Supporting the Development of Data Wrapping Ontologies (Extended Abstract)
Bibliographic record
Abstract
The use of a conceptual model or an ontology to wrap and describe relational data sources has been shown to be very effective in several frameworks involving management and access of data, such as information integration through mediated schemata [1], and the Semantic Web [2]. Ontologies provide a conceptual view of the application domain, which is closer to the user perspective, and automated reasoning can be leveraged to support exploration and querying of the underlying data sources. In this paper we focus on the problem of designing ontologies which describe relational data sources, and whose purpose is to provide a semantically enriched access to the underlying data. We use the term data wrapping ontologies to distinguish these ontologies from domain ontologies; whose purpose is to model a domain. In order to maximise the benefits of using data wrapping ontologies, these should be rich enough to ease their integration with the domain ontology and, at the same time, precisely characterise the data they wrap. Ontologies extracted automatically from data sources (e.g. by analysing the constraints in the logical schema) are faithful representations of the data sources; however, they are usually shallow and with a limited vocabulary. For this reason, they can be used as bootstrap ontologies, and the task of enriching the extracted ontology is crucial in order to build a truly effective ontology-based information access system. The process of enriching an ontology involves at least the introduction of new axioms and/or new terms. While, from a purely ontological viewpoint, an ontology can be arbitrarily modified, we need to bear in mind that the ultimate purpose of the data wrapper is to access the information available from the data sources. This means that newly introduced terms (concepts or roles) should be “backed” by data in the sources; i.e. queries over these terms should be rewritable w.r.t. data sources. It is easy to provide examples where newly introduced terms will always return empty answers, regardless the actual data contained in the sources (see Section 3). This not necessarily because they are unsatisfiable in the usual model ⋆ This paper is an excerpt from the ASWC 2009 paper “Supporting the Development of Data Wrapping Ontologies” by the same authors. The work presented in this paper has been partially funded by the European project ONTORULE. Proc. 23rd Int. Workshop on Description Logics (DL2010), CEUR-WS 573, Waterloo, Canada, 2010.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".