Using Linked Data to Build Semantic Web Applications: A Case Study
Bibliographic record
Abstract
Linked data applications offer a promising avenue to harness the power of the Semantic Web.However, realizing this power involves addressing a variety of technical, data-centric, and user experience challenges.This paper presents MusicFans, a realworld application developed as part of a case study intended to assess the practicality and effectiveness of using the Linked Open Data (LOD) cloud within a specific domain while deliberately avoiding the need for developing local ontologies and data repositories.Two primary challenges that we have encountered in the development of MusicFans were: i) establishing the application's domain scope due to the limited LOD cloud resources providing usable SPARQL endpoints, and ii) crafting a user interface that enables users not familiar with the SPARQL query language to place queries.The later challenge is compounded by the fact that SPARQL queries must align closely with the structure and the semantics of the underlying dataset schemas.We advocate that currently the most viable approach to tackle this challenge is to rely on a pre-defined library of dynamically generated SPARQL query templates.The paper delves into the design of these templates and discusses how they facilitate user interaction with the application in diverse real-world scenarios.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".