D4.2 – Initial report on ontology implementation
Notice bibliographique
Résumé
This deliverable provides an initial report on the work done on ontology implementation during the first 24 months of ARIADNEplus, assessing it and planning the related activities for the second period, i.e. months 25-48. The activity happens primarily under T4.4, but related work also takes place under WP2 (Extending and Supporting the ARIADNE community), WP5 (Extending the ARIADNEplus data infrastructure), WP12 (data integration and interoperability), and WP14 (The ARIADNEplus knowledge management system), and these provide the focus of other deliverables either already submitted (D2.2 and D5.2) or due shortly (D12.2 and D14.1). The overall objective of WP4 is to Integrate the datasets of the Archaeological Research Communities, and Task 4.4 is focussed on Implementing the ARIADNE ontology. The task concerns the implementation of the ARIADNE ontology extensions, known as application profiles, to specific subdomains of archaeology and archaeological science. The work is organized in subtasks by domain. The deliverable introduces the AO-Cat and it discusses the distinction between collection and item-level records. It reports on the state of progress on the development of application profiles in each subdomain and introduces the plans for harmonisation of the profiles at the implementation stage. The AO-Cat itself provides a suitable application profile for sites and monument records and excavation reports (sub-task 4.4.0), as well as for individual artefacts (sub-task 4.4.7). It also appears that it will be sufficient to describe site-level information within most of the other sub-domains. However, it is anticipated that more specific application profiles will be required for other subtasks, including palaeo-anthropology (4.4.1). The most advanced application profile is an extension of the CIDOC CRM for Heritage Science. It appears that this may be adapted to cover several laboratorybased sub-domains, including Bio-archaeology and Ancient DNA (4.4.2), Environmental Archaeology (4.4.3), Inorganic Materials study (4.4.4), and Dating (4.4.5). The sub-domain of field survey (4.4.6) may also need its own application profile, as will specific aspects of remote sensing (4.4.8), and standing structures (4.4.9), although the working group on spatiotemporal data (4.4.10) has agreed that the field is so diverse and fragmented that the first priority has to be a catalogue of geospatial services. Maritime and underwater archaeology (4.4.11) is currently on hold, but is served by AO-Cat to some extent. Archaeological fieldwork (4.4.12) is also covered by AO-Cat at site level, but detailed excavation archives would require a complex application profile, although several partners have already done work on mapping their databases to the CIDOC-CRM and work is underway on developing an application profile. The applications profiles for inscriptions (4.4.13) and burials (4.4.14) are also relatively well advanced. The next priorities are to complete work on those application profiles that are already well advanced, to assess which sub-domains which are underway can be amalgamated and harmonised using the CIDOC CRM and its extensions, such as CRMarchaeo, and to complete the outstanding profiles, where possible. Workshops are planned to investigate how the application profiles can be implemented within VREs to be developed in D4Science, and how these will help address the research questions of archaeologists by allowing them to combine multiple datasets.
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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 | 0,018 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,007 | 0,007 |
| Science ouverte | 0,004 | 0,008 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,066 | 0,073 |
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 ».