first step, much further work is required to turn such a textual relation into a logical form that supports inferencing. The strings from arg1 and arg2 must be normalized, disambiguated, and mapped to a formal taxonomy. The relation phrase must likewise be normalized and mapped to a clearly defined logical relation. Some relation phrases can be mapped to a set of pre-defined relations such as Part-0f and Causes. We focus instead on arbitrary relation phrases that are discovered from text. For this, we need to automatically merge synonymous relations and discover meta-properties such as entailment. Ultimately, we want the coverage of a bottom-up approach together with the rich set of axioms associated with a