REAL TIME SEMANTIC INTEROPERABILITY IN AD HOC NETWORKS OF GEOSPATIAL DATA SOURCES: CHALLENGES, ACHIEVEMENTS AND PERSPECTIVES
Bibliographic record
Abstract
Abstract. Recent advances in geospatial technologies have made available large amount of geospatial data. Meanwhile, new developments in Internet and communication technologies created a shift from isolated geospatial databases to ad hoc networks of geospatial data sources, where data sources can join or leave the network, and form groups to share data and services. However, effective integration and sharing of geospatial data among these data sources and their users are hampered by semantic heterogeneities. These heterogeneities affect the spatial, temporal and thematic aspects of geospatial concepts. There have been many efforts to address semantic interoperability issues in the geospatial domain. These efforts were mainly focused on resolving heterogeneities caused by different and implicit representations of the concepts. However, many approaches have focused on the thematic aspects, leaving aside the explicit representation of spatial and temporal aspects. Also, most semantic interoperability approaches for networks have focused on automating the semantic mapping process. However, the ad hoc network structure is continuously modified by source addition or removal, formation of groups, etc. This dynamic aspect is often neglected in those approaches. This paper proposes a conceptual framework for real time semantic interoperability in ad hoc networks of geospatial data sources. The conceptual framework presents the fundamental elements of real time semantic interoperability through a hierarchy of interrelated semantic states and processes. Then, we use the conceptual framework to set the discussion on the achievements that have already been made, the challenges that remain to be addressed and perspectives with respect to these challenges.
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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 | 0.020 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.014 | 0.027 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| 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".