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Record W1604005737

Identifying Objects Over Time with Description Logics.

2008· article· en· W1604005737 on OpenAlexaff
David Toman, Grant Weddell

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Database Systems and Queries
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceTheoretical computer sciencePath (computing)Function (biology)Reduction (mathematics)Temporal logicBoolean functionProgramming languageAlgorithmMathematics
DOInot available

Abstract

fetched live from OpenAlex

A fundamental requirement for cooperating agents is to agree on a selection of component values of objects that can be used for reliably communicating references to the objects, that is, to function as their keys. In distributed environments such as the web, it is more likely that a choice of such values may have time limits on the duration of their ability to serve as keys, e.g., values denoting permissions, authorizations, ser-vice codes, mobile addresses and so on. In this paper, we con-sider how a Boolean complete description logic with a con-cept constructor for expressing “always ” can also be em-bellished with a concept constructor for dynamic or tempo-ral forms of equality generating constraints we call temporal path functional dependencies. In particular, we introduce the temporal description logic DLFDtemp, demonstrate how it can be used, among other things, to capture and reason about temporal keys and functional dependencies for a hypotheti-cal distributed hospital database, and prove that the general membership problem for DLFDtemp is EXPTIME-complete. The latter is accomplished by exhibiting a reduction of the general membership problem for DLFDtemp to the simpler dialect DLF. We also show that the addition of very sim-ple kinds of eventualities leads to a significant increase in the complexity of the membership problem. 1

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.892
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.032
GPT teacher head0.228
Teacher spread0.196 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

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