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.
Mapping Repair in Ontology-based Data Access Evolving Systems
Authors: Domenico Lembo, Riccardo Rosati, Valerio Santarelli, Domenico Fabio Savo, Evgenij Thorstensen
IJCAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 17,577 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,810 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 then present a set of results on the complexity of query answering in the above framework, when the ontology is expressed in DL-Lite R. In these settings we provide a number of complexity results on the entailment of conjunctive queries. |
| Researcher Affiliation | Academia | 1 Sapienza Universit a di Roma 2 University of Oslo |
| Pseudocode | Yes | Algorithm 1: Check DMR-QE (M, T , S , D, q) |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about the release of open-source code for the described methodology. The paper is theoretical in nature. |
| Open Datasets | No | The paper presents theoretical concepts and complexity analysis, and does not refer to the use of any specific publicly available or open dataset for training. |
| Dataset Splits | No | As the paper focuses on theoretical analysis and does not describe empirical experiments, no training/test/validation dataset splits are provided. |
| Hardware Specification | No | The paper is theoretical in nature and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper focuses on theoretical analysis and does not provide any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes theoretical frameworks and algorithms, but does not provide details on experimental setup, hyperparameters, or training configurations. |