OntSum: A Semantic Query Routing Scheme in P2P Networks Based on Concise Ontology Indexing
Bibliographic record
Abstract
Locating desirable resources is very important for a large distributed system. However, the distributed, heterogeneous, and unstructured nature of the system makes this issue very challenging. The discovering mechanism has to be not only semantically rich, in order to cope with complex queries, but also scalable to handle large numbers of information sources. In this paper, we address these problems by proposing OntSum, an efficient peer-to-peer query routing scheme based on concise ontology indexing. Unlike most existing systems, our system does not assume a global ontology but heterogeneous ontologies. Peers in the system use their own ontologies to describe their resource knowledge and the network topology is adjusted according to peers' ontological properties. A novel indexing strategy enables forwarding queries only to semantically related nodes. The architecture improves interoperability among network participants and aids efficient resource discovery through an expressive query language.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".