Generalized graph pattern discovery in linked data with data properties and a domain ontology
Bibliographic record
Abstract
Nowadays, in many practical situations, analytical tasks need to be performed on complex heterogeneous data, often described by a domain ontology (DO). Such cases abound in life science fields such as agro-informatics, where observations and measures on animals/plants are logged for subsequent mining. The data is naturally structured as graph(s), unlabelled and missing some values, hence it fits well pattern mining. In our own precision farming project aimed at decision support for dairy cow management, we mine for knowledge in milk production data. In one task, we aim at contrast patterns explaining the relative impact of independent production factors. To that end, ontologically-generalized graph patterns (OGPs), a variety of generalized graph patterns, where vertices and edges are labelled by DO classes and properties, respectively, were defined. A mining methodology was also designed that reconciles OWL DOs, abstraction from RDF graphs and literals in data. To address the well-known cost-related limitations of graph mining -exacerbated here by class/property specializations and data properties- we split the mining task into (1) mining of generic object property topology patterns and (2) label refinement. Those focus on two sorts of OGPs, called topologies and class stars, respectively, which, after being mined separately, get (3) assembled into fully-fledged OGPs.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".