BACKGROUND

Today's digitization industry effort reveals more and more autonomous systems. Each of them may be equipped with a plurality of sensors, some of them may only have a still image camera. Well-known recognition techniques may be used to identify one or more objects of a captured image. However, in order to enable an orientation in a real-world environment the autonomous systems, like a production robot, a service robot in the healthcare industry, or a shop floor vehicle, would need to be enabled to identify and recognize not only an object in an image but also capture a context between identified objects in the image and have a basic interpretation of what is happening between the objects, i.e., associated attributes of the identified objects and a “guess” what has happened or what will happen. Such techniques may go far beyond traditional detection or classification of images with neural networks or other machine-learning techniques.

SUMMARY

According to an embodiments of the present disclosure a method for event is provided. The method includes receiving input data. The method further includes identifying an object in the input data with the identified object associated with a first node in a knowledge graph. The method further includes determining a second node of a first object event with the second node related to the first node in the knowledge graph. The method further includes contextualizing the identified input object with the first object event. Further provided are a system and a computer program product for performing the method.

The above summary is not intended to describe each illustrated embodiment or every implementation of the present disclosure.

