A Hidden Markov Model-Based Approach for Lightweight Ontology Modularization Using K-Means Clustering
Bibliographic record
Abstract
Nowadays, ontologies are backbone of Semantic Web.Several domains use ontologies as knowledge models.As their number is constantly increasing, designers are opting to reuse some of those that exist to build new ones.When it is impossible to reuse a part depending on its organization, they import the whole ontology and this makes the manipulation cumbersome especially if the ontology has large concepts.Therefore, segmenting ontologies into partitions, if they are not yet, becomes a constant challenge for designers.This paper presents an approach to modularize ontology using hidden Markov model.Ontology triples are extracted within ontology through SPARQL queries and labelled with integers.The labelled triples constituted a Markov chain where ontology concepts are states and ontology relationships are symbols.This set is used to initialize HMM parameters such as states transition probabilities and symbols observation probabilities matrix and initial states probabilities vector.The transition probabilities matrix of HMM is then used as input of K-Means algorithm to generated modules of ontology concepts.This approach does not handled ontology axioms, which characterize heavy ontologies, and only lightweight ontologies are considered.Experiment on eighteen ontologies, obtained modules satisfied ontology modularization criteria such as independence, non-redundancy, correctness and completeness.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".