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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Ontology-Mediated Query Answering over Log-Linear Probabilistic Data
Authors: Stefan Borgwardt, İsmail İlkan Ceylan, Thomas Lukasiewicz2711-2718
AAAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 18,133 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,798 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose a new data model that integrates the paradigm of ontology-mediated query answering with probabilistic databases, employing a log-linear probability model. We compare our approach to existing proposals, and provide supporting computational results. and We obtain a host of complexity results. |
| Researcher Affiliation | Academia | Stefan Borgwardt Faculty of Computer Science Technische Universit at Dresden, Germany EMAIL Ismail Ilkan Ceylan, Thomas Lukasiewicz Department of Computer Science University of Oxford, UK EMAIL EMAIL |
| Pseudocode | No | The paper describes theoretical models and reductions but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | We leave as future work an implementation, combining existing gradient-based optimization methods with efficient rewriting techniques and PDB or MLN inference engines. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical experiments with a training dataset. It references existing probabilistic knowledge bases as sources for data for a theoretical learning approach, but not for empirical training. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments, therefore no training/validation/test splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental hardware specifications. |
| Software Dependencies | No | The paper is theoretical and explicitly states that implementation is future work, therefore no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details or hyperparameters. |