A Semantic Graph Query Language

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.

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A graph query language and its query processing

Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337) ◽

10.1109/icde.1999.754973 ◽

1999 ◽

Cited By ~ 17

Author(s):

Lei Sheng ◽

Z.M. Ozsoyoglu ◽

G. Ozsoyoglu

Keyword(s):

Query Processing ◽

Query Language ◽

Graph Query ◽

Graph Query Language

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An Overview of Graph Indexing and Querying Techniques

Bioinformatics ◽

10.4018/978-1-4666-3604-0.ch011 ◽

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.

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Querying Graph Databases

Advanced Database Query Systems - Advances in Data Mining and Database Management ◽

10.4018/978-1-60960-475-2.ch013 ◽

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.

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

10.1117/12.765531 ◽

2008 ◽

Author(s):

Jeongkyu Lee ◽

M. Emre Celebi

Keyword(s):

Query Language ◽

Temporal Region ◽

Video Databases ◽

Graph Query ◽

Graph Query Language ◽

Spatio Temporal

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Neural factoid geospatial question answering

Journal of Spatial Information Science ◽

10.5311/josis.2021.23.159 ◽

2021 ◽

pp. 65-90

Author(s):

Haonan Li ◽

Ehsan Hamzei ◽

Ivan Majic ◽

Hua Hua ◽

Jochen Renz ◽

...

Keyword(s):

Natural Language ◽

Question Answering ◽

Query Language ◽

Graph Representation ◽

Geospatial Information ◽

Graph Representations ◽

Semantic Graph ◽

Complex Syntax ◽

Question Answering Systems ◽

Semantic Encoding

Existing question answering systems struggle to answer factoid questions when geospatial information is involved. This is because most systems cannot accurately detect the geospatial semantic elements from the natural language questions, or capture the semantic relationships between those elements. In this paper, we propose a geospatial semantic encoding schema and a semantic graph representation which captures the semantic relations and dependencies in geospatial questions. We demonstrate that our proposed graph representation approach aids in the translation from natural language to a formal, executable expression in a query language. To decrease the need for people to provide explanatory information as part of their question and make the translation fully automatic, we treat the semantic encoding of the question as a sequential tagging task, and the graph generation of the query as a semantic dependency parsing task. We apply neural network approaches to automatically encode the geospatial questions into spatial semantic graph representations. Compared with current template-based approaches, our method generalises to a broader range of questions, including those with complex syntax and semantics. Our proposed approach achieves better results on GeoData201 than existing methods.

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