Datalog+- Ontology Consolidation
Authors: Cristhian Ariel D. Deagustini, Maria Vanina Martinez, Marcelo A. Falappa, Guillermo R. Simari
JAIR 2016 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we introduce the notion of incoherence regarding Datalog ontologies, in terms of satisfiability of sets of constraints, and show how under specific conditions incoherence leads to inconsistent Datalog ontologies. The main contribution of this work is a novel approach to restore both consistency and coherence in Datalog ontologies. The proposed approach is based on kernel contraction and restoration is performed by the application of incision functions that select formulas to delete. Nevertheless, instead of working over minimal incoherent/inconsistent sets encountered in the ontologies, our operators produce incisions over non-minimal structures called clusters. We present a construction for consolidation operators, along with the properties expected to be satisfied by them. Finally, we establish the relation between the construction and the properties by means of a representation theorem. |
| Researcher Affiliation | Academia | Cristhian Ariel D. Deagustini EMAIL Mar ıa Vanina Mart ınez EMAIL Marcelo A. Falappa EMAIL Guillermo R. Simari EMAIL AI R&D Lab., Institute for Computer Science and Engineering (ICIC) Consejo Nacional de Investigaciones Cient ıficas y T ecnicas (CONICET) Universidad Nacional del Sur (UNS), Alem 1253, (B8000CPB) Bah ıa Blanca, Argentina. |
| Pseudocode | No | The paper describes methods through formal definitions, propositions, and theorems, along with illustrative examples. It does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | We are currently working on the implementation of our operators; we plan to study different techniques that can be used in order to produce an efficient implementation, possibly tailored for specific fragments of Datalog. |
| Open Datasets | No | The paper uses illustrative examples (e.g., Example 1, 3, 4, 6, 7, 8, 9, 11, 12) defined within the text to explain the theoretical framework. It does not utilize or provide access information for any publicly available or open datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments on datasets, therefore no dataset splits (training, validation, test) are mentioned. |
| Hardware Specification | No | The paper presents a theoretical framework and uses illustrative examples rather than empirical experiments. As such, no specific hardware used for running experiments is mentioned. |
| Software Dependencies | No | The paper focuses on a theoretical framework for Datalog ontology consolidation. It mentions that 'We are currently working on the implementation of our operators', indicating that no specific software dependencies with version numbers are provided in this paper as the implementation is future work. |
| Experiment Setup | No | The paper is theoretical, introducing a framework for ontology consolidation with illustrative examples. It does not describe any empirical experiments, and thus no details on experimental setup, hyperparameters, or training configurations are provided. |