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Alex Ivliev

dblp:324/8517 · DBLP profile ↗
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4ranked-venue papers
2as first author
4since 2021 · last 2026
0000-0002-1604-6308ORCID · corroborated

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

Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021Databases, data management, data science and information retrieval · 2 · 1 first-author · 2 since 2021Theory of computation · 2 · 1 first-author · 2 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
2026 SPARQLing Datalog for Rule-Based Reasoning over Large Knowledge Graphs
Alex Ivliev, Markus Krötzsch, Maximilian Marx 0001
ESWC (1)1
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
KR1
2024 Towards Mass Spectrum Analysis with ASP
Nils Küchenmeister, Alex Ivliev, Markus Krötzsch
LPNMR2
2022 Efficient Dependency Analysis for Rule-Based Ontologies
Larry González, Alex Ivliev, Markus Krötzsch, Stephan Mennicke
ISWC2