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Taxonomic corpus-based concept summary generation for document annotation.

Nkisi-Orji, Ikechukwu; Wiratunga, Nirmalie; Hui, Kit-Ying; Heaven, Rachel; Massie, Stewart


Ikechukwu Nkisi-Orji

Kit-Ying Hui

Rachel Heaven


Jaap Kamps

Giannis Tsakonas

Yannis Manolopoulos

Lazaros Iliadis

Ioannis Karydis


Semantic annotation is an enabling technology which links documents to concepts that unambiguously describe their content. Annotation improves access to document contents for both humans and software agents. However, the annotation process is a challenging task as annotators often have to select from thousands of potentially relevant concepts from controlled vocabularies. The best approaches to assist in this task rely on reusing the annotations of an annotated corpus. In the absence of a pre-annotated corpus, alternative approaches suffer due to insufficient descriptive texts for concepts in most vocabularies. In this paper, we propose an unsupervised method for recommending document annotations based on generating node descriptors from an external corpus. We exploit knowledge of the taxonomic structure of a thesaurus to ensure that effective descriptors (concept summaries) are generated for concepts. Our evaluation on recommending annotations show that the content that we generate effectively represents the concepts. Also, our approach outperforms those which rely on information from a thesaurus alone and is comparable with supervised approaches.

Start Date Sep 18, 2017
Publication Date Sep 30, 2017
Print ISSN 0302-9743
Publisher Springer (part of Springer Nature)
Pages 49-60
Series Title Lecture notes in computer science
Series Number 10450
Series ISSN 0302-9743
ISBN 9783319670072
Institution Citation NKISI-ORJI, I., WIRATUNGA, N., HUI, K.-Y., HEAVEN, R. and MASSIE, S. 2017. Taxonomic corpus-based concept summary generation for document annotation. In Kampus, J., Tsakonas, G., Manolopoulos, Y., Iliadis, L. and Karydis, I. (eds.) Proceedings of the 21st International conference on theory and practice of digital libraries (TPDL 2017): research and advanced technology for digital libraries, 18-21 September 2017, Thessaloniki, Greece. Lecture notes in computer science, 10450. Cham: Springer [online], pages 49-60. Available from:
Keywords Taxonomy; Text annotation; Information discovery


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