Identification of Web Information using Concept Hierarchies and On-line Updates of Concept Importance
Bibliographic record
Abstract
The Internet users have to perform a lot of search to find web pages with relevant information. The paper proposes an approach for utilization of a hierarchy of concepts to perform identification of web pages in the environment of the Semantic Web. A user provides a hierarchy of concepts that can only partially cover their domain of interest. Ontologies related to that domain are used to instantiate the hierarchy with concrete information, as well as to enhance it with new concepts initially unknown to the user. A web page is checked against concepts from the hierarchy and activation levels of those concepts are aggregated using Ordered Weighted Averaging (OWA) operators that are part of the hierarchy of concepts. Mechanisms of aggregations embedded in OWA operators are determined using linguistic quantifiers and importance of concepts. The importance of concepts is constantly changing, and in order to automatically assign importance values and keep track of changes a new algorithm for Adaptive Assigning of Term Importance (AATI) is used. Keywords—hierarchy of concepts, ontology, ordered weighted aggregation, text identification
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".