End-to-End Entity Classification on Multimodal Knowledge Graphs

Autor: Wilcke, W. X., Bloem, P., de Boer, V., Veer, R. H. van t, van Harmelen, F. A. H.

End-to-end multimodal learning on knowledge graphs has been left largely unaddressed. Instead, most end-to-end models such as message passing networks learn solely from the relational information encoded in graphs' structure: raw values, or literals,

Externí odkaz: http://arxiv.org/abs/2003.12383

A Deep Neural Network for Link Prediction on Knowledge Graphs

Autor: Wilcke, W. X., Victor de Boer, Frank van Harmelen, Kleijn, M. T. M.

Publikováno v: Wilcke, W X, de Boer, V, van Harmelen, F A H & de Kleijn, M T M 2016, ' A Deep Neural Network for Link Prediction on Knowledge Graphs ', ICT.OPEN 2016, Amersfoort, Netherlands, 22/03/16-23/03/16 .
Vrije Universiteit Amsterdam
ICT.OPEN 2016

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::ca58a0f2e9f766cd9190a5fa22281427
https://research.vu.nl/ws/files/55125318/WilckeWX_ICTOPEN16.pdf

User-Driven Pattern Mining on knowledge graphs: an Archaeological Case Study

Autor: Wilcke, W. X., Victor de Boer, Frank van Harmelen

Publikováno v: Vrije Universiteit Amsterdam
Benelearn 2017
Wilcke, W X, de Boer, V & van Harmelen, F 2017, ' User-Driven Pattern Mining on knowledge graphs: an Archaeological Case Study ', Benelearn 2017, Eindhoven, Netherlands, 9/06/17-10/06/17 .

In recent years, there has been a growing interest from the Digital Humanities in knowledge graphs as data modelling paradigm. Already, many data sets have been published as such and are available in the Linked Open Data cloud. With it, the nature of

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::2fd33aeda3cb9e3b216f0d7cb547ab9d
https://research.vu.nl/en/publications/88aee8bf-d116-40cc-aa92-5704a3469fa4

ARIADNE: First Report on Data Mining

Autor: Wilcke, W. X., Victor de Boer, Frank van Harmelen, Mauritius de Kleijn, Wansleeben, M.

Publikováno v: Vrije Universiteit Amsterdam

Recent years have witnessed a growing interest from archaeological communities in Linked Data. ARIADNE, the Advanced Research Infrastructure for Archaeological Data set Networking in Europe, facilitates a central web portal that provides access to ar

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::0fb65ff66a5dc32e87e91aec4b7ae623
https://research.vu.nl/en/publications/dfa31ef6-a2fd-4867-a6b2-7654aabff10c

Learning an Optimized Deep Neural Network for Link Prediction on Knowledge Graphs

Autor: Wilcke, W.X.

Publikováno v: Wilcke, W X 2015, ' Learning an Optimized Deep Neural Network for Link Prediction on Knowledge Graphs ', ECML PKDD-European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, 7/09/15-11/09/15 .
ECML PKDD-European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Wilcke, W X 2015, Learning an Optimized Deep Neural Network for Link Prediction on Knowledge Graphs . in J Hollmén & P Papapetrou (eds), Proceedings of the ECMLPKDD 2015 Doctoral Consortium . Aalto University, Helsinki, pp. 226-235, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 7/09/15 . < https://aaltodoc.aalto.fi/handle/123456789/18224 >

Recent years have seen the emergence of graph-based Knowl-edge Bases build upon Semantic Web technologies, known as KnowledgeGraphs (KG). Popular examples are DBpedia and GeoNames. The formal system underlying these KGs provides inherent support for

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::6bda57a32acf907281ba890fdb485e27
https://aaltodoc.aalto.fi/handle/123456789/18224