UMBC ebiquity

Online unsupervised coreference resolution for semi-structured heterogeneous data

Authors: Jennifer Sleeman

Book Title: Proceedings of the 11th International Semantic Web Conference

Date: November 30, 2012

Abstract: A pair of RDF instances are said to corefer when they are intended to denote the same thing in the world, for example, when two nodes of type foaf:Person describe the same individual. This problem is central to integrating and inter-linking semi-structured datasets. We are developing an online, unsupervised coreference resolution framework for heterogeneous, semi-structured data. The online aspect requires us to process new instances as they appear and not as a batch. The instances are heterogeneous in that they may contain terms from different ontologies whose alignments are not known in advance. Our framework encompasses a two-phased clustering algorithm that is both flexible and distributable, a probabilistic multidimensional attribute model that will support robust schema mappings, and a consolidation algorithm that will be used to perform instance consolidation in order to improve recall measures over time by addressing data spareness.

Type: InProceedings

Publisher: Springer

Pages: 457-460

Tags: coreference resolution, instance matching, heterogeneous data, unsupervised learning, semantic web, online algorithms

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