Conditional Constraints for Knowledge Graph Embeddings. - Michael Weyns; Pieter Bonte; Bram Steenwinckel; Filip De Turck; Femke Ongenae (DL4KG@ESWC, 2020)

Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs. - Bram Steenwinckel; Gilles Vandewiele; Pieter Bonte; Michael Weyns; Heiko Paulheim; Petar Ristoski; Filip De Turck; Femke Ongenae (DEXA Workshops, 2021)

Leak localization in Water Distribution Networks By Directly Fitting the Learning Parameters of a Gaussian Naive Bayes Classifier. - Ganjour Mazaev; Michael Weyns; Filip Vancoillie; Guido Vaes; Femke Ongenae; Sofie Van Hoecke (IEEE Big Data, 2022)

INK: knowledge graph embeddings for node classification. - Bram Steenwinckel; Gilles Vandewiele; Michael Weyns; Terencio Agozzino; Filip De Turck; Femke Ongenae (Data Min. Knowl. Discov., 2022)

Explainable knowledge graph embeddings for industrial process monitoring & control. - Michael Weyns; Thibault Blyau; Bram Steenwinckel; Filip De Turck; Sofie Van Hoecke; Femke Ongenae (Inf. Fusion, 2025)

Enabling Cross-Context Learning with Knowledge Graphs for Anomaly Detection in Communications Networks. - Michael Weyns; Sander Vanden Hautte; Annelies Lejon; Veerle Ledoux; Pieter Bonte; Filip De Turck; Sofie Van Hoecke; Femke Ongenae (J. Netw. Syst. Manag., 2025)