On the Power and Limitations of Examples for Description Logic Concepts
Authors: Balder ten Cate, Raoul Koudijs, Ana Ozaki
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We investigate the power of labeled examples for describing description-logic concepts. Specifically, we systematically study the existence and efficient computability of finite characterisations, i.e., finite sets of labeled examples that uniquely characterize a single concept, for a wide variety of description logics between EL and ALCQI,both without an ontology and in the presence of a DL-Lite ontology. Finite characterisations are relevant for debugging purposes, and their existence is a necessary condition for exact learnability with membership queries. |
| Researcher Affiliation | Academia | Balder ten Cate1 , Raoul Koudijs2 , Ana Ozaki2,3 1 Institute for Logic, Language and Computation (ILLC), University of Amsterdam 2University of Bergen 3University of Oslo |
| Pseudocode | No | The paper describes algorithms conceptually, such as a 'polynomial-time algorithm' for testing subsumption, but does not present them in a structured pseudocode format or a labeled 'Algorithm' block. |
| Open Source Code | No | The paper does not provide information about open-source code availability for the described methodology. |
| Open Datasets | No | This is a theoretical paper and does not involve the use of datasets for training or empirical evaluation. |
| Dataset Splits | No | As this is a theoretical paper, there are no dataset splits for training, validation, or testing mentioned. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details, hyperparameters, or training configurations. |