| Capturing industrial information models with ontologies and constraints E Kharlamov, BC Grau, E Jiménez-Ruiz, S Lamparter, G Mehdi, ... The Semantic Web–ISWC 2016: 15th International Semantic Web Conference, Kobe …, 2016 | 79 | 2016 |
| Analyzing political sentiment on Twitter M Ringsquandl, D Petkovic 2013 AAAI Spring Symposium Series, 2013 | 51 | 2013 |
| Reasoning on knowledge graphs with debate dynamics M Hildebrandt, JAQ Serna, Y Ma, M Ringsquandl, M Joblin, V Tresp Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4123-4131, 2020 | 45 | 2020 |
| Neural multi-hop reasoning with logical rules on biomedical knowledge graphs Y Liu, M Hildebrandt, M Joblin, M Ringsquandl, R Raissouni, V Tresp The Semantic Web: 18th International Conference, ESWC 2021, Virtual Event …, 2021 | 43 | 2021 |
| On event-driven knowledge graph completion in digital factories M Ringsquandl, E Kharlamov, D Stepanova, S Lamparter, R Lepratti, ... 2017 IEEE International Conference on Big Data (Big Data), 1676-1681, 2017 | 30 | 2017 |
| Semantic-Guided Feature Selection For Industrial Automation Systems M Ringsquandl, S Lamparter, S Brandt, T Hubauer, R Lepratti Proceedings of the 14th International Semantic Web Conference, 225-240, 2015 | 30 | 2015 |
| Event-enhanced learning for KG completion M Ringsquandl, E Kharlamov, D Stepanova, M Hildebrandt, S Lamparter, ... The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete …, 2018 | 20 | 2018 |
| Control apparatus of an automation system T Hubauer, S Lamparter, M Ringsquandl, M Roshchin US Patent 10,545,967, 2020 | 19 | 2020 |
| Assessing IFC classes with means of geometric deep learning on different graph encodings F Collins, A Braun, M Ringsquandl, D Hall, A Borrmann Proc. of the 2021 European Conference on Computing in Construction, 2021 | 17 | 2021 |
| Knowledge fusion of manufacturing operations data using representation learning M Ringsquandl, S Lamparter, R Lepratti, P Kröger Advances in Production Management Systems. The Path to Intelligent …, 2017 | 17 | 2017 |
| Power to the relational inductive bias: Graph neural networks in electrical power grids M Ringsquandl, H Sellami, M Hildebrandt, D Beyer, S Henselmeyer, ... Proceedings of the 30th ACM International Conference on Information …, 2021 | 11 | 2021 |
| Task-driven knowledge graph filtering improves prioritizing drugs for repurposing F Ratajczak, M Joblin, M Ringsquandl, M Hildebrandt BMC bioinformatics 23 (1), 84, 2022 | 10 | 2022 |
| Graph-based predictions and recommendations in flexible manufacturing systems M Ringsquandl, S Lamparter, R Lepratti IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society …, 2016 | 10 | 2016 |
| Diagnostics of trains with semantic diagnostics rules E Kharlamov, O Savković, M Ringsquandl, G Xiao, G Mehdi, EG Kalayc, ... Inductive Logic Programming: 28th International Conference, ILP 2018 …, 2018 | 6 | 2018 |
| Shape encoding for semantic healing of design models and knowledge transfer to Scan-to-BIM FC Collins, M Ringsquandl, A Braun, DM Hall, A Borrmann Proceedings of the Institution of Civil Engineers-Smart Infrastructure and …, 2022 | 5 | 2022 |
| Filling gaps in industrial knowledge graphs via event-enhanced embedding M Ringsquandl, E Kharlamov, D Stepanova, M Hildebrandt, S Lamparter, ... 17th International Semantic Web Conference, 2018 | 5 | 2018 |
| SOMM: industry oriented ontology management tool I Horrocks, B Cuenca Grau, E Kharlamov, E Jimenez Ruiz, Y Nenov, ... ISWC 2016, 2016 | 4 | 2016 |
| Estimating processing times within context-aware manufacturing systems M Ringsquandl, S Lamparter, R Lepratti IFAC-PapersOnLine 48 (3), 2009-2014, 2015 | 4 | 2015 |
| TAXONOMY EXTRACTION FROM AUTOMOTIVE NATURAL LANGUAGE REQUIREMENTS USING UNSUPERVISED LEARNING M Ringsquandl, M Schraps International Journal on Natural Language Computing 3 (4), 2014 | 4 | 2014 |
| Analysis of the attention in tabular language models A Koleva, M Ringsquandl, V Tresp NeurIPS 2022 First Table Representation Workshop, 2022 | 3 | 2022 |
