Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Capturing Relational Schemas and Functional Dependencies in RDFS
Authors: Diego Calvanese, Wolfgang Fischl, Reinhard Pichler, Emanuel Sallinger, Mantas Simkus
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We thus introduce expressive identification constraints to capture functional dependencies and define an RDF Normal Form, which precisely captures the classical Boyce-Codd Normal Form of relational schemas. and We now show that RNF captures BCNF in RGs using the relational to RDF graph direct mapping. Theorem 4. Let R be a relation symbol and Σ a set of fds over R. Then (R, Σ) is in BCNF iff rdm(R, Σ) is in RNF. and Algorithm 1: CHECKRNF... Theorem 5. The algorithm CHECKRNF in Algorithm 1 is a decision procedure for RNF. Theorem 6. Deciding whether T is in RNF is feasible in polynomial time. |
| Researcher Affiliation | Academia | Diego Calvanese KRDB Research Centre Free Univ. of Bozen-Bolzano, Italy EMAIL Wolfgang Fischl, Reinhard Pichler, Emanuel Sallinger, Mantas ˇSimkus Institute of Information Systems Vienna Univ. of Technology, Austria EMAIL |
| Pseudocode | Yes | Algorithm 1: CHECKRNF |
| Open Source Code | No | The paper does not mention releasing open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use or provide information about publicly available datasets for experimental evaluation. |
| Dataset Splits | No | The paper does not provide specific dataset split information (training, validation, test) as it is a theoretical work. |
| Hardware Specification | No | The paper is theoretical and does not provide specific hardware details for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details, hyperparameters, or training configurations. |