Taxonomic corpus-based concept summary generation for document annotation.

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

Abstract

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.

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: https://doi.org/10.1007/978-3-319-67008-9_5

• Conference Name: 21st International conference on theory and practice of digital libraries (TPDL 2017)
• Conference Location: Thessaloniki, Greece
• Start Date: Sep 18, 2017
• End Date: Sep 21, 2017
• Acceptance Date: May 26, 2017
• Online Publication Date: Sep 2, 2017
• Publication Date: Sep 30, 2017
• Deposit Date: Nov 10, 2017
• Publicly Available Date: Sep 3, 2018
• Print ISSN: 0302-9743
• Publisher: Springer
• Pages: 49-60
• Series Title: Lecture notes in computer science
• Series Number: 10450
• Series ISSN: 0302-9743
• ISBN: 9783319670072
• DOI: https://doi.org/10.1007/978-3-319-67008-9_5
• Keywords: Taxonomy; Text annotation; Information discovery
• Public URL: http://hdl.handle.net/10059/2586