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 [1].
Beyond SPARQL under OWL 2 QL Entailment Regime: Rules to the Rescue
Authors: Georg Gottlob, Andreas Pieris
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we focus on OWL 2 QL and we propose Tri Q-Lite 1.0, a tractable rule-based formalism that supports the above functionalities, and thus it can be used for querying RDF data. Unlike existing composite approaches, our formalism has simple syntax and semantics in the same spirit as good old Datalog.Our technical results can be summarized as follows: We introduce in Section 4 Tri Q-Lite 1.0, which is based on warded Datalog , sg, , and show that it fulfills all desiderata; the technical reasons are given in Table 1. Theorem 3 Query evaluation for Tri Q-Lite 1.0 is PTIME-complete in data complexity. |
| Researcher Affiliation | Academia | Georg Gottlob1 Andreas Pieris2 1Department of Computer Science, University of Oxford, UK 2Institute of Information Systems, Vienna University of Technology, Austria EMAIL, EMAIL |
| Pseudocode | No | An abstract description of our algorithm follows. Proof(a, D, Π) starts from a, and applies appropriate resolution steps until the database D is reached. |
| Open Source Code | No | The paper does not mention providing open-source code for the methodology it describes. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets, thus it does not provide concrete access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation; therefore, no training/test/validation dataset splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific details such as hyperparameter values or training configurations. |