Deep embeddings and Graph Neural Networks: can context improve domain-independent predictions?
Graph neural networks (GNNs) are deep learning architectures that apply graph convolutions through message-passing processes between nodes, represented as embeddings. GNNs have recently become popular because of how they obtain a contextual representation of each node that takes into account the inf...
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| Format: | master thesis |
| Status: | Versión aceptada para publicación |
| Publication Date: | 2022 |
| Country: | España |
| Institution: | Universidad de Sevilla (US) |
| Repository: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/148087 |
| Online Access: | https://hdl.handle.net/11441/148087 |
| Access Level: | Open access |
| Keyword: | Knowledge Graphs Graph Neural Networks Attributive embeddings Deep graph embeddings Machine Learning Grafos de Conocimiento Redes Neuronales de Grafos Embeddings Atributivos Embeddings profundos de grafos |
| Summary: | Graph neural networks (GNNs) are deep learning architectures that apply graph convolutions through message-passing processes between nodes, represented as embeddings. GNNs have recently become popular because of how they obtain a contextual representation of each node that takes into account the information from surrounding nodes. Existing work has focused on the development of GNN architectures, using basic domain-specific information about the nodes to compute embeddings. In the context of knowledge graphs, much effort has been put towards developing deep learning techniques to obtain node embeddings that preserve information about relationships and structure without relying on domain-specific data. The potential of the application of graph neural networks with deep embeddings of knowledge graphs remains largely unexplored. In this project, we carry out a number of experiments to answer open research questions about how said embeddings perform when using a graph neural network. We test 7 different deep embeddings across several attribute prediction tasks in two attribute-rich datasets. We conclude that there is a significant performance improvement but it varies heavily depending on the task and deep embedding technique. Given the interest of the results obtained, we have submitted an article to the conference CIKM’22, and we have defined some tasks and future work in order to continue this study in the form of a PhD. |
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