Using Description Logics for RDF Constraint Checking and Closed-World Recognition
Authors: Peter Patel-Schneider
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Further this constraint checking can be implemented as SPARQL querying and thus effectively performed. As shown in this paper, Description Logics can be used to provide the necessary framework for both checking constraints and providing closed-world recognition facilities, and thus cover most of what SPIN and Sh Ex provide. Indeed Stardog ICV is an implementation of the approach described in this paper showing how the approach to constraints here can be implemented by a translation to SPARQL. Example Here is a small example of how Description Logic constructs can be used for constraint checking. |
| Researcher Affiliation | Industry | Peter F. Patel-Schneider Nuance Communications 1198 East Arques Avenue, Sunnyvale, California, U. S A. pfpschneider@gmail.com |
| Pseudocode | No | The paper describes its approach conceptually and provides examples, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions external implementations and code repositories related to the approach (e.g., 'Recent work at Mannheim by Thomas Bosch (see https://github.com/boschthomas/OWL2-SPIN-Mapping) translates OWL descriptions interpreted as constraints into SPARQL using a similar approach'), but does not state that the authors themselves provide open-source code for the methodology described in this paper. |
| Open Datasets | No | The paper includes 'RDF triples in Figure 1 provide the data for the example' which is presented directly within the paper. It does not provide concrete access information (link, DOI, repository, or citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper uses an illustrative example dataset presented directly in a figure and does not provide specific dataset split information (percentages, sample counts, or predefined splits) for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact CPU/GPU models, memory, or detailed computer specifications) used for running its examples or any computational processes. |
| Software Dependencies | No | The paper mentions the use of SPARQL queries and refers to systems like Stardog ICV and OWL2-SPIN-Mapping, but does not provide specific ancillary software details with version numbers (e.g., 'Python 3.8, PyTorch 1.9, and CUDA 11.1') needed to replicate the experiment. |
| Experiment Setup | No | The paper describes a theoretical framework and provides an example for constraint checking, but it does not contain specific experimental setup details such as hyperparameter values, optimizer settings, or training configurations. |