Towards Knowledge Acquisition from Information Extraction

A paper written by J. William Murdock and Chris Welty. It was presented at the ISWC2006.

[edit] Abstract

In our research to use information extraction to help populate the semantic web, we have encountered significant obstacles to interoperability between the technologies. We believe these obstacles to be endemic to the basic paradigms, and not quirks of the specific implementations we have worked with. In particular, we identified five dimensions of interoperability that must be addressed to successfully populate semantic web knowledge bases from information extraction systems that are suitable for reasoning. We called the task of transforming IE data into knowledge-bases knowledge integration, and briefly presented a framework called KITE in which we are exploring these dimensions. Finally, we reported on the initial results of an experiment in which the knowledge integration process used the deeper semantics of OWL ontologies to improve the precision of relation extraction from text. By adding a simplistic consistency-checking step, we showed an 8.7% relative improvement in precision over a very robust IE application without that checking.

The schedule for this talk can be found in the conference programme and a linked list of all talks is provided in the article on ISWC2006 papers. This article has originally been created from the RDF metadata for ISWC 2006.