ISSA: generic pipeline, knowledge model and visualization tools to help scientists search and make sense of a scientific archive

Toulet Anne, Michel Franck, Bobasheva Anna, Menin Aline, Dupré Sébastien, Deboin Marie-Claude, Winckler Marco, Tchechmedjiev Andon. 2022. ISSA: generic pipeline, knowledge model and visualization tools to help scientists search and make sense of a scientific archive. In : The Semantic Web – ISWC 2022: 21st International Semantic Web Conference, Virtual Event, October 23–27, 2022, Proceedings. Sattler Ulrike (ed.), Hogan Aidan (ed.), Keet Maria (ed.), Presutti Valentina (ed.), Almeida João Paulo A. (ed.), Takeda Hideaki (ed.), Monnin Pierre (ed.), Pirrò Giuseppe (ed.), D’Amato Claudia (ed.). Cham : Springer, 660-677. (Lecture Notes in Computer Science, 13489) ISBN 978-3-031-19432-0 International Semantic Web Conference (ISWC 2022). 21, Hangzhou, Chine, 23 Octobre 2022/27 Octobre 2022.

https://doi.org/10.1007/978-3-031-19433-7_38

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Résumé : Faced with the ever-increasing number of scientific publications, researchers struggle to keep up, find and make sense of articles relevant to their own research. Scientific open archives play a central role in helping deal with this deluge, yet keyword-based search services often fail to grasp the richness of the semantic associations between articles. In this paper, we present the methods, tools and services implemented in the ISSA project to tackle these issues. The project aims to (1) provide a generic, reusable and extensible pipeline for the analysis and processing of articles of an open scientific archive, (2) translate the result into a semantic index stored and represented as an RDF knowledge graph; (3) develop innovative search and visualization services that leverage this index to allow researchers, decision makers or scientific information professionals to explore thematic association rules, networks of co-publications, articles with co-occurring topics, etc. To demonstrate the effectiveness of the solution, we also report on its deployment and user-driven customization for the needs of an institutional open archive of 110,000+ resources. Fully in line with the open science and FAIR dynamics, the presented work is available under an open license with all the accompanying documents necessary to facilitate its reuse. The knowledge graph produced on our use-case is compliant with common linked open data best practices.

Mots-clés libres : Data indexing, Scientific literature, Information Retrieval, Linked open data, Knowledge graph

Agences de financement hors UE : Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation, CollEx-Persée

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