Enabling better aggregation and discovery of cultural heritage content for Europeana and its partner institutions. Master's thesis oral defence
Notice bibliographique
Résumé
Presentation carried out during a master's thesis oral defence at the Haute école de gestion de Genève on 28 August 2020. The recording is available here: https://vimeo.com/453003769 Abstract of the master's thesis: Europeana, a non-profit foundation launched in 2008, aims to improve access to Europe’s digital cultural heritage through its open data platform which aggregates metadata and links to digital surrogates held by over 3700 providers. The data comes both directly from cultural heritage institutions (libraries, archives, museums) as well as through intermediary aggregators. Europeana’s current operating model leverages the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) and the Europeana Data Model (EDM) for data import through Metis, Europeana's ingestion and aggregation service. However, OAI-PMH is an outdated technology, is not web-centric, which presents high maintenance implications, in particular for smaller institutions. Consequently, Europeana seeks to find alternative aggregation mechanisms that could complement or supersede it over the long term, and which could also bring further potential benefits. The research scope of this master’s thesis is to extend on previous aggregation experiments that Europeana successfully carried out with various technologies, such as aggregation based on Linked Open Data (LOD) datasets or through the International Image Interoperability Framework (IIIF) APIs. The literature review first focuses on metadata standards and the aggregation landscape in the cultural heritage domain, and then provides an extensive overview of Web-based technologies with respect to two essential components enabling aggregation: data transfer and synchronisation as well as data modelling and representation. Three key results were obtained. First, the participation in the Europeana Common Culture project resulted in the documentation revision of the LOD-aggregator, a generic toolset for harvesting and transforming LOD. Second, 52 respondents completed an online survey to gauge the awareness, interest, and use of technologies other than OAI-PMH for (meta)data aggregation. Third, an assessment of potential aggregation pilots was carried out considering the 23 organisations who expressed interest in follow-up experiments on the basis of the available data and existing implementations. In the allotted time, one pilot was attempted using Sitemaps and Schema.org. In order to encourage the adoption of new aggregation mechanisms, a list of proposed suggestions was then established. All of these recommendations were aligned with the Europeana Strategy 2020-2025 and directed towards one or several of the key roles of the aggregation workflow (data provider, aggregator, Europeana). Even if a shift in Europena’s operating model would require extensive human and technical resources, the effort is clearly worthwhile as technologies presented in this dissertation are indeed well-suited for data enrichment and for keeping an easy update of data. The transition from OAI-PMH will also be facilitated by the integration of such mechanisms within the Metis Sandbox, Europeana's new ad-hoc system where contributors will be able to test their data sources before ingestion into Metis. Ultimately, this shift is also expected to lead to a better digital transformation and discoverability of digital cultural heritage objects.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,007 | 0,009 |
| Science ouverte | 0,001 | 0,010 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,006 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».