Uncertain Knowledge, Ontologies, and Reasoners – A Discussion of Semantic Ambiguity in the Presence of Statistical Uncertainty

Before continuing with uncertain domain knowledge, the subject of this and future posts, I’d like to give an example. I’ll introduce a scenario in which both semantic ambiguity (refer to my prior post) and statistical uncertainty (refer to the tutorials in the right-hand column of this blog) are present.

One of the limitations of most current description logic (DL) reasoners (refer to my August 6 & 24 posts below) is the inability to handle uncertain knowledge. It is a serious obstacle to the expansion of the Semantic Web, to name just one technology, because many domains of human interest contains knowledge that cannot be represented with absolute certainty. One example of an uncertain domain is medicine, in particular, disease diagnosis. Symptoms, causes and consequences of many diseases are uncertain which complicates conceptualization of such domains in formal ontologies and thus restricts machine understanding.

The next two figures, rendered without further explanation, illustrate that the difficulties introduced by semantic ambiguity of a deterministic kind can be made even more difficult when there is a need to consider statistical uncertainty at the same time. This might be the case when a patient is seen by one doctor and then asks a second doctor for his or her opinion.

However, these days, a good deal of technology is often employed by doctors before they speak to patients and to each other.

{ click on the images above for larger views }

To be continued . . .