Querying Rich Ontologies by Exploiting the Structure of Data
Autor(en):
Zusammenfassung
Ontology-based data access (OBDA) has emerged as a paradigm for accessing heterogeneous and incomplete data sources. A fundamental reasoning service in OBDA, the ontology mediated query (OMQ) answering has received much attention from the research community. However, there exists a disparity in research carried for OMQ algorithms for lightweight DLs which have found their way into practical implementations, and algorithms for expressive DLs for which the work has had mainly theoretical oriented goals. In the dissertation, a technique that leverages the structural properties of data to help alleviate the problems that typically arise when answering the queries in expressive settings is developed. In this paper, a brief summary of the technique along with the different algorithms developed for OMQ for expressive DLs is given.
- Vollständige Referenz
- BibTeX
Bajraktari, L.,
(2020).
Querying Rich Ontologies by Exploiting the Structure of Data.
KI - Künstliche Intelligenz: Vol. 34, No. 3.
Springer.
(S. 395-398).
DOI: 10.1007/s13218-020-00672-9
@article{mci/Bajraktari2020,
author = {Bajraktari, Labinot},
title = {Querying Rich Ontologies by Exploiting the Structure of Data},
journal = {KI - Künstliche Intelligenz},
volume = {34},
number = {3},
year = {2020},
,
pages = { 395-398 } ,
doi = { 10.1007/s13218-020-00672-9 }
}
author = {Bajraktari, Labinot},
title = {Querying Rich Ontologies by Exploiting the Structure of Data},
journal = {KI - Künstliche Intelligenz},
volume = {34},
number = {3},
year = {2020},
,
pages = { 395-398 } ,
doi = { 10.1007/s13218-020-00672-9 }
}
Sollte hier kein Volltext (PDF) verlinkt sein, dann kann es sein, dass dieser aus verschiedenen Gruenden (z.B. Lizenzen oder Copyright) nur in einer anderen Digital Library verfuegbar ist. Versuchen Sie in diesem Fall einen Zugriff ueber die verlinkte DOI: 10.1007/s13218-020-00672-9
Haben Sie fehlerhafte Angaben entdeckt? Sagen Sie uns Bescheid: Feedback abschicken
Mehr Information
ISSN: 1610-1987
Datum: 2020
Typ: Text/Journal Article

