Abstract
A summary is a comprehensive description that grasps the essence of a subject. A text, a collection of text documents, a query answer can be summarized by simple means such as an automatically generated list of the most frequent words or ”advanced” by a meaningful textual description of the subject. In between these two extremes are summaries by means of selected concepts exploiting background knowledge providing selected key concepts. We address in this paper an approach where conceptual summaries are provided through a conceptualization as given by an ontology. The idea is to restrict a background ontology to the set of concepts that appears in the text to be summarized and therebyl provide a structure, a so-called instantiated ontology, that is specific to the domain of the text and can be used to condense to a summary not only quantitatively but also conceptually covers the subject of the text. In this chapter we introduce different approaches to summarization. We consider a strictly ontologly based approach where summaries are derived solely from the instantiated ontology, a conceptual clustering over the instantiated concepts based on a semantic similarity measure, and an approach based on probabilities.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jensen, P.A., Nilsson, J.F.: Ontology-based Semantics for Prepositions, in Syntax and Semantics of Prepositions. In: Paint-Dizier, P. (ed.) Text, Speech and Language Technology, vol. 29. Springer, Heidelberg (2006)
Aronson, S.R.: Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. In: Proc. AMIA Symp., pp. 17–21 (2001)
Abney, S.: Partial parsing via finite-state cascades. In: Proceedings of the ESSLLI 1996 Robust Parsing Workshop (1996)
Bodenreider, O.: The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Research 32, D267–D270 (2004)
Hahn, U., Mani, I.: The Challenges of Automatic Summarization Computer (November 2000)
Melli, G., Wang, Y., Liu, Y., Kashani, M.M., Shi, Z., Gu, B., Sarkar, A., Popowich, F.: Description of SQUASH, the SFU Question Answering Summary Handler for the DUC 2005 Summarization Task. In: Proceedings of DUC 2005, Vancouver, Canada, pp. 103–110 (2005)
Shi, Z., Melli, G., Wang, Y., Liu, Y., Gu, B., Kashani, M.M., Sarkar, A., Popowich, F.: Question Answering Summarization of Multiple Biomedical Documents. In: Kobti, Z., Wu, D. (eds.) Canadian AI 2007. LNCS, vol. 4509, pp. 284–295. Springer, Heidelberg (2007)
Andreasen, T., Bulskov, H.: Conceptual Querying Through Ontologies. Fuzzy Sets and Systems (2008) (to appear)
Nilsson, J.F.: A logico-algebraic framework for ontologies – ONTOLOG. In: Jensen, P.A., Skadhauge, P. (eds.) First International OntoQuery Workshop, University of Southern Denmark (2001)
Miller, G.A., Chodorow, M., Landes, S., Leacock, C., Thomas, R.G.: Using a semantic concordance for sense identification. In: Proc. of the ARPA Human Language Technology Workshop, pp. 240–243 (1994)
Miller, G.A.: Wordnet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)
Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man, and Cybernetics 19(1), 17–30 (1989)
Unified Medical Language System U.S. National Library of Medicine, http://www.nlm.nih.gov/research/umls/
Medical Literature Analysis and Retrieval System Online U.S. National Library of Medicine, http://www.ncbi.nlm.nih.gov/pubmed/
Bulskov, H., Knappe, R., Andreasen, T.: On measuring similarity for conceptual querying. In: Andreasen, T., Motro, A., Christiansen, H., Larsen, H.L. (eds.) FQAS 2002. LNCS, vol. 2522, pp. 100–111. Springer, Heidelberg (2002)
Resnik, P.: Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language (1999)
Andreasen, T., Knappe, R., Bulskov, H.: Domain-specific similarity and retrieval. In: Proceedings IFSA 2005, pp. 496–502. Tsinghua University Press (2005)
Andreasen, T., Jensen, P.A., Nilsson, J.F., Paggio, P., Pedersen, B.S., Thomsen, H.E.: Content-based text querying with ontological descriptors. Data Knowledge Engineering 48(2), 199–219
Yager, R.R., Petry, F.E.: A Multicriteria Approach to Data Summarization Using Concept Hierarchies. IEEE Trans. on Fuzzy Sys. 14(6) (2006)
Bulskov, H., Andreasen, T., Terney, T.V.: Conceptual Summaries as Query Answers. In: Fuzzy Information Processing Society, 2007. NAFIPS apos 2007. Annual Meeting of the North American, June 24-27, pp. 458–462 (2007)
Andreasen, T., Bulskov, H.: Conceptual Querying Through Ontologies. In: Fuzzy Sets and Systems (2008) (to appear)
Zhou, X., Han, H.: Survey of word sense disambiguation approaches. In: 18th FLAIRS Conference (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Andreasen, T., Bulskov, H. (2009). On Deriving Data Summarization through Ontologies to Meet User Preferences. In: Ras, Z.W., Dardzinska, A. (eds) Advances in Data Management. Studies in Computational Intelligence, vol 223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02190-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-02190-9_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02189-3
Online ISBN: 978-3-642-02190-9
eBook Packages: EngineeringEngineering (R0)