Role Forgetting for ALCOQH(universal role)-Ontologies Using an Ackermann-Based Approach
Authors: Yizheng Zhao, Renate A. Schmidt
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 {yizheng.zhao, renate.schmidt}@manchester.ac.uk |
| 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. |