Edge: Enriching Knowledge Graph Embeddings with External Text
This work addresses the challenge of aligning diverse data sources to enhance knowledge graph representations, which is incremental by building on prior methods that used hard co-occurrence.
The paper tackles the sparsity problem in knowledge graphs by proposing Edge, a framework that enriches knowledge graph embeddings using external text through soft augmentation, achieving improved performance in link prediction and node classification on four benchmark datasets.
Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods. While there is an abundance of textual information throughout the web and many existing knowledge bases, aligning information across these diverse data sources remains a challenge in the literature. Previous work has partially addressed this issue by enriching knowledge graph entities based on "hard" co-occurrence of words present in the entities of the knowledge graphs and external text, while we achieve "soft" augmentation by proposing a knowledge graph enrichment and embedding framework named Edge. Given an original knowledge graph, we first generate a rich but noisy augmented graph using external texts in semantic and structural level. To distill the relevant knowledge and suppress the introduced noise, we design a graph alignment term in a shared embedding space between the original graph and augmented graph. To enhance the embedding learning on the augmented graph, we further regularize the locality relationship of target entity based on negative sampling. Experimental results on four benchmark datasets demonstrate the robustness and effectiveness of Edge in link prediction and node classification.