Joint Embedding Learning of Educational Knowledge Graphs
This work addresses the specific challenge of embedding educational knowledge graphs, which is incremental as it adapts existing techniques to a domain where literals are prioritized over structure.
The paper tackled the problem of embedding learning for educational knowledge graphs, where rich literals are more valuable than structural relationships, by proposing a novel model that jointly considers both structural and literal information, and experimental results on three educational graphs and benchmarks proved its effectiveness and superiority over baselines.
As an efficient model for knowledge organization, the knowledge graph has been widely adopted in several fields, e.g., biomedicine, sociology, and education. And there is a steady trend of learning embedding representations of knowledge graphs to facilitate knowledge graph construction and downstream tasks. In general, knowledge graph embedding techniques aim to learn vectorized representations which preserve the structural information of the graph. And conventional embedding learning models rely on structural relationships among entities and relations. However, in educational knowledge graphs, structural relationships are not the focus. Instead, rich literals of the graphs are more valuable. In this paper, we focus on this problem and propose a novel model for embedding learning of educational knowledge graphs. Our model considers both structural and literal information and jointly learns embedding representations. Three experimental graphs were constructed based on an educational knowledge graph which has been applied in real-world teaching. We conducted two experiments on the three graphs and other common benchmark graphs. The experimental results proved the effectiveness of our model and its superiority over other baselines when processing educational knowledge graphs.