A Categorization of KR&R Methods for Requirement Analysis of a Query Answering Knowledge Base
Bibliographic record
Abstract
Our long-term goal is to build a query answering system that can answer questions on a wide variety of topics and explain the answers. In such a situation, a designer faces the challenge of how to specify the KR&R requirements that are needed to answer questions. In this paper, we introduce a categorization of KR&R methods, and apply it to specifying the requirements for answering questions in six different domains: Physics, Chemistry, Biology, Environmental Science, Microeconomics, and U.S. Government & Politics. Drawing from the corpus of about 500 questions that we analyzed, we consider an example question in each domain and show the analytical process that we used to derive the requirements in terms of the KR&R categorization. We analyze the effectiveness of the current KR&R categorization, and identify directions for future work suggesting how this categorization can be further evolved by community participation.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".