Relating RSS news/items

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5648 LNCS, Issue , 2009, Pages 442-452

  Getahun, Fekade ^a   Tekli, Joe ^a   Chbeir, Richard ^a   Viviani, Marco ^a   Yetongnon, Kokou ^a  

^a UNIVERSITÉ DE BOURGOGNE   (France)

Author keywords

Neighbourhood; Relationships; RSS Relatedness; Similarity

Indexed keywords

CONTENT AND STRUCTURE; END-USERS; NEIGHBOURHOOD; RELATIONSHIPS; SIMILARITY; VECTOR SPACE MODELS;

EID: 70350216067     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02818-2_36     Document Type: Conference Paper

References (19)

1. Bille, P.: A survey on tree edit distance and related problems. Theoretical CS 337(1-3), 217-239 (2005)
2. Budanitsky, A., Hirst, G.: Evaluating wordnet-based measures of lexical semantic relatedness. Computational Linguistics 32(1), 13-47 (2006)
3. Chawathe, S.S.: Comparing hierarchical data in external memory. In: VLDB 1999, pp. 90-101. Morgan Kaufmann Publishers Inc., San Francisco (1999)
4. Flesca, S., Manco, G., Masciari, E., Pontieri, L.: Fast detection of xml structural similarity. IEEE Transactions on Knowledge and Data Engineering 17(2), 160-175 (2005)
5. Garcia, I., Ng, Y.: Eliminating Redundant and Less-Informative RSS News Articles Based on Word Similarity and a Fuzzy Equivalence Relation. In: ICTAI 2006, pp. 465-473 (2006)
6. Getahun, F., Tekli, J., Atnafu, S., Chbeir, R.: Towards efficient horizontal multimedia database fragmentation using semantic-based predicates implication. In: SBBD 2007, pp. 68-82 (2007)
7. Grabs, T., Schek, H.-J.: Generating Vector Spaces On-the-fly for Flexible XML Retrieval. In: ACM SIGIR Workshop on XML and Information Retrieval 2002, pp. 4-13 (2002)
8. Kade, A.M., Heuser, C.A.: Matching XML documents in highly dynamic applications. In: ACM symposium on Document engineering 2008, pp. 191-198 (2008)
9. La Fontaine, R.: Merging XML files: A new approach providing intelligent merge of XML data sets. In: Proceedings of XML, Barcelona, Spain (May 2002)
10. Lau, H., Ng, W.: A Unifying Framework for Merging and Evaluating XML Information. In: Zhou, L.-z., Ooi, B.-C., Meng, X. (eds.) DASFAA 2005. LNCS, vol. 3453, pp. 81-94. Springer, Heidelberg (2005)
11. Lin, D.: An Information-Theoretic Definition of Similarity. In: Proceedings of the 15th International Conference on Machine Learning, pp. 296-304. Morgan Kaufmann Publishers, San Francisco (1989)
12. McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)
13. Nierman, A., Jagadish, H.V.: Evaluating structural similarity in XML documents. In: WebDB 2002, pp. 61-66 (2002)
14. Princeton University Cognitive Science Laboratory. WordNet: a lexical database for the English language, http://wordnet.princeton.edu/
15. R. Richardson and A. F. Smeaton. Using WorldNet in a knowledge-based approach to information retrieval. Technical Report CA-0395, Dublin, Ireland (1995)
16. RSS Advisory Board. RSS 2.0 Specification, http://www.rssboard.org/
17. Tekli, J., Chbeir, R., Yetongnon, K.: A hybrid approach for xml similarity. SOFSEM 07: 783-795
18. Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd annual meeting on Association for Computational Linguistics, Morristown, NJ, USA, pp. 133-138 (1994)
19. WWW Consortium. The Document Object Model, http://www.w3.org/DOM