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..
Role Forgetting for ALCOQH(universal role)-Ontologies Using an Ackermann-Based Approach
Authors: Yizheng Zhao, Renate A. Schmidt
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Despite our method not being complete, performance results of an evaluation with a prototypical implementation have shown very good success rates on real-world ontologies. To gain insight into the practical applicability of the method, we implemented a prototype in Java using the OWL-API,2 and evaluated it on two corpora of slightly adjusted real-world ontologies from the NCBO Bio Portal repository. |
| Researcher Affiliation | Academia | Yizheng Zhao and Renate A. Schmidt School of Computer Science, The University of Manchester, UK EMAIL |
| Pseudocode | Yes | Figure 1: Ackermann rules for eliminating r NR from a set of clauses in reduced form. In the rules we assume that r sig R(N). |
| Open Source Code | No | The paper states: 'we implemented a prototype in Java using the OWL-API', but does not provide any link or explicit statement that the source code for their method is open-source or publicly available. |
| Open Datasets | Yes | To gain insight into the practical applicability of the method, we implemented a prototype in Java using the OWL-API,2 and evaluated it on two corpora of slightly adjusted real-world ontologies from the NCBO Bio Portal repository.3 |
| Dataset Splits | No | The paper describes running experiments on 'real-world ontologies' and forgetting symbols from them, but as it's not a machine learning task with model training, it does not mention traditional training/validation/test dataset splits for model reproduction. It mentions forgetting 30% and 70% of role symbols, which is about the input to the forgetting process, not data splitting for training a model. |
| Hardware Specification | Yes | The experiments were run on a desktop computer with an Intel Core TM i7-4790 processor, four cores running at up to 3.60 GHz and 8 GB of DDR3-1600 MHz RAM. |
| Software Dependencies | No | The paper states: 'implemented a prototype in Java using the OWL-API', but does not specify version numbers for Java or the OWL-API. |
| Experiment Setup | No | The paper describes a 'timeout of 100 seconds' for each run and that 'symbols to be forgotten were randomly chosen', but it does not specify hyperparameters or other system-level training settings as the task is not a machine learning training task. |