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..
On the Power and Limitations of Examples for Description Logic Concepts
Authors: Balder ten Cate, Raoul Koudijs, Ana Ozaki
IJCAI 2024 | Venue PDF | 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. |