Revising Ontologies

The main idea and benefit of belief graphical models is factoring a joint uncertainty measure in the form of a combination of local measures thanks to independence relations that

traditional partial meet contraction on belief bases closed by a consequence operation that is between the classical consequence operator and the identity (i.e., the base

I If the set of beliefs is already consistent with the new information, then the review coincides with expansion, but if new knowledge is inconsistent with any previous beliefs,

From the revision point of view, our voting example consists of three candidate options a, b and c and several voters who express their total preferences regarding these options..

The method involves determining a rep- resentative set of ‘boundary’ probability distributions consistent with the current belief base, revising each of these probability

In so doing, we will then also need to code the machinery that computes the overall < from each of the individual voter’s preference relations. This could be done either by a set

ت نم لك راذنإ نىعبم ،ماعلا عدرلا في تلثتم تيلا ةبوقعلا فادهلأ اقيقتح ،ءاوس دح ىلع ينمرلمجا هل لوس باقعلبا ديدهتلا قيرط نع هيلع موكلمحا هيف راس يذلا وحنلا

The first method finds a single representative belief state through maximum entropy inference and the second method revises boundary belief states using minimum cross-entropy