Machine learning approach for generation of explainable new entities in a knowledge graph for optimization or improvement of target properties

A computer system programmed for incorporating a new entity in a knowledge graph for optimizing or improving a target property, the computer system comprising one or more hardware processors which, alone or in combination, are configured to provide for execution of the following steps:

• detecting counterfactual causes in a causality graph that are to be modified to achieve the target property, wherein the causality graph is connected to the knowledge graph by links representing semantic relations;
• generating the new entity in the knowledge graph by embedding the new entity in a latent space of the knowledge graph relative to existing entities;
• and
• simulating a change of causes in the causality graph resulting from generating the new entity in the knowledge graph.

A tangible, non-transitory computer-readable medium for incorporating a new entity in a knowledge graph for optimizing or improving a target property, the computer-readable medium having instructions thereon, which, upon being executed by one or more processors, provides for execution of the following steps:

• detecting counterfactual causes in a causality graph that are to be modified to achieve the target property, wherein the causality graph is connected to the knowledge graph by links representing semantic relations;
• generating the new entity in the knowledge graph by embedding the new entity in a latent space of the knowledge graph relative to existing entities;
• and
• simulating a change of causes in the causality graph resulting from generating the new entity in the knowledge graph.