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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |