Name
Abstract | ||
|---|---|---|
We consider ontology-based query answering in a setting where some of the data are numerical and of a probabilistic nature, such as data obtained from uncertain sensor readings. The uncertainty for such numerical values can be more precisely represented by continuous probability distributions than by discrete probabilities for numerical facts concerning exact values. For this reason, we extend existing approaches using discrete probability distributions over facts by continuous probability distributions over numerical values. We determine the exact (data and combined) complexity of query answering in extensions of the well-known description logics EL and ALC with numerical comparison operators in this probabilistic setting. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-66167-4_5 | Lecture Notes in Artificial Intelligence |
Field | DocType | Volume |
Query optimization,Ontology (information science),Query language,Query expansion,Computer science,Algorithm,Description logic,Probability distribution,Relational operator,Probabilistic logic | Conference | 10483 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.35 |
References | Authors | |
22 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Franz Baader | 1 | 8123 | 646.64 |
| Patrick Koopmann | 2 | 16 | 10.10 |
| Anni-Yasmin Turhan | 3 | 388 | 53.02 |