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Record W2406241794

Singular Referring Expressions in Conjunctive Query Answers: the case for a CFD DL Dialect.

2015· article· en· W2406241794 on OpenAlex
Alexander Borgida, David Toman, Grant Weddell

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDescription Logics · 2015
Typearticle
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsConjunctive queryComputer scienceContext (archaeology)Noun phraseExpression (computer science)Constant (computer programming)PhraseBase (topology)Theoretical computer scienceNatural language processingInformation retrievalProgramming languageNounMathematicsRelational database
DOInot available

Abstract

fetched live from OpenAlex

A referring expression in linguistics is any noun phrase identifying an object in a way that will be useful to interlocutors. In the context of conjunctive queries over a description logic knowledge base (DL KB), typically constant symbols (usually treated as rigid designators) are used as referring expressions in a certain answer to the query. In this paper, we begin to explore how this can be usefully generalized by allowing more general DL concept descriptions, called singular referring expressions, to replace constants in this role. In particular, we lay the foundation for singular referring expressions in conjunctive query answers over a DL KB using a member of the CFD family of DL dialects. In the process, we introduce a specific language for referring concept types, and present initial results on how conjunctive queries with referring concept types can be efficiently supported.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.724
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.102
GPT teacher head0.311
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it