Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
On the Expressivity of ASK Queries in SPARQL
Authors: Xiaowang Zhang, Jan Van den Bussche, Kewen Wang, Heng Zhang, Xuanxing Yang, Zhiyong Feng3057-3064
AAAI 2020 | Venue PDF | LLM Run Details | Input Tokens: 17,384 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,016 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper is a first systematic study of the relative expressive power of various fragments of ASK queries in SPARQL. |
| Researcher Affiliation | Academia | 1College of Intelligence and Computing, Tianjin University, Tianjin, China 2Faculty of Sciences, Hasselt University, Hasselt, Belgium 3School of Information and Communication Technology, Griffith University, Brisbane, Australia 4Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, China |
| Pseudocode | No | The paper does not contain any sections explicitly labeled as 'Pseudocode' or 'Algorithm', nor are there structured algorithm blocks. |
| Open Source Code | No | The paper does not provide any information about the availability of open-source code for the described methodology. |
| Open Datasets | No | This paper is theoretical and does not involve the use of datasets for training or evaluation. |
| Dataset Splits | No | This paper is theoretical and does not involve the use of datasets, thus no validation split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe computational experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers required for replication. |
| Experiment Setup | No | The paper is theoretical and does not involve empirical experiments, therefore no experimental setup details like hyperparameters are provided. |