Towards practical taxonomic classification for description logics on the Semantic Web
Bibliographic record
Abstract
Description logics offer a well-defined semantics for many common and useful reasoning tasks that can be formalized under the notion of subsumption and are currently finding use as a representation language for the Semantic Web (e.g. DAML+OIL). For this domain, description logics can be highly useful for reasoning about relationships between entities in distributed knowledge bases by classifying them into a taxonomic subsumption hierarchy. However, there are many practical considerations for designing a sound and complete, yet efficient algorithm for performing large-scale taxonomic classification. Traditionally, structural subsumption algorithms have provided efficient techniques for performing classification but have only supported relatively inexpressive languages. Consequently, in response to the need for efficient taxonomic classification of more expressive languages, we develop an approach that extends previous structural subsumption algorithms to support sound and complete classification of a moderately expressive description logic including both conjunctive and disjunctive constructors. Furthermore, we extend this algorithm to a more expressive description logic with the claim that its sources of incompleteness are infrequent and benign in practice. Finally, we show that for the expected distribution of concept structures, both of these taxonomic classification algorithms require polynomial time in the size of the knowledge base and argue that such a result is a necessity for practical classification algorithms that will scale with the expected growth of the Semantic Web. 1
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".