Restricted Chase (Non)Termination for Existential Rules with Disjunctions
Authors: David Carral, Irina Dragoste, Markus Krötzsch
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world ontologies show that our acyclicity notions improve significantly over known criteria. |
| Researcher Affiliation | Academia | David Carral, Irina Dragoste, Markus Kr otzsch Center for Advancing Electronics Dresden (cfaed), TU Dresden, Germany david.carral@tu-dresden.de, irina.dragoste@tu-dresden.de, markus.kroeztsch@tu-dresden.de |
| Pseudocode | No | The paper describes algorithms and processes textually but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states: 'We have implemented tests for RMSA, RMFA, RMFC, MSA, and MFA using RDFox [Motik et al., 2014] as a rule engine.' This refers to a third-party tool and does not provide a link or explicit statement about releasing the authors' own implementation code. |
| Open Datasets | Yes | To evaluate the effectiveness of our criteria, we have used MOWLCorp, a large corpus of real-world OWL ontologies [Matentzoglu and Parsia, 2014; Matentzoglu et al., 2013], which we transformed into rules. |
| Dataset Splits | No | The paper mentions using a corpus of ontologies but does not specify any training, validation, or test splits for these ontologies in the context of their experiments. It rather classifies existing ontologies based on their properties. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'RDFox [Motik et al., 2014] as a rule engine' and 'HermiT [Motik et al., 2009]' but does not provide specific version numbers for these or any other software libraries or dependencies used in their implementation. |
| Experiment Setup | No | The paper describes the theoretical definitions and criteria for chase termination, but it does not provide specific experimental setup details like hyperparameters, learning rates, or other system-level training configurations, as its experiments are not based on training machine learning models. |