Ontology Learning from Text Using Relational Concept Analysis
Bibliographic record
Abstract
We propose an approach for semi-automated construction of ontologies from text whose core component is a relational concept analysis (RCA) framework which extends formal concept analysis (FCA), a lattice-theory paradigm for discovering abstractions within objects x attributes tables, to the processing of several sorts of individuals described both by own properties and inter-individual links. As a pre-processing, text analysis is used to transform a document collection into a set of data tables, or contexts, and inter-context relations. RCA then turns these into a set of concept lattices with inter-related concepts. A core ontology is derived from the lattices in a semi-automated manner, by translating relevant lattice elements into ontological concepts and relations, i.e., either taxonomic or transversal ones. The ontology is further refined by abstracting new transversal relations from the initially identified ones using RCA. We discuss as well the results of an application of the method to astronomy texts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".