# Seminaive Materialisation in DatalogMTL

arXiv: https://arxiv.org/abs/2208.07100 · HTML: https://mlsanity.com/paper/2208.07100/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 15 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [The Vadalog System: Datalog-based Reasoning for Knowledge Graphs](https://mlsanity.com/paper/1807.08709/) (2018)
- [Querying Log Data with Metric Temporal Logic (Technical Report)](https://mlsanity.com/paper/1703.08982/) (2017)
- [MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators](https://mlsanity.com/paper/2201.04596/) (2022)
- [Data Complexity and Rewritability of Ontology-Mediated Queries in Metric Temporal Logic under the Event-Based Semantics (Full Version)](https://mlsanity.com/paper/1905.12990/) (2019)

## Abstract (arXiv)

DatalogMTL is an extension of Datalog with metric temporal operators that has found applications in temporal ontology-based data access and query answering, as well as in stream reasoning. Practical algorithms for DatalogMTL are reliant on materialisation-based reasoning, where temporal facts are derived in a forward chaining manner in successive rounds of rule applications. Current materialisation-based procedures are, however, based on a naive evaluation strategy, where the main source of inefficiency stems from redundant computations. In this paper, we propose a materialisation-based procedure which, analogously to the classical seminaive algorithm in Datalog, aims at minimising redundant computation by ensuring that each temporal rule instance is considered at most once during the execution of the algorithm. Our experiments show that our optimised seminaive strategy for DatalogMTL is able to significantly reduce materialisation times.