An Event-Driven Approach for Querying Graph-Structured Data Using Natural Language.
Bibliographic record
Abstract
An ideal way for people to query graph-based knowledge, including triplestores in the semantic web, would be for them to ask questions in a natural language (NL). However, existing NL query interfaces to graph-based data have limited expressive power and cannot accommodate arbitrarilynested quantification (i.e. phrases such as “a gangster who joined every gang”) together with multiple complex prepositional phrases, such as “in a city located in Illinois in 1918 using a set of keys that was stolen from a gangster”. It would appear that the commonly-used “entity-based ” triplestores, together with what has become the de-facto approach of converting NL queries to SPARQL queries before being evaluated, hinders the development of expressive NL query processors. The reason is that entity-based triples are not conducive to the development of semantic theories of complex prepositional phrases, and the development of such theories is made considerably more complex when translation to SPARQL has to be taken into account. An alternative approach, which uses “event-based ” triplestores, treats (bracketed) English queries as expressions of the lambda calculus which can be evaluated directly with respect to the triplestore. This approach facilitates the development of a formal denotational semantics of English queries which easily accommodates complex prepositional phrases. The approach described here could be used to develop a denotational semantics for a highly-expressive NL query language, and then that semantics could be used to guide the design of an NL query to SPARQL translator, thereby taking advantage of SPARQL optimizations.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".