On Concept Forgetting in Description Logics with Qualified Number Restrictions
Authors: Yizheng Zhao, Renate Schmidt
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This paper presents a practical method for computing solutions of concept forgetting in the description logic ALCOQ( , , ), basic ALC extended with nominals, qualified number restrictions, role negation, role conjunction and role disjunction. The method is based on a non-trivial generalisation of Ackermann s Lemma, and attempts to compute either semantic solutions of concept forgetting or uniform interpolants in ALCOQ( , , ). It is so far the only approach to concept forgetting in description logics with number restrictions plus nominals, as well as in description logics with ABoxes. Results of an evaluation with a prototypical implementation have shown that the method was successful in more than 90% of the test cases from a large corpus of biomedical ontologies. |
| Researcher Affiliation | Academia | Yizheng Zhao and Renate A. Schmidt School of Computer Science, The University of Manchester, UK |
| Pseudocode | No | The paper presents rules in Figure 1, labeled 'The Ackermann rule', which describe how the elimination process works. However, these are presented as mathematical rules/cases rather than in a structured pseudocode block or algorithm listing. |
| 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 about making the source code for their method publicly available. |
| Open Datasets | Yes | The corpus was based on a snapshot of the Bio Portal repository taken in March 2017 [Matentzoglu and Parsia, 2017], containing 396 OWL API compatible ontologies. |
| Dataset Splits | No | The paper does not specify distinct training, validation, and test splits for the data used in the evaluation. It mentions running experiments 'on each ontology' from a corpus of biomedical ontologies and randomly selecting concept names to be forgotten for 'test cases', but not a standard train/validation/test split for a model. |
| Hardware Specification | Yes | The experiments were conducted 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 'we implemented a prototype in Java using the OWL API'. However, it does not specify the version numbers for Java or the OWL API, which are necessary for reproducible software dependencies. |
| Experiment Setup | Yes | To fit in with different real-world application scenarios, we evaluated the performance of the prototype for forgetting different numbers of concept names from each test ontology. In particular, we considered respectively the cases of forgetting 10%, 30% and 50% of concept names in the signature of each ontology. The names to be forgotten were randomly selected. ... The experiments were run 100 times on each ontology and we averaged the results in order to verify the accuracy of our findings. A timeout of 1000 seconds was imposed on each run. |