Report on the Third Workshop on Exploiting Semantic Annotations in Information Retrieval (ESAIR), Toronto, Canada
Bibliographic record
Abstract
There is an increasing amount of structure on the Web as a result of modern Web lan- guages, user tagging and annotation, and emerging robust NLP tools. These meaningful, semantic, annotations hold the promise to significantly enhance information access, by en- hancing the depth of analysis of today?s systems. Currently, we have only started exploring the possibilities and only begin to understand how these valuable semantic cues can be put to fruitful use. The workshop had an interactive format consisting of keynotes, boasters and posters, breakout groups and reports, and a final discussion, which was prolonged into the evening. There was a strong feeling that we made substantial progress. Specifically, each of the breakout groups contributed to our understanding of the way forward. First, annotations and use cases come in many different shapes and forms depending on the domain at hand, but at a higher level there are commonalities in annotation tools, indexing methods, user interfaces, and general methodology. Second, there is a framework emerging to view annota- tion as (1) a linking procedure, connecting (2) an analysis of information objects with (3) a semantic model of some sort, expressing relations that contribute to (4) a task of interest to end users. Third, we should look at complex tasks that cannot be comprehensible articulated in a few keywords, and embrace interaction both to incrementally refine the search request and to explore the results at various stages, guided by the semantic structure.
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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.099 | 0.028 |
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".