Query-answering with text and knowledge graph
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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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".