SpaceRL-KG: Searching paths automatically combining embedding-based rewards with Reinforcement Learning in Knowledge Graphs
Knowledge Graph Completion seeks to find missing elements in a Knowledge Graph, usually edges representing some relation between two concepts. One possible way to do this is to find paths between two nodes that indicate the presence of a missing edge. This can be achieved through Reinforcement Learn...
| Autores: | , , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/165813 |
| Acesso em linha: | https://hdl.handle.net/11441/165813 https://doi.org/10.1016/j.eswa.2024.124410 |
| Access Level: | acceso abierto |
| Palavra-chave: | Knowledge Graphs Reinforcement Learning Translation embeddings Deep graph embeddings Machine learning |
| Resumo: | Knowledge Graph Completion seeks to find missing elements in a Knowledge Graph, usually edges representing some relation between two concepts. One possible way to do this is to find paths between two nodes that indicate the presence of a missing edge. This can be achieved through Reinforcement Learning, by training an agent that learns how to navigate through the graph, starting at a node with a missing edge and identifying what edge among the available ones at each step is more promising in order to reach the target of the missing edge. While some approaches have been proposed to this effect, their reward functions only take into account whether the target node was reached or not, and only apply a single Reinforcement Learning algorithm. In this regard, we present a new family of reward functions based on node embeddings and structural distance, leveraging additional information related to semantic similarity and removing the need to reach the target node to obtain a measure of the benefits of an action. Our experimental results show that these functions, as well as the novel use of more modern Reinforcement Learning techniques, are able to obtain better results than the existing strategies in the literature. |
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