Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
SemTag and seeker: bootstrapping the semantic web via automated semantic annotation
WWW '03 Proceedings of the 12th international conference on World Wide Web
Two supervised learning approaches for name disambiguation in author citations
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
What's there and what's not?: focused crawling for missing documents in digital libraries
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Name disambiguation in author citations using a K-way spectral clustering method
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
IdentityRank: Named Entity Disambiguation in the Context of the NEWS Project
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
Ontology-driven automatic entity disambiguation in unstructured text
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
From manual to semi-automatic semantic annotation: about ontology-based text annotation tools
Proceedings of the COLING-2000 Workshop on Semantic Annotation and Intelligent Content
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Author name disambiguation is essential for improving performance of document indexing, retrieval, and web search. Author name disambiguation resolves the conflict when multiple authors share the same name label. This paper introduces a novel approach which exploits ontologies and ontology-based category utility for author name disambiguation. Author name disambiguation determines the correct author from various candidate authors in the populated author ontology. Candidate authors are evaluated using proposed ontology-based category utility to resolve disambiguation. Ontology-based category utility has been proposed to exploit semantic information in ontology for semantic analysis for disambiguation. The ontology-based category utility increases the number of disambiguation by about 10% compared with that of category utility, and increases the overall amount of accuracy by around 98%.