Proceedings of the 2nd international workshop on Ontologies and information systems for the semantic web
Bibliographic record
Abstract
The emergence of the World Wide Web made massive amounts of data available and data exists in many scattered electronic data sources (e-sources) over the Web. Even though some of the data isin well-organized data sources, interoperability and integration with data from other sources, semantic coordination and conflict resolution are required for its full exploitation. Semantic Web enabled applications can potentially produce better results for semantic integration, interoperability and search. In particular, ontologies are widely regarded as the best solution toglobal information integration and semantic interoperability. The main objective of the 2nd International Workshop on Ontologies and Information Systems for the Semantic Web (ONISW 2008) is to bring together researchers in Information Management interested in the relation between ontology and information models, to present results and to discuss theoretical aspects and good practice. The Call for Paper solicited contributions that cover topics such as Ontology and epistemology in information systems, Ontology learning, Semantic interoperability, Ontology-based schema mapping/matching and integration, Ontology mapping tools, languages, and visualization, Schema transformation, Ontology-based data transformation and data migration tools, Ontology-based query mediation, Querying the Semantic Web, Ontology-driven application system and Web service design, etc. The Call for Papers attracted 21 submissions from Africa, Asia, Canada, Europe, and the United States. Each paper carefully reviewed by at least three members of program committee. Finally, the program committee accepted 16 papers. This volume of the proceedings contains papers presented in the 2nd International Workshop on Ontologies and Information Systems for the Semantic Web (ONISW 2008), which was held in Napa Valley, California, October 30, 2008. The workshop was held in conjunction with ACM 17th Conference on Information and Knowledge Management (CIKM).
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.028 | 0.011 |
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".