Best Answers over Incomplete Data : Complexity and First-Order Rewritings
Authors: Amélie Gheerbrant, Cristina Sirangelo
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We compare different ways of casting query answering as a decision problem and characterise its complexity for first-order queries, showing significant differences in the behaviour of best and certain answers. We then restrict attention to best answers for unions of conjunctive queries and produce a practical algorithm for finding them based on query rewriting techniques. |
| Researcher Affiliation | Academia | Am elie Gheerbrant and Cristina Sirangelo Universit e de Paris, IRIF, CNRS, F-75013 Paris, France {amelie, cristina}@irif.fr |
| Pseudocode | No | The paper provides formal definitions, lemmas, propositions, and theorems, along with examples of queries and their transformations, but it does not include any blocks explicitly labeled as |
| Open Source Code | No | The paper does not mention the release of any source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use or reference any datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe any dataset splits (training, validation, or testing) for empirical validation. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or system-level training settings. |