Fast and Accurate Framework for Ontology Matching in Web of Things
Bibliographic record
Abstract
The Web of Things (WoT) can help with knowledge discovery and interoperability issues in many Internet of Things (IoT) applications. This article focuses on semantic modeling of WoT and proposes a new approach called Decomposition for Ontology Matching (DOM) to discover relevant knowledge by exploring correlations between WoT data using decomposition strategies. The DOM technique adopts several decomposition techniques to order highly linked ontologies of WoT data into similar groups. The main idea is to decompose the instances of each ontology into similar groups and then match instances of similar groups instead of entire instances of two ontologies. Three main algorithms for decomposition have been developed. The first algorithm is based on radar scanning, which determines the distribution of distances between each instance and all other instances to determine the cluster centroid. The second algorithm is based on adaptive grid clustering, where it focuses on distribution information and the construction of spanning trees. The third algorithm is based on split index clustering, where instances are divided into groups of cells from which noise is removed during the merging process. Several studies were conducted with different ontology databases to illustrate the use of the DOM technique. The results show that DOM outperforms state-of-the-art ontology matching models in terms of computational cost while maintaining the quality of the matching. Moreover, these results demonstrate that DOM is capable of handling various large datasets in WoT contexts.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".