A Model for Learning Description Logic Ontologies Based on Exact Learning
Authors: Boris Konev, Ana Ozaki, Frank Wolter
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We investigate the problem of learning description logic (DL) ontologies in Angluin et al. s framework of exact learning via queries posed to an oracle. We study polynomial learnability of ontologies in L using data retrieval queries in Q and provide an almost complete classification for DLs that are fragments of EL with role inclusions and of DL-Lite and for data retrieval queries that range from atomic queries and EL/ELI-instance queries to conjunctive queries. Some results are proved by non-trivial reductions to learning from subsumption examples. |
| Researcher Affiliation | Academia | Boris Konev University of Liverpool United Kingdom Ana Ozaki University of Liverpool United Kingdom Frank Wolter University of Liverpool United Kingdom |
| Pseudocode | Yes | Algorithm 1: Reducing a positive counterexample; Algorithm 2: Minimizing an ABox A; Algorithm 3: Computing a tree shaped ABox |
| Open Source Code | No | The paper does not provide any statements or links indicating that open-source code for the described methodology is available. |
| Open Datasets | No | The paper is theoretical and does not describe experiments involving training on datasets. It refers to 'data retrieval examples' within a learning framework but not specific public datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies or version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |