Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Towards Linguistically Grounded Ontologies
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
Handbook on Ontologies
Distant supervision for relation extraction without labeled data
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Semantic annotation for knowledge management: Requirements and a survey of the state of the art
Web Semantics: Science, Services and Agents on the World Wide Web
Modeling relations and their mentions without labeled text
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
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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In this paper we present the SDOIrmi text graph-based semi-supervised algorithm for the task for relation mention identification when the underlying concept mentions have already been identified and linked to an ontology. To overcome the lack of annotated data, we propose a labelling heuristic based on information extracted from the ontology. We evaluated the algorithm on the kdd09cma1 dataset using a leave-one-document-out framework and demonstrated an increase in F1 in performance over a co-occurrence based AllTrue baseline algorithm. An extrinsic evaluation of the predictions suggests a worthwhile precision on the more confidently predicted additions to the ontology.