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Record W2141047949 · doi:10.1109/dexa.2008.131

ECQ: A Simple Query Language for the Semantic Web

2008· article· en· W2141047949 on OpenAlexfundno aff
María del Mar Roldán‐García, Joaquin J. Molina-Castro, José F. Aldana‐Montes

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSemantic Web and Ontologies
Canadian institutionsnot available
FundersRyerson University
KeywordsComputer scienceSemantic reasonerConjunctive queryDescription logicSemantic WebScalabilitySimple (philosophy)Semantic Web Rule LanguageOntologySyntaxWeb Ontology LanguageQuery languageInformation retrievalOntology languageProgramming languageWorld Wide WebNatural language processingSemantic Web StackArtificial intelligenceSemantic analyticsDatabase

Abstract

fetched live from OpenAlex

With the increasing development of real applications using semantic Web technologies, it is necessary to provide scalable and efficient ontology querying and reasoning systems. In this paper we present ECQ (Extended Conjunctive Queries), a simple but powerful query language for OWL. ECQ supports Tbox reasoning and complex queries, going beyond conjunctive queries, which are needed for real semantic Web applications. The syntax of ECQ is the same as other description logic languages, making it easy to use for this community of researchers. We also present an implementation of the language over a scalable and persistent OWL reasoner, that is called DBOWL. Therefore, queries can use Abox reasoning to obtain more complete results.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.821
Threshold uncertainty score0.166

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 communication0.0000.000
Open science0.0010.000
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 head0.030
GPT teacher head0.267
Teacher spread0.237 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations9
Published2008
Admission routes1
Has abstractyes

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