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Abstract
Recent years have seen the emergence of graph-based Knowl-
edge Bases build upon Semantic Web technologies, known as Knowledge
Graphs (KG). Popular examples are DBpedia and GeoNames. The formal system underlying these KGs provides inherent support for deductive reasoning. Growing popularity has exposed several limitations of this
ability, amongst which are scalability and uncertainty issues, as well as
coping with heterogeneous, noisy, and inconsistent data. By supplementing this form of reasoning with Machine Learning algorithms, these hurdles are much more easily overcome. Of the existing research in this area,
only a handful have been considering a Deep Neural Network. Moreover,
only one of these studies has addressed the problem of hyper-parameter
optimization, albeit under specific conditions. To contribute to this area
of research, we propose a research design that will investigate a Deep
Neural Network with optimized hyper-parameters for its effectiveness to
perform link prediction on real-world KGs.
edge Bases build upon Semantic Web technologies, known as Knowledge
Graphs (KG). Popular examples are DBpedia and GeoNames. The formal system underlying these KGs provides inherent support for deductive reasoning. Growing popularity has exposed several limitations of this
ability, amongst which are scalability and uncertainty issues, as well as
coping with heterogeneous, noisy, and inconsistent data. By supplementing this form of reasoning with Machine Learning algorithms, these hurdles are much more easily overcome. Of the existing research in this area,
only a handful have been considering a Deep Neural Network. Moreover,
only one of these studies has addressed the problem of hyper-parameter
optimization, albeit under specific conditions. To contribute to this area
of research, we propose a research design that will investigate a Deep
Neural Network with optimized hyper-parameters for its effectiveness to
perform link prediction on real-world KGs.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the ECMLPKDD 2015 Doctoral Consortium |
| Editors | J Hollmén, P Papapetrou |
| Place of Publication | Helsinki |
| Publisher | Aalto University |
| Pages | 226-235 |
| ISBN (Print) | 9789526064437 |
| Publication status | Published - 2015 |
| Event | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - Helsinki Duration: 7 Sept 2015 → 11 Sept 2015 |
Conference
| Conference | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases |
|---|---|
| Period | 7/09/15 → 11/09/15 |
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Learning an Optimized Deep Neural Network for Link Prediction on Knowledge Graphs
Wilcke, W. X., 2015.Research output: Contribution to Conference › Poster › Academic
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