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].
Stream Reasoning in Temporal Datalog
Authors: Alessandro Ronca, Mark Kaminski, Bernardo Cuenca Grau, Boris Motik, Ian Horrocks
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we propose novel reasoning problems to deal with these challenges, and study their computational properties on Datalog extended with a temporal sort and the successor function a core rule-based language for stream reasoning applications. The proofs of all results are given in an extended version of this paper (Ronca et al. 2017). |
| Researcher Affiliation | Academia | Alessandro Ronca, Mark Kaminski, Bernardo Cuenca Grau, Boris Motik, Ian Horrocks Department of Computer Science, University of Oxford, UK EMAIL |
| Pseudocode | Yes | Algorithm 1: Online Stream Reasoning Algorithm; Algorithm 2: Ofο¬ine Stream Reasoning Algorithm |
| Open Source Code | No | The paper does not mention any open-source code release for the described methodology. It is a theoretical paper focused on computational properties. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies that would involve datasets for training. No public dataset information is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits. No information on validation splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware specifications. Therefore, no hardware details are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe any empirical implementation that would list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not involve empirical experiments. Therefore, no experimental setup details, hyperparameters, or training configurations are provided. |