Abstract: A method for building a new knowledge graph may be provided. The method comprises providing an existing knowledge graph, sampling random walks through the existing knowledge graph, determining embedding vectors for vertices and edges of the sampled random walks, and training of a machine-learning model taking as input sequences of the embedding vectors of the random walks. Furthermore, the method comprise receiving a set of documents determining sequences of terms from phrases from the documents the documents, building sequences of embedding vectors from the determined sequences of terms from the phrases, and using the built sequences of embedding vectors from the determined sequences of terms from the phrases as input for the trained machine-learning model for predicting second sequences of terms. Finally, the method comprises merging the predicted second sequences of terms thereby building the new knowledge graph.

Abstract: In an approach for automatic knowledge graph construction, a processor receives a text document and trains a first machine-learning system to predict entities in the text document. Thereby, the text document with labeled entities is used as training data. A processor trains a second machine-learning system to predict relationship data between the entities, wherein, as training data, entities and edges of an existing knowledge graph and determined embedding vectors of the entities and edges are used. A processor receives a set of second text documents, determines second embedding vectors therefrom, and predicts entities and edges; thereby using the set of second text documents, the determined second embedding vectors, and the predicted entities and associated embedding vectors of the predicted entities as input for the first and second trained machine-learning model. A processor builds triplets of the entities and the edges representing a new knowledge graph.