D4.2 – Initial report on ontology implementation
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.066 | 0.073 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".