8. A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions that, when executed by one or more processors, cause the one or more processors to: receive a knowledge graph that is generated based on training data and an ontology for the training data, the training data including information associated with a subject of the ontology, and the ontology including: classes, and properties; convert the knowledge graph into knowledge graph embeddings, the knowledge graph embeddings including points in a k-dimensional metric space; receive additional ontology information; identify a new entity, in the additional ontology information, that is not present in the knowledge graph embeddings; generate revised knowledge graph embeddings that include an embedding for the new entity based on: a first average quantity of entities that are related to the knowledge graph and the new entity, and a second average quantity of entities that are related to the new entity and are not related to the knowledge graph; receive a query for information associated with the knowledge graph; generate candidate responses to the query based on the knowledge graph; score the candidate responses based on the revised knowledge graph embeddings; and identify a particular candidate response, of the candidate responses, based on scores for the candidate responses.
8. A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions that, when executed by one or more processors, cause the one or more processors to: receive a knowledge graph that is generated based on training data and an ontology for the training data, the training data including information associated with a subject of the ontology, and the ontology including: classes, and properties; convert the knowledge graph into knowledge graph embeddings, the knowledge graph embeddings including points in a k-dimensional metric space; receive additional ontology information; identify a new entity, in the additional ontology information, that is not present in the knowledge graph embeddings; generate revised knowledge graph embeddings that include an embedding for the new entity based on: a first average quantity of entities that are related to the knowledge graph and the new entity, and a second average quantity of entities that are related to the new entity and are not related to the knowledge graph; receive a query for information associated with the knowledge graph; generate candidate responses to the query based on the knowledge graph; score the candidate responses based on the revised knowledge graph embeddings; and identify a particular candidate response, of the candidate responses, based on scores for the candidate responses. 11. The non-transitory computer-readable medium of claim 8 , where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to: approximate the embedding for the new entity based on a weight greater than one, the weight being applied to the first average quantity of entities; and where the one or more instructions, that cause the one or more processors to generate the revised knowledge graph embeddings, cause the one or more processors to: generate the revised knowledge graph embeddings based on approximating the embedding for the new entity.