Nemo: Your Friendly and Versatile Rule Reasoning Toolkit

We present Nemo, a toolkit for rule-based reasoning and data processing that emphasises robustness and ease of use. Nemo’s core is a scalable and efficient main-memory reasoner that supports an expressive extension of Datalog with support for datatypes, existential rules, aggregates, and (stratified) negation. Built around this core is a versatile system of libraries and applications for interfacing with several data formats and programming languages, use as a progressive web application, and IDE integration. In this system description, we present this toolkit and discuss relevant application areas in rule-based knowledge representation, knowledge graph processing, and reasoner prototyping. Our evaluation on a range of tasks from these areas demonstrates Nemo’s robust performance in comparison to state-of-the-art rule engines.

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Publication Details

Published
2024-10-26
DOI
https://doi.org/10.24963/kr.2024/70
Citations
1
Primary Topic
Artificial Intelligence in Games
Type
article
Field-Weighted Citation Impact
0.35

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article

Nemo: Your Friendly and Versatile Rule Reasoning Toolkit

Alex Ivliev, Markus Krötzsch, Lukas Gerlach, Simon Meusel et al.
1 citations
Artificial Intelligence in Games
0.35
article

Nemo: Your Friendly and Versatile Rule Reasoning Toolkit

Alex Ivliev, Markus Krötzsch, Lukas Gerlach, Simon Meusel, Jakob Steinberg
article en
1 citations

Abstract

We present Nemo, a toolkit for rule-based reasoning and data processing that emphasises robustness and ease of use. Nemo’s core is a scalable and efficient main-memory reasoner that supports an expressive extension of Datalog with support for datatypes, existential rules, aggregates, and (stratified) negation. Built around this core is a versatile system of libraries and applications for interfacing with several data formats and programming languages, use as a progressive web application, and IDE integration. In this system description, we present this toolkit and discuss relevant application areas in rule-based knowledge representation, knowledge graph processing, and reasoner prototyping. Our evaluation on a range of tasks from these areas demonstrates Nemo’s robust performance in comparison to state-of-the-art rule engines.

Technische Universität Dresden (DE)
ITEA, Deutscher Akademischer Austauschdienst, Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung
Openalex Percentile: Top 32%
Artificial Intelligence in Games
0.35
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Nemo: Your Friendly and Versatile Rule Reasoning Toolkit — Alex Ivliev, Markus Krötzsch, et al. · (2024) | TGRS Research Map | TGRS