Building semantic mappings from databases to ontologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A recent special issue of AI Magazine (AAAI 2005) was dedicated to the topic of semantic integration — the problem of sharing data across disparate sources. At the core of the solution lies the discovery the “semantics” of different data sources. Ideally, the semantics of data are captured by a formal ontology of the domain together with a semantic mapping connecting the schema describing the data to the ontology. However, establishing the semantic mapping from a database schema to a formal ontology in terms of formal logic expressions is inherently difficult to automate, so the task was left to humans. In this paper, we report on our study (An, Borgida, & Mylopoulos 2005a; 2005b) of a semi-automatic tool, called MAPONTO, that assists users to discover plausible semantic relationships between a database schema (relational or XML) and an ontology, expressing them as logical formulas/rules.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it