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..
Actively Learning Concepts and Conjunctive Queries under ELr-Ontologies
Authors: Maurice Funk, Jean Christoph Jung, Carsten Lutz
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We consider the problem to learn a concept or a query in the presence of an ontology formulated in the description logic ELr, in Angluin s framework of active learning that allows the learning algorithm to interactively query an oracle (such as a domain expert). We show that the following can be learned in polynomial time: (1) EL-concepts, (2) symmetry-free ELI-concepts, and (3) conjunctive queries (CQs) that are chordal, symmetry-free, and of bounded arity. and Our main results are that the following can be learned in polynomial time under ELr-ontologies: (1) EL-concepts, (2) ELI-concepts that are symmetry-free, and (3) CQs that are chordal, symmetry-free, and of bounded arity. |
| Researcher Affiliation | Academia | Maurice Funk1 , Jean Christoph Jung2 and Carsten Lutz1 1University of Bremen 2University of Hildesheim EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Learning queries q T from ELQ / ELIQsf / CQcsf w under an ELr-ontology O. procedure LEARNCQ q H( x) := refine(q ( x0)) while q H O q T (equivalence query) do Let A, a be the positive counterexample returned and let q H( x ) be C3 Aq H ,O C3 A,O viewed as a CQ with answer variables x = x a q H( x) := refine(q H( x )) return q H( x) |
| Open Source Code | No | The paper does not provide any information about open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not describe empirical experiments involving dataset training. It mentions benchmarks (Fishmark, LUBM, NPD) for analyzing query characteristics, not for model training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments. Therefore, there are no dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and focuses on learnability in polynomial time. It does not describe any computational experiments or the specific hardware used to conduct them. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers used for implementation or experimentation. |
| Experiment Setup | No | The paper is theoretical and does not describe specific experimental setups, hyperparameters, or training configurations. |