Compact Walks: Taming Knowledge-Graph Embeddings with Domain- and Task-Specific Pathways
Pei-Yu Hou, Daniel Korn, Cleber Camilo Melo-Filho, David Wright, Alexander Tropsha, Rada Y. Chirkova · Proceedings of the 2022 International Conference on Management of Data · 2022
Knowledge-graph (KG) embeddings have emerged as a promise in addressing challenges faced by modern biomedical research, including the growing gap between therapeutic needs and available treatments. The popularity of KG embeddings in graph analytics is on the rise, due at least partially to the presumed semanticity of the learned embeddings. Unfortunately, the ability of a node neighborhood picked up by an embedding to capture the node's semantics may depend on the characteristics of the data. One of the reasons for this problem is that KG nodes can be promiscuous, that is, associated with a number of different relationships that are not unique or indicative of the properties of the nodes.