Ontology-driven Information Extraction with OntoSyphon

A paper written by Michael Cafarella and Luke McDowell. It was presented at the ISWC2006.

[edit] Abstract

The Semantic Web's need for machine understandable content has led researchers to attempt to automatically acquire such content from a number of sources, including the web. To date, such research has focused on "document-driven" systems that individually process a small to moderate size set of documents, annotating each with respect to a given ontology. This paper introduces OntoSyphon, an alternative that strives to more fully leverage existing ontological content while scaling to extract comparatively shallow content from millions of documents. OntoSyphon operates in an "ontology-driven" manner: taking any ontology as input, OntoSyphon uses the ontology to specify web searches that identify possible semantic instances, relations, and taxonomic information. Redundancy in the web, together with information from the ontology, is then used to automatically verify these candidate instances and relations, enabling OntoSyphon to operate in a fully automated, unsupervised manner. A prototype of OntoSyphon is fully implemented and we present experimental results that demonstrate substantial instance learning in a variety of domains based on independently constructed ontologies. We also introduce new techniques for improving instance verification, and demonstrate empirically that they improve upon previously known techniques.

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.