The field of knowledge engineering has been one of the most visible successes of AI to date. Knowledge acquisition is the main bottleneck in the knowledge engineer's work. The benchmark centers on the knowledge engineering viewpoint, covering some of the characteristics the knowledge engineer wants to find in a machine-learning tool. The proposed model has been applied to a set of machine-learning tools, comparing expected and obtained results.

The field of knowledge engineering has been one of the most visible successes of AI to date. Knowledge acquisition is the main bottleneck in the knowledge engineer's work. Machine-learning tools have contributed positively to the process of trying to eliminate or open up this bottleneck, but how do we know whether the field is progressing? How can we determine the progress made in any of its branches? How can we be sure of an advance and take advantage of it? This article proposes a benchmark as a classificatory, comparative, and metric criterion for machine-learning tools. The benchmark centers on the knowledge engineering viewpoint, covering some of the characteristics the knowledge engineer wants to find in a machine-learning tool. The proposed model has been applied to a set of machine-learning tools, comparing expected and obtained results. Experimentation validated the model and led to interesting results.