# Combining Event Calculus and Description Logic Reasoning via Logic Programming

arXiv: https://arxiv.org/abs/2109.04803 · HTML: https://mlsanity.com/paper/2109.04803/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2021.

## Built on by (most cited first, 4 of 4)

- [Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective](https://mlsanity.com/paper/2210.01344/) (2022)
- [Bottom-Up Grounding in the Probabilistic Logic Programming System Fusemate](https://mlsanity.com/paper/2305.18924/) (2023)
- [Efficient Temporal Datalog Materialisation for Composite Event Recognition](https://mlsanity.com/paper/2605.02488/) (2026)
- [LLMs for Executable Multi-Agent System Specification Generation](https://mlsanity.com/paper/2609.34619/) (2026)

## Abstract (arXiv)

The paper introduces a knowledge representation language that combines the event calculus with description logic in a logic programming framework. The purpose is to provide the user with an expressive language for modelling and analysing systems that evolve over time. The approach is exemplified with the logic programming language as implemented in the Fusemate system. The paper extends Fusemate's rule language with a weakly DL-safe interface to the description logic cal ALCIF and adapts the event calculus to this extended language. This way, time-stamped ABoxes can be manipulated as fluents in the event calculus. All that is done in the frame of Fusemate's concept of stratification by time. The paper provides conditions for soundness and completeness where appropriate. Using an elaborated example it demonstrates the interplay of the event calculus, description logic and logic programming rules for computing possible models as plausible explanations of the current state of the modelled system.