Bibliographic record
Abstract
There are various challenges in developing complex monolithic ontologies such as reduced reasoning performance, increased complexity in maintenance, and difficulty in integration. The development of ontologies in a modular manner can address these issues and enhance understandability, reusability and evolvability of ontologies as well as lead to better performance on ontology reasoning. This thesis proposes the Semantic Interface-based Modular Ontology Framework (SIMOF), a framework for developing and maintaining ontology modules and modular ontologies based on the notion of semantic interfaces. Semantic interfaces abstract the semantics of ontology modules from different points of views and enable the smooth integration of independently-developed ontology modules. SIMOF is composed of three main components: Interface-Based modular ontology Formalism (IBF), revision management component, and quality management component. IBF, as the core component of SIMOF, provides formal definitions for the syntax and semantics of ontology modules, interfaces, and modular ontologies based on the formal semantics of description logic knowledge bases. IBF also supports reasoning and query processing over modular ontologies based on a proposed module augmentation procedure as well as existing algorithms for reasoning over description logic knowledge bases. The revision management component supports revising ontology modules. A revision operator is proposed that satisfies some of the most important postulates of the belief revision theory. In addition, a revision algorithm is proposed that resolves inconsistencies induced by module modifications. The quality management component analyzes the quality of ontology modules. A set of quality metrics are proposed for measuring the quality of ontology modules and modular ontologies. These metrics are based on semantic-based definitions for dependencies between local and external knowledge represented in ontology modules. The prototype implementation of SIMOF includes an extension of the Web Ontology Language (OWL) that is designed based on IBF, an extension to the SWOOP ontology editor for developing modular ontologies, a new reasoner for modular ontology reasoning, and a query engine for executing conjunctive queries over modular ontologies. Two application examples of SIMOF are also described in this thesis for applying modular ontologies in real-world domains of discourse. Finally, the characteristics of SIMOF and comparison with other ontology modularization frameworks are discussed.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".