Title Image


Distributed Representation-Based Reasoning

  |   Data Analysis, Services   |   No comment

Knowledge Graph Embeddings (KGEs) are low-dimensional representations of the entities and relations in a knowledge Graph. They provide a generalizable context about the overall KG that can be used to infer relations or entities. The knowledge graph embeddings are computed so that they satisfy certain properties. KGE models define different score functions that measure the distance of two entities relative to its relation type in the low-dimensional embedding space. These score functions are used to train the KGE models so that the entities connected by relations are close to each other while the entities that are not connected are far away. There are many popular KGE models such as TransE, TransR, RESCAL, DistMult, ComplEx, and RotatE, which define different score functions to learn entity and relation embeddings.

No Comments

Post A Comment