Name
Title | ||
|---|---|---|
Integrating Uncertain Knowledge in a Domain Ontology for Room Concept Classifications |
Abstract | ||
|---|---|---|
Ontologies provide a representation of precise knowledge about concepts, their attributes and relations. The Dempster-Shafer
theory provides a representation of epistemicplausibilities. In AI both representations are typically developed separately
on purpose, which isappropriate unless theircombinationis required. Real world applications, however, sometimes require a
combination of both.
In this paper we will present such a combination of a domain ontology and uncertain knowledge. Our approach arises from the
need of a room classification system for representing room concepts (in the sense of classifying names that are cognitively
assigned to rooms, such as “kitchen”, “laboratory”, “office”) that can be derived from objects occurring in the rooms. These
room concepts can only be determined with a certain degree of belief, not so much depending on the system’s quality as depending
on ambiguities in the cognitive assignment of room concepts. Hence, uncertainty about concepts that exist in reality also
needs to be represented in the application.
|
Year | DOI | Keywords |
|---|---|---|
2006 | 10.1007/978-1-84628-663-6_18 | dempster shafer theory,classification system |
Field | DocType | Citations |
Ontology (information science),Ontology,Ontology-based data integration,Information retrieval,Process ontology,Artificial intelligence,Suggested Upper Merged Ontology,Upper ontology,Mathematics | Conference | 4 |
PageRank | References | Authors |
0.48 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joana Hois | 1 | 168 | 11.93 |
| Kerstin Schill | 2 | 183 | 25.15 |
| John A. Bateman | 3 | 488 | 80.06 |