Egor V. Kostylev

About Egor V. Kostylev

Egor V. Kostylev, With an exceptional h-index of 24 and a recent h-index of 20 (since 2020), a distinguished researcher at Universitetet i Oslo, specializes in the field of Artificial Intelligence, Knowledge Representation, Databases, Semantic Web.

His recent articles reflect a diverse array of research interests and contributions to the field:

Recurrent Graph Neural Networks and Their Connections to Bisimulation and Logic

Towards predicting future time intervals on Temporal Knowledge Graphs

The stable model semantics of datalog with metric temporal operators

Revisiting Inferential Benchmarks for Knowledge Graph Completion

On the correspondence between monotonic max-sum GNNs and datalog

Inductive Future Time Prediction on Temporal Knowledge Graphs with Interval Time

The complexity and expressive power of limit Datalog

Towards executable knowledge graph translation

Egor V. Kostylev Information

University

Position

Associate Professor

Citations(all)

1611

Citations(since 2020)

1134

Cited By

837

hIndex(all)

24

hIndex(since 2020)

20

i10Index(all)

42

i10Index(since 2020)

35

Email

University Profile Page

Google Scholar

Egor V. Kostylev Skills & Research Interests

Artificial Intelligence

Knowledge Representation

Databases

Semantic Web

Top articles of Egor V. Kostylev

Recurrent Graph Neural Networks and Their Connections to Bisimulation and Logic

Proceedings of the AAAI Conference on Artificial Intelligence

2024/3/24

Towards predicting future time intervals on Temporal Knowledge Graphs

2023/11/5

The stable model semantics of datalog with metric temporal operators

Theory and Practice of Logic Programming

2024/1

Revisiting Inferential Benchmarks for Knowledge Graph Completion

arXiv preprint arXiv:2306.04814

2023/6/7

On the correspondence between monotonic max-sum GNNs and datalog

arXiv preprint arXiv:2305.18015

2023/5/29

Inductive Future Time Prediction on Temporal Knowledge Graphs with Interval Time

2023

The complexity and expressive power of limit Datalog

ACM Journal of the ACM (JACM)

2021/12/22

Towards executable knowledge graph translation

2022

Ontology reshaping for knowledge graph construction: applied on Bosch welding case

2022

ScheRe: schema reshaping for enhancing knowledge graph construction

2022

GNNQ: a neuro-symbolic approach to query answering over incomplete knowledge graphs

2022

Egor V. Kostylev
Egor V. Kostylev

H-Index: 17

Enhancing knowledge graph generation with ontology reshaping – Bosch case

2022

Explainable GNN-based models over knowledge graphs

2022/4/25

Towards ontology reshaping for KG generation with user-in-the-loop: applied to Bosch welding

2021

DatalogMTL with negation under stable models semantics

2021/11/3

INDIGO: GNN-based inductive knowledge graph completion using pair-wise encoding

Advances in Neural Information Processing Systems

2021/12/6

Type checking semantically lifted programs via query containment under entailment regimes

Description Logics

2021

Stratified negation in Datalog with metric temporal operators

Proceedings of the AAAI Conference on Artificial Intelligence

2021/5/18

Declarative data analysis using limit Datalog programs

2020

Egor V. Kostylev
Egor V. Kostylev

H-Index: 17

The expressive power of graph neural networks as a query language

ACM SIGMOD Record

2020/12/9

See List of Professors in Egor V. Kostylev University(Universitetet i Oslo)

Co-Authors

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