Relating RSS News/Items

  • Authors:
  • Fekade Getahun;Joe Tekli;Richard Chbeir;Marco Viviani;Kokou Yetongnon

  • Affiliations:
  • Laboratoire Electronique, Informatique et Image, (LE2I) --- UMR-CNRS Université de Bourgogne --- Sciences et Techniques Mirande, Dijon Cedex, France 21078;Laboratoire Electronique, Informatique et Image, (LE2I) --- UMR-CNRS Université de Bourgogne --- Sciences et Techniques Mirande, Dijon Cedex, France 21078;Laboratoire Electronique, Informatique et Image, (LE2I) --- UMR-CNRS Université de Bourgogne --- Sciences et Techniques Mirande, Dijon Cedex, France 21078;Laboratoire Electronique, Informatique et Image, (LE2I) --- UMR-CNRS Université de Bourgogne --- Sciences et Techniques Mirande, Dijon Cedex, France 21078;Laboratoire Electronique, Informatique et Image, (LE2I) --- UMR-CNRS Université de Bourgogne --- Sciences et Techniques Mirande, Dijon Cedex, France 21078

  • Venue:
  • ICWE '9 Proceedings of the 9th International Conference on Web Engineering
  • Year:
  • 2009

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Abstract

Merging related RSS news (coming from one or different sources) is beneficial for end-users with different backgrounds (journalists, economists, etc.), particularly those accessing similar information. In this paper, we provide a practical approach to both: measure the relatedness, and identify relationships between RSS elements. Our approach is based on the concepts of semantic neighborhood and vector space model, and considers the content and structure of RSS news items.