US 2022/0067590 A1 · AUTOMATIC KNOWLEDGE GRAPH CONSTRUCTION

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.

Claims20

Dependent claims

A computer-implemented method for building a new knowledge graph, the method comprising:

receiving a first text document;
training a first machine-learning system to develop a first prediction model adapted to predict first entities in the first text document, wherein labeled entities from the first text document are used as first training data;
training a second machine-learning system to develop a second prediction model adapted to predict first edges between the first entities, wherein existing entities and existing edges of an existing knowledge graph and determined first embedding vectors of the existing entities and the existing edges are used as second training data;
receiving a set of second text documents;
determining second embedding vectors from text segments from the set of second text documents;
predicting second entities in the set of second text documents by using the set of second text documents and the second embedding vectors as inputs for the first trained machine-learning model;
predicting second edges in the set of second text documents by using the second entities and associated embedding vectors of the second entities as input for the second trained machine-learning model;
and
building triplets of the second entities and the related second edges to build a new knowledge graph.

6 more elements

A knowledge graph construction system for building a knowledge graph, the knowledge graph construction system comprising:

one or more computer processors;
one or more computer readable storage media;
program instructions stored on the computer readable storage media for execution by at least one of the one or more processors, the program instructions comprising:
program instructions to receive a first text document;
program instructions to train a first machine-learning system to develop a first prediction model adapted to predict first entities in the first text document, wherein labeled entities from the first text document are used as training data;
program instructions to train a second machine-learning system to develop a second prediction model adapted to predict first edges between the first entities, wherein existing entities and existing edges of an existing knowledge graph and determined first embedding vectors of the first entities and the first edges are used as first training data;
program instructions to receive a set of second text documents;
program instructions to determine second embedding vectors from text segments from the set of second text documents;
program instructions to predict second entities in the set of second text documents by using the set of second text documents and the second embedding vectors as inputs for the first trained machine-learning model;
program instructions to predict second edges in the set of second text documents by using the second entities and associated embedding vectors of the second entities as inputs for the second trained machine-learning model;
and
program instructions to build triplets of the second entities and the related second edges to build a new knowledge graph.

9 more elements

Dependencies are inferred from the claim text.

Automatic knowledge graph construction

Life of this patent

Automatic knowledge graph construction

Life of this patent

Abstract

Claims20