Our paper “Gollum: A Gold Standard for Large Scale Multi Source Knowledge Graph Matching” has been accepted at the 4th Conference on Automated Knowledge Graph Construction.

Our paper “On a Generalized Framework for Time-Aware Knowledge Graphs”, co-authored by Franz Krause, Tobias Weller, and Heiko Paulheim, has been awarded the best paper award at this year's Semantics conference.

Jan Portisch has successfully defended his PhD thesis titled “Exploiting General-Purpose Background Knowledge for Automated Schema Matching” on August 25th.

The paper “Walk this Way! Entity Walks and Property Walks for RDF2vec” by Jan Portisch and Heiko Paulheim has won the best poster award at ESWC 2022.

Our paper “Background Knowledge in Schema Matching: Strategy vs. Data” has been accepted at the 20th International Semantic Web Conference. The paper has been co-authored by Jan Portisch, Michael Hladik, and Heiko Paulheim. In the paper, we have conducted a number of controlled experiments with ...

The paper “Winning at Any Cost – Infringing the Cartel Prohibition With Reinforcement Learning” was accepted at the 19th International Conference on Practical Applications of Agents and Multi-Agent Systems.

The paper “GraphConfRec: A Graph Neural Network-Based Conference Recommender System”, co-authored by Andreea Iana and Heiko Paulheim, has been accepted for publication at the ACM/IEEE Joint Conference on Digital Libraries. In the paper, we explore the usage of modern graph neural network based ...

Earlier this year, we introduced kgextension, the Python extension for processing knowledge graphs. With this extension, data analysts can easily link their data at hand to both public and private knowledge graphs, and add the wealth of public knowledge graphs such as Wikidata to their data ...

Our paper “Collecting a Large Scale Dataset for Classifying Fake News Tweets Using Weak Supervision” has been accepted for publication in Future Internet.

CaLiGraph is an open knowledge graph extracted from categories, tables, and listings in Wikipedia.