Revisiting Controlled Query Evaluation in Description Logics

Authors: Domenico Lembo, Riccardo Rosati, Domenico Fabio Savo

IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We study data complexity of this problem for ontologies specified in the Description Logics DL-Lite R and EL and for variants of the censor language... Some of the complexity results we provide are indeed obtained through mutual reduction between CQE and CQA.
Researcher Affiliation Academia Domenico Lembo1 , Riccardo Rosati1 and Domenico Fabio Savo2 1Sapienza Universit a di Roma 2Universit a degli Studi di Bergamo {lembo, rosati}@diag.uniroma1.it, domenicofabio.savo@unibg.it
Pseudocode Yes Algorithm 1: Algorithm CQ-Ent-DL-Lite R(E, q) and Algorithm 2: Algorithm CQk-Ent-EL (E, q) are provided.
Open Source Code No The paper does not contain any statements or links indicating that source code for the described methodology is publicly available.
Open Datasets No The paper is theoretical and focuses on description logics and query evaluation frameworks; it does not utilize datasets in the empirical sense common in machine learning or data analysis papers. Therefore, it does not provide information about publicly available training datasets.
Dataset Splits No The paper is theoretical and focuses on description logics and query evaluation frameworks; it does not utilize datasets in the empirical sense common in machine learning or data analysis papers. Therefore, it does not provide information about validation splits.
Hardware Specification No As a theoretical paper, it does not describe specific experimental setups or hardware used for computation.
Software Dependencies No As a theoretical paper, it describes algorithms and complexity, but does not specify any software dependencies with version numbers that would be required for empirical replication.
Experiment Setup No As a theoretical paper, it does not detail any experimental setup, hyperparameters, or system-level training settings.