Actively Learning Concepts and Conjunctive Queries under ELr-Ontologies
Authors: Maurice Funk, Jean Christoph Jung, Carsten Lutz
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | 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 mfunk@uni-bremen.de, jungj@uni-hildesheim.de, clu@uni-bremen.de |
| 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. |