Article:Machine Decistion and Human Intervention

How can machines draw thoughtful conclusions without human intervention? Will that be possible in the Networked Society?

In order to achieve that, machines have to be able to derive high-level conclusions from low-level raw data sets and then to allow these machines to make further actions without any kind of human intervention. The results have to be produced quickly based on ever-changing and sometimes unreliable data sources. This was the challenge that we at Ericsson Research in San Jose and Tokyo teamed together to solve.

We developed a reasoning framework marrying the merits of ontology-based reasoning with other reasoning techniques. For those of you who don’t know, ontology is a semantic specification of a set of concepts and the relations between them. Ontology enables interoperability and supports sophisticated reasoning. Ontology-based reasoning is powerful, but it has well-known performance issues especially in cases where ontology is large and complicated.

During our research we learned that if you’re going to use semantic techniques for real-time reasoning, you need to optimize. This can be done using one or a combination of the following:

optimize the size (and maybe the shape) of the ontology that describes the problem domain;

use ontology only for the parts that really need it;

decouple ontology reasoning from the data sources

Our reasoning framework employs two key mechanisms.