# Unique Characterisability and Learnability of Temporal Queries Mediated by an Ontology

arXiv: https://arxiv.org/abs/2306.07662 · HTML: https://mlsanity.com/paper/2306.07662/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 74 references that are in the map.
- First posted on arXiv 13 Jun 2023.

## Builds on (most cited first, 16 of 16)

- [Learning Linear Temporal Properties](https://mlsanity.com/paper/1806.03953/) (2018)
- [The DL-Lite Family and Relations](https://mlsanity.com/paper/1401.3487/) (2014)
- [Ontology-based Data Access: A Study through Disjunctive Datalog, CSP, and MMSNP](https://mlsanity.com/paper/1301.6479/) (2013)
- [The Complexity of Learning Linear Temporal Formulas from Examples](https://mlsanity.com/paper/2102.00876/) (2021)
- [Learning Description Logic Ontologies. Five Approaches. Where Do They Stand?](https://mlsanity.com/paper/2104.01193/) (2021)
- [Exact Learning of Lightweight Description Logic Ontologies](https://mlsanity.com/paper/1709.07314/) (2017)
- [Logical Separability of Labeled Data Examples under Ontologies](https://mlsanity.com/paper/2007.01610/) (2020)
- [The complexity of reverse engineering problems for conjunctive queries](https://mlsanity.com/paper/1606.01206/) (2016)
- [Conjunctive Queries: Unique Characterizations and Exact Learnability](https://mlsanity.com/paper/2008.06824/) (2020)
- [SAT-Based PAC Learning of Description Logic Concepts](https://mlsanity.com/paper/2305.08511/) (2023)
- [First-Order Rewritability and Complexity of Two-Dimensional Temporal Ontology-Mediated Queries](https://mlsanity.com/paper/2111.06806/) (2021)
- [First Order-Rewritability and Containment of Conjunctive Queries in Horn Description Logics](https://mlsanity.com/paper/2011.09836/) (2020)
- [Actively Learning Concepts and Conjunctive Queries under ELr-Ontologies](https://mlsanity.com/paper/2105.08326/) (2021)
- [Frontiers and Exact Learning of ELI Queries under DL-Lite Ontologies](https://mlsanity.com/paper/2204.14172/) (2022)
- [Unique Characterisability and Learnability of Temporal Instance Queries](https://mlsanity.com/paper/2205.01651/) (2022)
- [Reverse Engineering of Temporal Queries Mediated by LTL Ontologies](https://mlsanity.com/paper/2305.01248/) (2023)

## Abstract (arXiv)

Algorithms for learning database queries from examples and unique characterisations of queries by examples are prominent starting points for developing automated support for query construction and explanation. We investigate how far recent results and techniques on learning and unique characterisations of atemporal queries mediated by an ontology can be extended to temporal data and queries. Based on a systematic review of the relevant approaches in the atemporal case, we obtain general transfer results identifying conditions under which temporal queries composed of atemporal ones are (polynomially) learnable and uniquely characterisable.