Decision Support Tools for Ontological Engineering
Bibliographic record
Abstract
Development cost of knowledge-based systems is often high because knowledge bases are often constructed from scratch in a distributed and heterogeneous environment involving different locations, system types, knowledge representation mechanisms and stakeholders. Hence, it is difficult to share and re-use existing knowledge base components. Sharing and re-using knowledge can help reduce costs and make it possible to create systems capable of more powerful problem solving. The Semantic Web (SW) is the next evolutionary step for the World Wide Web (Web). The SW aims to provide semantics to data on the Web, enabling computers to more easily share and perform problem solving on the data SW technology can be used to share and re-use knowledge between KBSs in a distributed and heterogeneous environment. Semantic data networks on the Web provide the bases of the SW, enabling knowledge that is distributed over the Web to be easily shared and processed by a machine. A semantic data structure is required for representing data or knowledge in a shareable format, and an ontology is designed to fulfill this objective. According to the World Wide Web Consortium, an ontology is "a specification of a conceptualization" (Gruber, 1993); ontologies are the vocabularies of the SW that define the concepts and relationships used to describe an area of interest. An ontology represents the definitions of concepts, axioms and facts which discribe real world phenomenon of interest. As suggested in Hence, an ontology can also become the basis for building knowledge-based systems. Ontologies implemented in XML 1 based languages, such as RDF 2 and OWL 3 , enable different KBS development groups or different Semantic Web applications to share and re-use their knowledge and data. Knowledge Acquisition (KA) is an important step for building knowledge-based systems (KBS). In the process, software tools are often used for helping knowledge engineers construct ontologies and knowledge bases more efficiently and effectively. The objective of 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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".