Enhancing Patent Expertise through Automatic Matching with Scientific Papers

Abstract : This paper focuses on a subtask of the QUAERO1 research program, a major innovating research project related to the automatic processing of multimedia and multilingual content. The objective discussed in this article is to propose a new method for the classification of scientific papers, developed in the context of an international patents classification plan related to the same field. The practical purpose of this work is to provide an assistance tool to experts in their task of evaluation of the originality and novelty of a patent, by offering to the latter the most relevant scientific citations. This issue raises new challenges in categorization research as the patent classification plan is not directly adapted to the structure of scientific documents, classes have high citation or cited topic and that there is not always a balanced distribution of the available examples within the different learning classes. We propose, as a solution to this problem, to apply an improved K-nearest-neighbors (KNN) algorithm based on the exploitation of association rules occurring between the index terms of the documents and the ones of the patent classes. By using a reference dataset of patents belonging to the field of pharmacology, on the one hand, and a bibliographic dataset of the same field issued from the Medline collection, on the other hand, we show that this new approach, which combines the advantages of numerical and symbolical approaches, improves considerably categorization performance, as compared to the usual categorization methods.
Type de document :
Communication dans un congrès
DS 2012 : The Fifteenth International Conference on Discovery Science, Oct 2012, Lyon, France. pp.299-312, 2012, 〈10.1007/978-3-642-33492-4_24〉
Liste complète des métadonnées

Littérature citée [33 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-00962386
Contributeur : Patricia Gautier <>
Soumis le : vendredi 21 mars 2014 - 11:10:53
Dernière modification le : mardi 24 avril 2018 - 13:35:12
Document(s) archivé(s) le : samedi 21 juin 2014 - 11:00:50

Fichier

DS_paper_45.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Kafil Hajlaoui, Pascal Cuxac, Jean-Charles Lamirel, Claire François. Enhancing Patent Expertise through Automatic Matching with Scientific Papers. DS 2012 : The Fifteenth International Conference on Discovery Science, Oct 2012, Lyon, France. pp.299-312, 2012, 〈10.1007/978-3-642-33492-4_24〉. 〈hal-00962386〉

Partager

Métriques

Consultations de la notice

276

Téléchargements de fichiers

187