Learning Query Inseparable εℒℋ Ontologies
Authors: Ana Ozaki, Cosimo Persia, Andrea Mazzullo2959-2966
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We investigate the complexity of learning query inseparable ELH ontologies in a variant of Angluin s exact learning model. Given a fixed data instance A and a query language Q, we are interested in computing an ontology H that entails the same queries as a target ontology T on A , that is, H and T are inseparable w.r.t. A and Q. The learner is allowed to pose two kinds of questions. The first is Does (T , A) |= q?, with A an arbitrary data instance and q and query in Q. An oracle replies this question with yes or no . In the second, the learner asks Are H and T inseparable w.r.t. A and Q?. If so, the learning process finishes, otherwise, the learner receives (A , q) with q Q, (T , A ) |= q and (H, A ) |= q (or vice-versa). Then, we analyse conditions in which query inseparability is preserved if A changes. Finally, we consider the PAC learning model and a setting where the algorithms learn from a batch of classified data, limiting interactions with the oracles. |
| Researcher Affiliation | Academia | Ana Ozaki, Cosimo Persia, Andrea Mazzullo KRDB Research Centre, Free University of Bozen-Bolzano |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. It describes algorithms conceptually but does not present their steps in a structured format. |
| Open Source Code | No | The paper does not provide a link or explicit statement about releasing source code for the methodology described in this paper. It mentions that 'Omitted proofs are available at https://arxiv.org/abs/1911.07229' which is not code. |
| Open Datasets | No | The paper does not mention using any specific publicly available datasets for empirical training or evaluation. The concept of 'ABox' and 'examples' are used in a theoretical sense within the learning model. |
| Dataset Splits | No | The paper does not specify any dataset splits (e.g., training, validation, test percentages or counts) as it is a theoretical work and does not involve empirical data splitting. |
| Hardware Specification | No | The paper is theoretical and does not involve empirical experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not involve empirical experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on complexity analysis of learning models, thus it does not provide any experimental setup details such as hyperparameters or training configurations. |