pSPARQL: A Querying Language for Probabilistic RDF Data

• Main Author: Hong Fang
• Format: Article
• Language: English
• Published: Hindawi-Wiley 2019-01-01
• Series: Complexity
• Online Access: http://dx.doi.org/10.1155/2019/8258197

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.