Abstract
The heterogeneity and the great mass of information found on the web today require an information treatment before being used. The annotations, like all other information, must be filtered to determine those that are relevant. The new concept of “relevant annotation” can be then, considered as a new source of evidence. In addition to the vast amount of annotations, we notice that annotations express generally brief ideas using some words that they cannot be comprehensible independently of his context. This is why, we thought to classify it in clusters annotations sharing the same context and semantically related. In this paper, we propose a new model based on clustering for the classification and probabilistic model for the filtering. In the experiments, we tried to consider the relevant annotation classes as a new source of information able to improve the collaborative information retrieval.
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 subscriptionsReferences
Agosti, M., Ferro, N.: Annotations as context for searching documents. In: Crestani, F., Ruthven, I. (eds.) CoLIS 2005. LNCS, vol. 3507, pp. 155–170. Springer, Heidelberg (2005)
Aha, D.W. (ed.): Lazy Learning. Kluwer Academic Publishers, Norwell (1997)
Benzécri, J.P.: L’analyse des données. Dunod, Paris (1973)
Cabanac, G.: Annotation collective dans le contexte RI : définition d’une plate-forme pour expérimenter la validation sociale. In Conférence en Recherche d’Information et Applications, CORIA. pp. 385–392 (2008)
Celeux, G., Diday, E., Govaert, G.: Classification automatique de données environnement statistique et informatique. Dunod, Informatique (1989)
Chang, Y.K., Cirillo, C., Razon, J.: Evaluation of feedback retrieval using modified freezing, residual collection and test and control groups. In: the SMART Retrieval System- Experiments in Automatic Document Processing, pp. 355–370 (1971)
Chebil, W., Soualmia, L.F., Omri, M.N., Darmoni, S.J.: Indexing biomedical documents with a possibilistic network. J. Assoc. Inf. Sci. Technol. (2015). doi:10.1002/asi.23435,66(2)
Dinet, J.: Deux têtes cherchent mieux qu’une? In: Medialog, 63 (2007)
Frommholz, I., Fuhr, N.: Probabilistic, object-oriented logics for annotation-based retrieval in digital libraries. In: JCDL 2006: Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries, ACM Press, New York, NY, USA, pp. 55–64 (2006)
Kahan, J., Koivunen, M.R., Prud’Hommeaux, E., Swick, R.R.: Annotea: an open RDF infrastructure for shared Web annotations. Comp. Netw. 32(5), 589–608 (2002)
Mokhtari, N., Dieng-Kuntz, R.: Extraction et exploitation des annotations contextuelles. In: Proceedings Extraction et gestion des connaissances EGC (2008)
Naouar, F., Hlaoua, L., Omri, M.N.: Possibilistic model for relevance feedback in collaborative information retrieval. Int. J. Web Appl. IJWA 4(2), 78–86 (2012)
Naouar, F., Hlaoua, L., Omri, M.N., Relevance feedback for collaborative retrieval based on semantic annotations. In: The International Conference on Information and Knowledge Engineering (IKE 2013), pp. 54–60 (2013)
Omri, M.N., Chouigui, N.: Measure of similarity between fuzzy concepts for identification of fuzzy user’s requests in fuzzy semantic networks. Int. J. Uncertainty Fuzziness Knowl. Based Syst. (IJUFKS) 9(6), 743–748 (2001)
Omri M.N., Chouigui, N., Linguistic variables definition by membership function and measure of similarity. In: Proceedings of the 14th International Conference on Systems Science, vol. 2, pp. 264–273 (2001)
Omri, M.N.: Pertinent knowledge extraction from a semantic network: application of fuzzy sets theory. Int. J. Artif. Intell. IJAIT 13(3), 705–719 (2004)
Omri, M.N.: Effects of terms recognition mistakes on requests processing for interactive information retrieval. Int. J. Inf. Retrieval Res. IJIRR 2(3), 19–35 (2012)
Omri, M.N.: Système interactif flou d’aide à l’utilisation de dispositifs techniques: SIFADE. Thèse de l’université Paris VI, Paris (1994)
Omri, M.N., Tijus, C.A.: Uncertain and approximative knowledge representation in fuzzy semantic networks. In: The Twelfth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE-99) (1999)
Omri, M.N., Chenaina, T.: Uncertain and approximate knowledge representation to reasoning on classification with a fuzzy networks based system. In: Fuzzy Systems Conference Proceedings, FUZZ-IEEE 1999, vol. 3, pp. 1632–1637 (1999)
Syed, N.A., Liu, H., Sung, K.K.: Incremental learning with support vector machines. In: Proceedings of International Joint Conference on Artificial intelligence (IJCAI) (1999)
Tryon, R.C.: Cluster Analysis. Edwards Brothers, Ann Arbor (1939)
Usunier, N., Bottou, L.: Guarantees for approximate incremental SVMs. In: International Conference on Artificial Intelligence and Statistics, pp. 884–891 (2010)
Vassef, H., Li, C.S, Castelli, V.: Combining fast search and learning for fast similarity search. In: Proceedings of SPIE. The International Society for Optical Engineering, vol. 3972, pp. 32–42 (2000)
Weinberger, K., Saul, L.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. (JMLR) 10, 207–244 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Naouar, F., Hlaoua, L., Omri, M.N. (2015). Possibilistic Information Retrieval Model Based on Relevant Annotations and Expanded Classification. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-26532-2_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26531-5
Online ISBN: 978-3-319-26532-2
eBook Packages: Computer ScienceComputer Science (R0)