Abstract: The present disclosure describes methods and systems for generating an approximated embedding of an out-of-knowledge-graph entity based on a knowledge graph. The method includes: receiving a target entity, a dataset associated with the target entity, and an embeddings space of a knowledge graph comprising a set of structured data, wherein the target entity is out of the knowledge graph and the embeddings space includes a set of vectors representing the set of structured data in the embeddings space; selecting a set of elements from the knowledge graph, each element being related to the target entity according to the dataset associated with the target entity; constructing a set of descriptory triples based on the target entity and the set of elements; obtaining an embedding matrix based on the descriptory triples and the embeddings space; and generating an approximated embedding for the target entity based on the embedding matrix.

Abstract: The present disclosure describes methods and systems for generating an explanation for a prediction based on a knowledge graph. The method includes receiving a target triple and a knowledge graph including a set of structured data; converting the knowledge graph to an embeddings space and outputting a plausibility prediction for the target triple; sampling a set of neighbors of elements of the target triple in the embeddings space; obtaining a set of example triples based on the set of neighbors according to the target triple; obtaining a prototype graph based on the set of the example triples according to the target triple; generating an explanation graph based on the prototype graph, the set of example triples, and the target triple; and generating an explanation for the plausibility prediction based on the explanation graph.

Abstract: A device may receive purchase data identifying purchases by users of user devices and identifying non-temporal data associated with the users, and may preprocess the purchase data to generate sequences of multivariate and multimodal symbols. The device may process the sequences of multivariate and multimodal symbols, with a long short-term memory based encoder-decoder model, to generate sequence embeddings, and may process the non-temporal data associated with the users, with a knowledge graph, to determine knowledge graph embeddings capturing the non-temporal data. The device may process the sequence embeddings and the knowledge graph embeddings, with a knowledge graph embedding model, to generate modified sequence embeddings, and may process the modified sequence embeddings, with a clustering model, to determine clusters of the users in relation to products or services purchased by the users. The device may perform one or more actions based on the clusters of the users.