Description du projet
Nouvelle approche de la communication scientifique
L’accès aux publications scientifiques s’est amélioré depuis l’introduction des technologies numériques et de l’intelligence artificielle. Malheureusement, les procédures traditionnelles de communication scientifique, basées sur des flux de travail orientés document, ne peuvent pas répondre à l’augmentation rapide de la littérature universitaire. Avec l’aide d’experts en la matière, d’interconnexion de données et d’algorithmes d’apprentissage automatique, un nouveau graphe de connaissances est en cours de développement. Le projet ScienceGraph, financé par l’UE, développera une formule de pointe pour représenter, analyser, augmenter et exploiter la communication scientifique d’une manière basée sur la connaissance. Il exprimera et reliera les résultats scientifiques à travers des graphes de connaissances interconnectés et sémantiquement riches permettant la représentation d’informations scientifiques interdisciplinaires complexes, le traçage de toute leur évolution, du discours connecté et de la dérive des concepts.
Objectif
Despite an improved digital access to scientific publications in the last decades, the fundamental principles of scholarly communication remain unchanged and continue to be largely document-based. The document-oriented workflows in science have reached the limits of adequacy as highlighted by recent discussions on the increasing proliferation of scientific literature, the deficiency of peer-review and the reproducibility crisis.
In ScienceGRAPH we aim to develop a novel model for representing, analysing, augmenting and exploiting scholarly communication in a knowledge-based way by expressing and linking scientific contributions and related artefacts through semantically rich, interlinked knowledge graphs. The model is based on deep semantic representation of scientific contributions, their manual, crowd-sourced and automatic augmentation and finally the intuitive exploration and interaction employing question answering on the resulting ScienceGRAPH base.
Currently, knowledge graphs are still confined to representing encyclopaedic, factual information. ScienceGRAPH advances the state-of-the-art by enabling to represent complex interdisciplinary scientific information including fine-grained provenance preservation, discourse capture, evolution tracing and concept drift. Also, we will demonstrate that we can synergistically combine automated extraction and augmentation techniques, with large-scale collaboration to reach an unprecedented level of knowledge graph breadth and depth.
As a result, we expect a paradigm shift in the methods of academic discourse towards knowledge-based information flows, which facilitate completely new ways of search and exploration. The efficiency and effectiveness of scholarly communication will significant increase, since ambiguities are reduced, reproducibility is facilitated, redundancy is avoided, provenance and contributions can be better traced and the interconnections of research contributions are made more explicit and transparent.
Champ scientifique
Programme(s)
Régime de financement
ERC-COG - Consolidator GrantInstitution d’accueil
30167 Hannover
Allemagne