Satisfaction and Implication of Integrity Constraints in Ontology-based Data Access
Authors: Charalampos Nikolaou, Bernardo Cuenca Grau, Egor V. Kostylev, Mark Kaminski, Ian Horrocks
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We establish decidability and complexity bounds for all these problems in the case where ontologies are expressed in DL-Lite R and constraints range from functional dependencies to disjunctive tuple-generating dependencies. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, University of Oxford, UK 2Infor, UK |
| Pseudocode | Yes | We then apply the following steps. 1) Do the following for each source dataset D with at most ma |γ| atoms defined over the predicates from M and the constants in γ extended with mv |γ| fresh constants. a) Compute the subset Hd of the minimal Herbrand model H of sk(T ) VM,D involving terms of functional depth at most d, where d is the quantity defined in Lemma 1 for M, T , and γ. b) For each counter-example λ to γ w.r.t. T VM,D involving only terms in Hd do the following. i) Compute the set W of all λ-witnesses q for T and a CQ qi(x) in γ such that |q | |qi| and q uses only target predicates from M, constants from qi(λ(x)), and quantified variables y1, . . . , y2 |qi|. ii) Compute Qλ = unfold M(W q W q ). iii) If Σ D does not finitely entail Qλ, return true. 2) Return false. |
| Open Source Code | No | The paper is theoretical and does not describe an implementation, nor does it provide any links or statements about open-source code for the described methodology. |
| Open Datasets | No | The paper refers to 'datasets' as abstract components of its theoretical framework (e.g., 'A source dataset D'), but it does not mention specific, publicly available datasets used for empirical training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation that would require dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe an implementation or specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experiments or their specific setup details, such as hyperparameter values or training configurations. |