Name
Abstract | ||
|---|---|---|
This paper proposes a probabilistic extension of terminological logics. The extension maintains the original performance of drawing inferences on a hierarchy of terminological definitions. It enlarges the range of applicability to real world domains determined not only by definitional but also by uncertain knowledge. First, we introduce the propositionally complete terminological language ALC. On the basis of the language construct probabilistic implication, it is shown how statistical information on concept dependencies can be represented. To guarantee (terminological and probabilistic) consistency, several requirements have to be met. Moreover, these requirements allow to infer implicitly existent probabilistic relationships and their quantitative computation. Consequently, our model applies to domains where both term descriptions and non-categorical relations between term extensions have to be represented. |
Year | DOI | Venue |
|---|---|---|
1991 | 10.1007/3-540-54659-6_89 | ECSQARU |
Keywords | Field | DocType |
hybrid approach,modeling uncertainty,terminological logics | Knowledge representation and reasoning,Computer science,Language construct,Artificial intelligence,Probabilistic logic,Hierarchy,Computation | Conference |
Volume | ISSN | ISBN |
548 | 0302-9743 | 3-540-54659-6 |
Citations | PageRank | References |
13 | 5.46 | 15 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jochen Heinsohn | 1 | 145 | 41.56 |