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SWAP - A Framework for Ontology Support in Semantic Web Applications

2007· book-chapter· en· W2496528216 on OpenAlexaff
Arijit Sengupta, Henry Kim

Bibliographic record

VenueIGI Global eBooks · 2007
Typebook-chapter
Languageen
FieldComputer Science
TopicService-Oriented Architecture and Web Services
Canadian institutionsYork University
Fundersnot available
KeywordsComputer scienceData WebSemantic Web StackWorld Wide WebSemantic WebWeb serviceDatabaseOWL-SWeb modeling

Abstract

fetched live from OpenAlex

We present SWAP (Semantic Web application pyramid), a framework for incorporating ontologies in data-oriented semantic Web applications. We have implemented this framework with a measurement ontology for a quality management Web service. This quality management Web service is built on top of a set of XML Web services implementing agents representing quality management clients, quality management servers, and vendors. SWAP facilitates data exchange between these Web services with vendor data stored in databases, and the processing of the data using a combination of RuleML and SQL. The testbed implementation demonstrates the feasibility and scalability of the framework for any type of three-tier ontology-based semantic Web applications involving low to moderate data exchange. We discuss methods for improving this framework for high data exchange volumes as well. The primary contribution of this framework is in the component-based implementation of real-world semantic Web applications.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0030.004
Science and technology studies0.0020.003
Scholarly communication0.0080.010
Open science0.0050.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0060.005

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.

Opus teacher head0.021
GPT teacher head0.287
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

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