Using Queries as Schema-Templates for Graph Databases

Using Queries as Schema-Templates for Graph Databases In contrast to heavy-handed ER-style data models in relational databases, knowledge graphs (or graph databases) capture entity semantics in terms of entity relationships and properties following a simple collect-as-you-go model. While this allows for a more flexible and dynamically adaptable knowledge representation, it comes at the price of more complex querying: with varying degrees of information sparsity, it will gradually become more difficult to figure out what an entity actually represents. Thus, matching the intended schema as specified by a query against actually occurring entity patterns in the graph database needs severe attention on a conceptual level. In this article, we analyze graph patterns as schema information from a graph pattern matching perspective. We argue that every query consists of a mixture of conceptual information (how entities are structured) together with evaluation information (further dependencies and constraints on data) and that this mixture is not always easy to divide. To arrive at truly schema-aware graph query processing, we propose several matching mechanisms, each mandating a specific semantic meaning of the graph pattern, and discuss their practical applicability. Keywords Graph databases · Graph queries · Conceptual modeling · Pattern matching 1Introduction a dynamically growing collect-as-you-go manner. A good example is http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Datenbank-Spektrum Springer Journals

Using Queries as Schema-Templates for Graph Databases

Loading next page...
 
/lp/springer_journal/using-queries-as-schema-templates-for-graph-databases-8sRJOT0jie
Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2018 by Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature
Subject
Computer Science; Information Storage and Retrieval; Data Mining and Knowledge Discovery; Database Management; Data Structures, Cryptology and Information Theory; IT in Business; Computer Systems Organization and Communication Networks
ISSN
1618-2162
eISSN
1610-1995
D.O.I.
10.1007/s13222-018-0286-9
Publisher site
See Article on Publisher Site

Abstract

In contrast to heavy-handed ER-style data models in relational databases, knowledge graphs (or graph databases) capture entity semantics in terms of entity relationships and properties following a simple collect-as-you-go model. While this allows for a more flexible and dynamically adaptable knowledge representation, it comes at the price of more complex querying: with varying degrees of information sparsity, it will gradually become more difficult to figure out what an entity actually represents. Thus, matching the intended schema as specified by a query against actually occurring entity patterns in the graph database needs severe attention on a conceptual level. In this article, we analyze graph patterns as schema information from a graph pattern matching perspective. We argue that every query consists of a mixture of conceptual information (how entities are structured) together with evaluation information (further dependencies and constraints on data) and that this mixture is not always easy to divide. To arrive at truly schema-aware graph query processing, we propose several matching mechanisms, each mandating a specific semantic meaning of the graph pattern, and discuss their practical applicability. Keywords Graph databases · Graph queries · Conceptual modeling · Pattern matching 1Introduction a dynamically growing collect-as-you-go manner. A good example is

Journal

Datenbank-SpektrumSpringer Journals

Published: May 31, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off