Abstract
In this paper we introduce the project of our PhD thesis. The subject is a model for news articles filtering. We propose a framework combining information about named entities extracted from news articles with article texts. Named entities are enriched with additional attributes crawled from semantic web resources. These properties are then used to enhance the filtering results. We described various ways of a user profile creation, using our model. This should enable news filtering covering any specific user needs. We report on some preliminary experiments and propose a complex experimental environment and different measures.
Chapter PDF
References
Liu, J., Dolan, P., Pedersen, E.R.: Personalized News Recommendation Based on Click Behavior. In: IUI 2010: Proceedings of the 2010 International Conference on Intelligent User Interfaces, pp. 31–40 (2010)
Das, A.S., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering. In: Proceedings of the 16th International Conference on World Wide Web (WWW 2007), pp. 271–280. ACM, New York (2007)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work (CSCW 1994), pp. 175–186. ACM, New York (1994)
Pazzani, M., Billsus, D.: Content-Based Recommendation Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)
Carreira, R., Crato, J.M., Gonalves, D., Jorge, J.A.: Evaluating adaptive user profiles for news classification. In: Proceedings of the 9th International Conference on Intelligent User Interfaces, pp. 206–212. ACM (2004)
Billsus, D., Pazzani, M.: A Hybrid User Model for News Story Classification (1999)
Billsus, D., Pazzani, M.J.: User Modeling for Adaptive News Access. In: User Modeling and User-Adapted Interaction, vol. 10, pp. 147–180. Springer, Netherlands (2000)
Bogers, T., Bosch, A.: Comparing and evaluating information retrieval algorithms for news recommendation. In: Proceedings of the ACM Conference on Recommender Systems (2007), pp. 141–144 (2007)
Kobilarov, G., Scott, T., Raimond, Y., Oliver, S., Sizemore, C., Smethurst, M., Bizer, C., Lee, R.: Media Meets Semantic Web – How The BBC Uses DBpedia and Linked Data to Make Connections. In: Aroyo, L., Traverso, P., Ciravegna, F., Cimiano, P., Heath, T., Hyvönen, E., Mizoguchi, R., Oren, E., Sabou, M., Simperl, E. (eds.) ESWC 2009. LNCS, vol. 5554, pp. 723–737. Springer, Heidelberg (2009)
Maruščák, D., Novotný, R., Vojtáš, P.: Unsupervised Structured Web Data and Attribute Value Extraction. In: Proceedings of 8th Annual Conference Znalosti 2009, Brno (2009)
Robert, I., Jurgen, U., Christian, B., Andreas, H.: LDSpider: An open-source crawling framework for the Web of Linked Data. In: Proceedings of 9th International Semantic Web Conference (ISWC 2010). Springer, Heidelberg (2010)
Robertson, S.E., Jones, K.S.: Relevance weighting of search terms. Journal of the American Society for Information Science, 129–146 (1976)
Lašek, I., Vojtáš, P.: Semantic Information Filtering - Beyond Collaborative Filtering. In: 4th International Semantic Search Workshop (2011), http://km.aifb.kit.edu/ws/semsearch11/11.pdf (accessed June 13, 2011)
Agrawal, R., Imielienski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proceedings of Conference on Management of Data, pp. 207–216. ACM Press, New York (1993)
Han, E.-H., Karypis, G.: Centroid-Based Document Classification: Analysis and Experimental Results. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 424–431. Springer, Heidelberg (2000)
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Symposium on Math, Statistics, and Probability, pp. 281–297. University of California Press, Berkeley (1967)
Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Heidelberg (1999)
Lv, Y., Moon, T., Kolari, P., Zheng, Z., Wang, X., Chang, Y.: Learning to model relatedness for news recommendation. In: Proceedings of the 20th International Conference on World Wide Web, WWW 2011, pp. 57–66. ACM Press (2011)
Robertson, S., Walker, S.: Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In: Proceedings of SIGIR 1994, pp. 232–241. ACM Press, New York (1994)
Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: Proceedings of SIGIR 1999, pp. 275–281. ACM Press, New York (1998)
Krajci, S., Krajciova, J.: Social Network and One-sided Fuzzy Concept Lattices. In: Proceedings of FUZZ-IEEE 2007, IEEE International Conference on Fuzzy Systems, pp. 1–6. Imperial College, London (2007)
Fišer, D.: Sémantická anotace doménově závislých dat. Katedra softwarového inženýrství, MFF UK (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lašek, I. (2011). DC Proposal: Model for News Filtering with Named Entities. In: Aroyo, L., et al. The Semantic Web – ISWC 2011. ISWC 2011. Lecture Notes in Computer Science, vol 7032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25093-4_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-25093-4_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25092-7
Online ISBN: 978-3-642-25093-4
eBook Packages: Computer ScienceComputer Science (R0)