2010 SupervisedIdentCMentionsAndLinkingToOntology

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Subject Headings: SDOI Algorithm, Supervised Ontology-based Concept Mention Identification, Supervised Concept Mention to Ontology Linking.

Notes

Cited By

2012

  • (Theocharopoulou & Giannakis, 2012) ⇒ Georgia Theocharopoulou, and Konstantinos Giannakis. (2013). “Web Mining to Create Semantic Content: A Case Study for the Environment.” In: Proceedings of the 8th Artificial Intelligence Applications and Innovations (AIAI 2012).

Quotes

Abstract

We propose a purely supervised learning approach to the task of identifying concept mentions within a document and of linking these mentions to their corresponding concept in a given ontology. Concept mention identification is performed with a trained CRF sequential model. Each mention is associated with a set of candidate ontology concepts, and binary training feature vectors are generated for these pairings. We formalize the feature space to expand on those those proposed in the literature, and also propose the inclusion of features derived from the training corpus. Iterative classification is proposed as a method of handling collective decisions in a supervised manner. The approach, named SCMILO, is validated against the ability to identify the concept mentions within the 139 KDD-2009 conference paper abstracts, and to link these mentions to a domain-specific ontology for the field of data mining.

1. Introduction

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References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2010 SupervisedIdentCMentionsAndLinkingToOntologyGabor Melli
Martin Ester
Supervised Identification and Linking of Concept Mentions to a Domain-Specific OntologyProceedings of the 19th ACM International Conference on Information and Knowledge Managementhttp://dl.acm.org/authorize?39985210.1145/1871437.18717122010