The wealth of structured (e. g. Wikidata) and unstructured data about the world available today presents an incredible opportunity for tomorrow's Artificial Intelligence.
The occurrence of a fact (edge) is modeled as a multivariate point process whose intensity function is modulated by the score for that fact computed based on the learned entity embeddings.
Previous cross-lingual knowledge graph (KG) alignment studies rely on entity embeddings derived only from monolingual KG structural information, which may fail at matching entities that have different facts in two KGs.
Hyperbolic embeddings have recently gained attention in machine learning due to their ability to represent hierarchical data more accurately and succinctly than their Euclidean analogues.
Named entity recognition (NER) is one of the best studied tasks in natural language processing.
#3 best model for Nested Mention Recognition on ACE 2005
Most of the existing work on embedding (or latent feature) based knowledge graph analysis focuses mainly on the relations between entities.
We discuss two distinct approaches to the generation of such embeddings, namely the training of state-of-the-art embeddings on raw text and annotated versions of the corpus, as well as node embeddings of a co-occurrence graph representation of the annotated corpus.