Generalized graph pattern discovery in linked data with data properties and a domain ontology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.009 | 0.017 |
| 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 it