Counting Query Answers over a DL-Lite Knowledge Base
Authors: Diego Calvanese, Julien Corman, Davide Lanti, Simon Razniewski
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Finally, it must be emphasized that this work is mostly theoretical, and does not deliver a practical algorithm for query answering under count semantics over DL-Lite KBs. |
| Researcher Affiliation | Academia | Free University of Bozen-Bolzano, Italy 2 Umeå University, Sweden 3 Max-Planck-Institut für Informatik, Germany |
| Pseudocode | No | The paper describes the 'Perfect Refcnt' algorithm and its rules (Atom Rewrite, Reduce, GE훼, GE훽) conceptually and with examples, but it does not present them in a structured pseudocode block or a clearly labeled algorithm section. |
| Open Source Code | No | The paper does not provide any information regarding the availability of open-source code for the described methodology. There are no links to repositories or statements about code release. |
| Open Datasets | No | The paper is theoretical and does not use or reference any empirical datasets for training or evaluation. The examples used (e.g., Example 1, 2, 3) are theoretical constructions to illustrate concepts or algorithms. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data, thus no dataset splits for training, validation, or testing are mentioned. |
| Hardware Specification | No | The paper is theoretical and focuses on complexity analysis and algorithm design. It does not describe any empirical experiments or the hardware used to run them. |
| Software Dependencies | No | The paper is theoretical and focuses on logic, complexity, and algorithms. It does not describe any empirical experiments or list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and describes algorithms and complexity results. It does not include an empirical experimental setup with details such as hyperparameters or training configurations. |