pSPARQL: A Querying Language for Probabilistic RDF Data

المؤلف

Fang, Hong

المصدر

Complexity

العدد

المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-7، 7ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2019-03-26

دولة النشر

مصر

عدد الصفحات

7

التخصصات الرئيسية

الفلسفة

الملخص 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.

نمط استشهاد جمعية علماء النفس الأمريكية (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

نمط استشهاد الجمعية الأمريكية للغات الحديثة (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

نمط استشهاد الجمعية الطبية الأمريكية (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

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-1132869