Two supervised learning approaches for name disambiguation in author citations
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Disambiguating Web appearances of people in a social network
WWW '05 Proceedings of the 14th international conference on World Wide Web
Unsupervised personal name disambiguation
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
IEEE Intelligent Systems
Semantic annotation, indexing, and retrieval
Web Semantics: Science, Services and Agents on the World Wide Web
Ontology-driven automatic entity disambiguation in unstructured text
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Co-occurrence and ranking of entities based on semantic annotation
International Journal of Metadata, Semantics and Ontologies
Named Entity Disambiguation: A Hybrid Statistical and Rule-Based Incremental Approach
ASWC '08 Proceedings of the 3rd Asian Semantic Web Conference on The Semantic Web
Exploring Wikipedia and text features for named entity disambiguation
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Disambiguating entity references within an ontological model
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Entity reference resolution via spreading activation on RDF-Graphs
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part I
IdentityRank: Named entity disambiguation in the news domain
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
Named entity disambiguation has been one of the main challenges to research in Information Extraction and development of Semantic Web. Therefore, it has attracted much research effort, with various methods introduced for different domains, scopes, and purposes. In this paper, we propose a new approach that is not limited to some entity classes and does not require well-structured texts. The novelty is that it exploits relations between co-occurring entities in a text as defined in a knowledge base for disambiguation. Combined with class weighting and coreference resolution, our knowledge-based method outperforms KIM system in this problem. Implemented algorithms and conducted experiments for the method are presented and discussed.