Abstract
This article is a continuation of research work started with an idea of semantic compression. As authors proved that semantic compression is viable concept for English, they decided to focus on potential applications. An algorithm is presented that employing WiSENet allows for knowledge acquisition with flexible rules that yield high precision results. Detailed discussion is given with description of devised algorithm, usage examples and results of experiments.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ceglarek, D., Haniewicz, K., Rutkowski, W.: Semantically enhanced intellectual property protection system - sEIPro2S. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS, vol. 5796, pp. 449–459. Springer, Heidelberg (2009)
Ceglarek, D., Haniewicz, K., Rutkowski, W.: Semantic compression for specialised information retrieval systems. In: Nguyen, N.T., Katarzyniak, R., Chen, S.-M. (eds.) Advances in Intelligent Information and Database Systems. SCI, vol. 283, pp. 111–121. Springer, Heidelberg (2010)
Ceglarek, D., Haniewicz, K., Rutkowski, W.: Quality of semantic compression in classification. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ICCCI 2010. LNCS, vol. 6421, pp. 162–171. Springer, Heidelberg (2010)
Gonzalo, J., et al.: Indexing with WordNet Synsets can improve Text Retrieval (1998)
Hotho, A., Staab, S., Stumme, G.: Explaining text clustering results using semantic structures. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 217–228. Springer, Heidelberg (2003)
Hotho, A., Maedche, A., Staab, S.: Ontology-based Text Document Clustering. In: Proceedings of the Conference on Intelligent Information Systems, Zakopane. Physica/Springer, Heidelberg (2003)
Khan, L., McLeod, D., Hovy, E.: Retrieval effectiveness of an ontology-based model for information selection (2004)
Krovetz, R., Croft, W.B.: Lexical Ambiguity and Information Retrieval (1992)
Frakes, W.B., Baeza-Yates, R.: Information Retrieval: Data Structures and Algorithms. Prentice-Hall, Englewood Cliffs (1992)
McNaughton, R., Yamada, H.: Regular expressions and state graphs for automata. IRE Transactions on Electronic Computers EC-9(1), 39–47 (1960)
Fellbaum, C.: WordNet - An Electronic Lexical Database. MIT Press, Cambridge (1998), ISBN:978-0-262-06197-1
Zellig, H.: Distributional Structure. Word 10(2/3), 146–162 (1954)
Califf, M.E., Mooney, R.J.: Bottom-up relational learning of pattern matching rules for information extraction. J. Mach. Learn. Res. 4, 177–210 (2003)
Percova, N.N.: On the types of semantic compression of text. In: COLING 1982 Proceedings of the 9th Conference on Computational Linguistics, Praha, vol. 2 (1982)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Ceglarek, D., Haniewicz, K., Rutkowski, W. (2011). Towards Knowledge Acquisition with WiSENet. In: Nguyen, N.T., Trawiński, B., Jung, J.J. (eds) New Challenges for Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19953-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-19953-0_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19952-3
Online ISBN: 978-3-642-19953-0
eBook Packages: EngineeringEngineering (R0)