Database Reference
In-Depth Information
HNQL
HyperNode Query Language (HNQL) is a query and update language for the
hypernode model [ 25 ]. HNQL consists of a basic set of operators for declarative
querying and updating of hypernodes. In addition to the standard deterministic
operators, HNQL provides several nondeterministic operators, which arbitrarily
choose a member from a set. HNQL is further extended in a procedural style by
adding to the said set of operators an assignment construct, a sequential composi-
tion construct, a conditional construct for making inferences, and, finally, loop and
while loop constructs for providing iteration (or equivalently, recursion) facilities.
9.2.2.4 Semantic Languages
A semantic query language is a query language which is defined for querying a
semantic data model.
The semantic query language presented in [ 26 ] provides a foundation for
extracting information from the semantic graph in which the possible structure of
the graph is described by ontology (Fig. 9.13 ) that defines the vertex types, the edge
types, and how edges may interconnect vertices to form a directed graph. It uses a
query with a specific format containing a function that specifies patterns and
conditions for matching graphs in the database. Figure 9.13 shows an example of
a pattern used by Kaplan query language.
9.2.2.5 Discussion
Querying social networks turns out to be a nontrivial task due to the intrinsic
complexity of the networked data. Also, these kinds of querying focus on a special
type of information. Moreover, the information needs of a community or a social
network are diverse and can be categorized as two types: (1) values or measures such
as the centrality and diameter; (2) information about attributes relations and data
management on social networks. In this section, we present a comparison of the
previous graph database languages and we discuss whether they are well adapted to
query a social network. The existing graph query languages cannot extract all the
Fig. 9.13 Ontology describing the graph ( left ) and the pattern to extract students working on same
topic ( right )
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