Computing Probabilistic Queries in the Presence of Uncertainty via Probabilistic Automata

Andronikos, Theodore; Singh, Alexander; Giannakis, Konstantinos; Sioutas, Spyros

doi:10.1007/978-3-319-74875-7_7

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…This line of thought was further pursued in [11], where a connection between SPARQL queries involving transitive predicates and ω-regular languages, i.e., the analog of regular languages in case of infinite words, and finite automata accepting infinite inputs is established. Tools and techniques from the theory of probabilistic automata can also be used when dealing with data characterized by a certain degree of uncertainty, e.g., biomedical data, as was demonstrated in [12].…”

Section: Related Workmentioning

confidence: 99%

Classification of Sparql Queries Into Equivalence Classes of Relevant Queries

Andronikos¹

2017

ijarcs

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This paper is inspired by ideas from the field of theoretical Mathematics, used for the partitioning of abstract spaces into equivalence classes, and applies analogous concepts in order to propose a classification of SPARQL queries into equivalence classes. The novel concepts of relevant queries and covering query are introduced in a manner appropriate for the study of SPARQL queries. These new definitions shed new light on the relations among SPARQL queries. They enable the formal identification of similar queries and this leads to the partition of SPARQL queries into equivalence classes of relevant queries. This work also discusses how the covering query relating two or more relevant queries can be useful from the perspective of computational cost when evaluating composite queries composed of simpler relevant queries. Hence, the introduction of the concept of relevance between queries provides not only obvious theoretical advantages, but also concrete practical ones, which in many cases have the potential to lower the computational cost of query evaluation.

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Section: Related Workmentioning

confidence: 99%

Classification of Sparql Queries Into Equivalence Classes of Relevant Queries

Andronikos¹

2017

ijarcs

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“…Reference [16] presents a probabilistic knowledge base system, ARCHIMEDESONE, for query answering with inference by scaling up the knowledge expansion and statistical inference algorithms. Reference [17] proposes a probabilistic automata-based framework of query evaluation in the presence of uncertainty efficiently.…”

Section: Introductionmentioning

confidence: 99%

pSPARQL: A Querying Language for Probabilistic RDF Data

Fang

2019

Complexity

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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.

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Computing Probabilistic Queries in the Presence of Uncertainty via Probabilistic Automata

Cited by 2 publications

References 19 publications

Classification of Sparql Queries Into Equivalence Classes of Relevant Queries

Classification of Sparql Queries Into Equivalence Classes of Relevant Queries

pSPARQL: A Querying Language for Probabilistic RDF Data

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