pSPARQL: A Querying Language for Probabilistic RDF Data
Author
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-03-26
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
More and more linked data (taken as knowledge) can be automatically generated from nonstructured data such as text and image via learning, which are often uncertain in practice.
On the other hand, most of the existing approaches to processing linked data are mainly designed for certain data.
It becomes more and more important to process uncertain linked data in theoretical aspect.
In this paper, we present a querying language framework for probabilistic RDF data (an important uncertain linked data), where each triple has a probability, called pSRARQL, built on SPARQL, recommended by W3C as a querying language for RDF databases.
pSPARQL can support the full SPARQL and satisfies some important properties such as well-definedness, uniqueness, and some equivalences.
Finally, we illustrate that pSPARQL is feasible in expressing practical queries in a real world.
American Psychological Association (APA)
Fang, Hong. 2019. pSPARQL: A Querying Language for Probabilistic RDF Data. Complexity،Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1132869
Modern Language Association (MLA)
Fang, Hong. pSPARQL: A Querying Language for Probabilistic RDF Data. Complexity No. 2019 (2019), pp.1-7.
https://search.emarefa.net/detail/BIM-1132869
American Medical Association (AMA)
Fang, Hong. pSPARQL: A Querying Language for Probabilistic RDF Data. Complexity. 2019. Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1132869
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1132869