Probabilistic Reasoning for OWL DL Ontologies -- Reasoning about Uncertain Domain Knowledge

Pronto is an extension of Pellet that enables probabilistic knowledge representation and reasoning in OWL ontologies. Pronto is distributed as a Java library equipped with a command line tool for demonstrating its basic capabilities. (There is no 1.0 release!) The figure below outlines the relationships among Pronto, an OWL DL Ontology, and the editor that might have created the ontology. Pellet supports reasoning with the full expressivity of OWL-DL (SHOIN(D) in Description Logic jargon) and has been extended to support the forthcoming OWL 2 specification (SROIQ(D)).



Pronto offers core OWL reasoning services for knowledge bases containing uncertain knowledge; that is, it processes statements like “Bird is a subclass-of Flying Object with probability greater than 90%” or “Tweety is-a Flying Object with probability less than 5%”. The use cases for Pronto include ontology and data alignment, as well as reasoning about uncertain domain knowledge generally; for example, risk factors associated with breast cancer.

Pronto adds the following capabilities to Pellet:

* Adding probabilistic statements to an ontology (using OWL's annotation properties)

* Inferring new probabilistic statements from a probabilistic ontology

* Explaining results of probabilistic reasoning

Pronto depends on Pellet, which is included in the Pronto release package. It also relies on Ops Research's OR-Objects package, which needs to be downloaded separately.

To download Pronto, click here.

To download OR-Objects, click here.

The features of Pronto (in addition to the features of Pellet) are outlined in the file basic.pdf, located in the /doc directory of the Pronto download.

If you are interested in a rigorous description of the approach taken by Pronto, read the paper by Thomas Lukasiewicz “Probabilistic Description Logics for the Semantic Web,” which is cited under Resources in basic.pdf.

For further reading on Probabilistic Reasoning, click

http://clarkparsia.com/weblog/2007/09/27/introducing-pronto/

http://klinov.blogspot.com/2007_11_01_archive.html

http://clarkparsia.com/weblog/2007/10/02/using-pronto/


For further reading on Pellet features, click

http://clarkparsia.com/pellet/features

An upcoming post will discuss ontologies that use fuzzy logic.

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 . . .