A Graph Query Language for EMF Models

2011 ◽  
pp. 167-182 ◽  
Author(s):  
Gábor Bergmann ◽  
Zoltán Ujhelyi ◽  
István Ráth ◽  
Dániel Varró
Keyword(s):  
Query Language ◽  
Graph Query ◽  
Graph Query Language

SociaLite: An Efficient Graph Query Language Based on Datalog

2015 ◽  
Vol 27 (7) ◽  
pp. 1824-1837 ◽  
Author(s):  
Jiwon Seo ◽  
Stephen Guo ◽  
Monica S. Lam
Keyword(s):  
Query Language ◽  
Graph Query ◽  
Graph Query Language

scholarly journals Introduction to the Principles of Linked Open Data

2017 ◽  
Author(s):  
Jonathan Blaney
Keyword(s):  
Query Language ◽  
Open Data ◽  
Linked Open Data ◽  
Graph Query ◽  
Graph Query Language ◽  
Core Concepts

Introduces core concepts of Linked Open Data, including URIs, ontologies, RDF formats, and a gentle intro to the graph query language SPARQL.

scholarly journals A Semantic Graph Query Language

2006 ◽  
Author(s):  
I Kaplan
Keyword(s):  
Query Language ◽  
Semantic Graph ◽  
Graph Query ◽  
Graph Query Language

An Overview of Graph Indexing and Querying Techniques

2011 ◽  
pp. 71-88 ◽  
Author(s):  
Sherif Sakr ◽  
Ghazi Al-Naymat
Keyword(s):  
Social Networks ◽  
Query Language ◽  
Future Research ◽  
Protein Networks ◽  
Research Directions ◽  
Query Graph ◽  
Graph Indexing ◽  
Graph Query ◽  
Graph Query Language ◽  
Future Research Directions

Recently, there has been a lot of interest in the application of graphs in different domains. Graphs have been widely used for data modeling in different application domains such as: chemical compounds, protein networks, social networks and Semantic Web. Given a query graph, the task of retrieving related graphs as a result of the query from a large graph database is a key issue in any graph-based application. This has raised a crucial need for efficient graph indexing and querying techniques. In this chapter, we provide an overview of different techniques for indexing and querying graph databases. An overview of several proposals of graph query language is also given. Finally, we provide a set of guidelines for future research directions.

An Overview of Graph Indexing and Querying Techniques

2013 ◽  
pp. 222-239
Author(s):  
Sherif Sakr ◽  
Ghazi Al-Naymat
Keyword(s):  
Social Networks ◽  
Query Language ◽  
Future Research ◽  
Protein Networks ◽  
Research Directions ◽  
Query Graph ◽  
Graph Indexing ◽  
Graph Query ◽  
Graph Query Language ◽  
Future Research Directions

Recently, there has been a lot of interest in the application of graphs in different domains. Graphs have been widely used for data modeling in different application domains such as: chemical compounds, protein networks, social networks and Semantic Web. Given a query graph, the task of retrieving related graphs as a result of the query from a large graph database is a key issue in any graph-based application. This has raised a crucial need for efficient graph indexing and querying techniques. In this chapter, we provide an overview of different techniques for indexing and querying graph databases. An overview of several proposals of graph query language is also given. Finally, we provide a set of guidelines for future research directions.

Querying Graph Databases

2011 ◽  
pp. 304-322
Author(s):  
Sherif Sakr ◽  
Ghazi Al-Naymat
Keyword(s):  
Social Networks ◽  
Query Language ◽  
Future Research ◽  
Graph Databases ◽  
Research Directions ◽  
Query Graph ◽  
Graph Indexing ◽  
Graph Query ◽  
Graph Query Language ◽  
Future Research Directions

Recently, there has been a lot of interest in the application of graphs in different domains. Graphs have been widely used for data modeling in different application domains such as: chemical compounds, protein networks, social networks, and Semantic Web. Given a query graph, the task of retrieving related graphs as a result of the query from a large graph database is a key issue in any graph-based application. This has raised a crucial need for efficient graph indexing and querying techniques. This chapter provides an overview of different techniques for indexing and querying graph databases. An overview of several proposals of graph query language is also given. Finally, the chapter provides a set of guidelines for future research directions.

scholarly journals STRG-QL: spatio-temporal region graph query language for video databases

2008 ◽  
Author(s):  
Jeongkyu Lee ◽  
M. Emre Celebi
Keyword(s):  
Query Language ◽  
Temporal Region ◽  
Video Databases ◽  
Graph Query ◽  
Graph Query Language ◽  
Spatio Temporal

TOWARD A UNIFIED ENGLISH-LIKE REPRESENTATION OF SEMANTIC MODELS, DATA, AND GRAPH PATTERNS FOR SUBJECT MATTER EXPERTS

2013 ◽  
Vol 07 (03) ◽  
pp. 215-236 ◽  
Author(s):  
ANDREW CRAPO ◽  
ABHA MOITRA
Keyword(s):  
Open Source ◽  
English Language ◽  
Query Language ◽  
Source Code ◽  
Language Models ◽  
Modeling Languages ◽  
Development Environment ◽  
Ontology Language ◽  
Declarative Modeling ◽  
Graph Query Language

The Semantic Application Design Language (SADL) combines advances in standardized declarative modeling languages based on formal logic with advances in domain-specific language (DSL) development environments to create a controlled-English language that translates directly into the Web Ontology Language (OWL), the SPARQL graph query language, and a compatible if/then rule language. Models in the SADL language can be authored, tested, and maintained in an Eclipse-based integrated development environment (IDE). This environment offers semantic highlighting, statement completion, expression templates, hyperlinking of concepts to their definition, model validation, automatic error correction, and other advanced authoring features to enhance the ease and productivity of the modeling environment. In addition, the SADL language offers the ability to build in validation tests and test suites that can be used for regression testing. Through common Eclipse functionality, the models can be easily placed under source code control, versioned, and managed throughout the life of the model. Differences between versions can be compared side-by-side. Finally, the SADL-IDE offers an explanation capability that is useful in understanding what was inferred by the reasoner/rule engine and why those conclusions were reached. Perhaps more importantly, explanation is available of why an expected inference failed to occur. The objective of the language and the IDE is to enable domain experts to play a more active and productive role in capturing their knowledge and making it available as computable artifacts useful for automation where appropriate and for decision support systems in applications that benefit from a collaborative human-computer approach. SADL is built entirely on open source code and most of SADL is itself released to open source. This paper explores the concepts behind the language and provides details and examples of the authoring and model lifecycle support facilities.

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