Realization of Semantic Search Using Concept Learning and Document Annotation Agents.
Bibliographic record
Abstract
Currently, search systems are based on commitment to a common ontology. In the real world, it is preferred to enable Web repositories to exchange information freely while keeping their own ontology. This helps contents providers to represent the information independently in the repositories at the expense of bringing complexity to the communication and negotiation. To solve the communication complexity problem we present (1) a method for semantic search supported by ontological concept learning, and (2) a prototype multi-agent system that can handle semantic search while encapsulating complexity of such process from the users. The method introduces a spiral search process and a layered structure of semantic interoperability. Agents, which conduct semantic search on behalf of users, deploy ontologies to organize documents in their corresponding repositories. Through a detailed experiment we will show that agents can improve their search capability by learning new concepts from each other, and consequently, provide better search results to the users. Index Terms — multi-agent system, semantic search, ontology, concept learning, interoperability, annotation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".