Fine grained classification of named entities
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CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
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Weakly Supervised Approaches for Ontology Population
Proceedings of the 2008 conference on Ontology Learning and Population: Bridging the Gap between Text and Knowledge
Fine-grained classification of named entities exploiting latent semantic kernels
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Instance-based ontology population exploiting named-entity substitution
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Weakly-supervised acquisition of labeled class instances using graph random walks
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NEWS '10 Proceedings of the 2010 Named Entities Workshop
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COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
REACTOR: a framework for semantic relation extraction and tagging over enterprise data
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LINDEN: linking named entities with knowledge base via semantic knowledge
Proceedings of the 21st international conference on World Wide Web
APOLLO: a general framework for populating ontology with named entities via random walks on graphs
Proceedings of the 21st international conference companion on World Wide Web
LIEGE:: link entities in web lists with knowledge base
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Automatically populating ontology with named entities extracted from the unstructured text has become a key issue for Semantic Web and knowledge management techniques. This issue naturally consists of two subtasks: (1) for the entity mention whose mapping entity does not exist in the ontology, attach it to the right category in the ontology (i.e., fine-grained named entity classification), and (2) for the entity mention whose mapping entity is contained in the ontology, link it with its mapping real world entity in the ontology (i.e., entity linking). Previous studies only focus on one of the two subtasks and cannot solve this task of populating ontology with named entities integrally. This paper proposes APOLLO, a grAph-based aPproach for pOpuLating ontoLOgy with named entities. APOLLO leverages the rich semantic knowledge embedded in the Wikipedia to resolve this task via random walks on graphs. Meanwhile, APOLLO can be directly applied to either of the two subtasks with minimal revision. We have conducted a thorough experimental study to evaluate the performance of APOLLO. The experimental results show that APOLLO achieves significant accuracy improvement for the task of ontology population with named entities, and outperforms the baseline methods for both subtasks.