Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Model for Learning Description Logic Ontologies Based on Exact Learning
Authors: Boris Konev, Ana Ozaki, Frank Wolter
AAAI 2016 | Venue PDF | 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. |