Representing and classifying arguments on the Semantic Web
Bibliographic record
Abstract
Abstract Until recently, little work has been dedicated to the representation and interchange of informal, semi-structured arguments of the type found in natural language prose and dialogue. To redress this, the research community recently initiated work towards an Argument Interchange Format (AIF). The AIF aims to facilitate the exchange of semi-structured arguments among different argument analysis and argumentation-support tools. In this paper, we present a Description Logic ontology for annotating arguments, based on a new reification of the AIF and founded in Walton's theory of argumentation schemes. We demonstrate how this ontology enables a new kind of automated reasoning over argument structures, which complements classical reasoning about argument acceptability. In particular, Web Ontology Language reasoning enables significantly enhanced querying of arguments through automatic scheme classifications, instance classification, inference of indirect support in chained argument structures, and inference of critical questions. We present the implementation of a pilot Web-based system for authoring and querying argument structures using the proposed ontology.
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.
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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".