Query-answering with text and knowledge graph
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Query-answering (QA) is one of the key areas in Artificial Intelligence, where various researches are performed in recent years. Building query-answering system helps the organization of all sectors. Generating automatic responses saves both time and money. We examine the problem of query-answering over knowledge graphs (KG) where various QA approaches focus on simpler queries and do not work very well for complex queries or vice versa. In addition to that, reasoning over KG is also to be handled properly to predict the proper answer to the corresponding query. Models that use SPARQL are good at domain-related queries, but they are unable to handle out-of-domain queries. Combining contextual text representation and semantic graph representation is a challenge. Our area of research is to combine text and KG for open domain query-answering. Adapting the joint representation ensures that the model can perform well in both simple and complex queries. In this chapter, we explain the various works that have been conducted and the challenges that have come along with it.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it