Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
The Bag Semantics of Ontology-Based Data Access
Authors: Charalampos Nikolaou, Egor V. Kostylev, George Konstantinidis, Mark Kaminski, Bernardo Cuenca Grau, Ian Horrocks
IJCAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 18,130 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,445 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 show that, in contrast to the set case, ontologies may not have a universal model (i.e., a single model over which all CQs can be correctly evaluated), and bag query answering becomes CONP-hard in data complexity even if we restrict ourselves to DL-Litebag core ontologies. 3. To regain tractability, we study the class of rooted CQs [Bienvenu et al., 2012]... We show that rooted CQs over DL-Litebag core ontologies not only admit a universal model and enjoy favourable computational properties, but also allow for rewritings that can be directly evaluated over the bag ABox of the ontology. |
| Researcher Affiliation | Academia | Charalampos Nikolaou, Egor V. Kostylev, George Konstantinidis, Mark Kaminski, Bernardo Cuenca Grau, and Ian Horrocks Department of Computer Science, University of Oxford, UK |
| Pseudocode | No | The paper includes formal definitions, theorems, and proofs but no structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any information or links to open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve empirical training on datasets. No information about publicly available datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets. No information about dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments, thus no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup involving hyperparameters or training configurations. |