Simon Meusel

dblp:355/5435 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Theory of computation · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Knowledge representation and reasoning · 100%
Databases, data mining, and information retrieval
1 paper
Data integration and cleaning · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning › logic programming
datalog
0.812024
Nemo: Your Friendly and Versatile Rule Reasoning Toolkit · KR 2024
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge-based systems › rule-based systems
rule-based reasoning
0.812024
Nemo: Your Friendly and Versatile Rule Reasoning Toolkit · KR 2024
YearPublicationVenuePosition
2024 Nemo: Your Friendly and Versatile Rule Reasoning Toolkit
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.
Alex Ivliev, Lukas Gerlach 0002, Simon Meusel, Jakob Steinberg, Markus Krötzsch
KR3