Monotone Abstractions in Ontology-Based Data Management
Authors: Gianluca Cima, Marco Console, Maurizio Lenzerini, Antonella Poggi5556-5563
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We define a monotone query language based on disjunctive Datalog enriched with an epistemic operator, and show that its expressive power suffices for expressing the best approximations of monotone abstractions of UCQs. We provide the following contributions. 1. We present a general framework for abstraction in OBDM, based on the definition of queries as functions from the logical models of OBDM systems to sets of tuples. ... 3. We consider a scenario where the OBDM specification J is based on DL-Lite RDFS ... and present algorithms that, given a source query q S expressed as UCQ, compute the best (sound or complete) approximations of the J-abstraction of q S in the class of monotone queries, expressed in Datalog K. 4. The above algorithms provide the proof that, in the considered scenario, for any UCQ q S, the best (sound or complete) approximations of the J-abstraction of q S in the class of monotone queries always exists. |
| Researcher Affiliation | Academia | Gianluca Cima1, Marco Console2, Maurizio Lenzerini2, Antonella Poggi2 1CNRS & University of Bordeaux 2Sapienza University of Rome gianluca.cima@u-bordeaux.fr, {console, lenzerini, poggi}@diag.uniuniroma1.it |
| Pseudocode | Yes | Algorithm 1: Inv Map Input: OBDM specification , S, M Output: Set R of Datalog K rules |
| Open Source Code | No | No explicit statement about providing open-source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper focuses on theoretical contributions and does not use any public or private datasets for experimentation. |
| Dataset Splits | No | The paper describes theoretical contributions and does not mention any training, validation, or test dataset splits. |
| Hardware Specification | No | No specific hardware specifications (e.g., GPU/CPU models, memory) are mentioned in the paper, consistent with its theoretical nature. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers (e.g., programming languages, libraries, or solvers with versions) used for implementation or experimentation. |
| Experiment Setup | No | As the paper focuses on theoretical contributions, it does not describe any experimental setup details such as hyperparameters or training configurations. |