SIM‐NET: A View‐Based Semantic Similarity Model for<i>Ad Hoc</i>Networks of Geospatial Databases
Bibliographic record
Abstract
Abstract Semantic similarity is a fundamental notion in GIScience for achieving semantic interoperability among geospatial data. Until now, several semantic similarity models have been proposed; however, few of these models address the issues related to the assessment of semantic similarity in ad hoc networks. Also, several models are based on a definition of concepts where features are independent, an assumption that reduces the richness of the geospatial concept representation. This article presents the conceptual basis for Sim‐Net, a novel semantic similarity model for ad hoc networks based on Description Logics (DL). Sim‐Net is based on the multi‐view paradigm. This paradigm is used to include inferential knowledge in semantic similarity, that is, the knowledge about implicit dependencies between features of concepts. In Sim‐Net, assessing semantic similarity relies on the notions of Semantic Reference Systems and Formal Concept Analysis (FCA), which are combined to establish a common semantic reference frame for ontologies of the ad hoc network called the view lattice. The Sim‐Net semantic similarity measure distinguishes concepts that belong to different or similar domains and takes into account the neighbours of a concept in the network. An application example is used to show the positive impact of the properties of Sim‐Net.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".