Grakn Architecture

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Nowadays, the amount of biological data available online has proliferated, but
this has been accompanied by enormous challenges arising from the need to
integrate and connect related information from different sources.
Common problems include locating resources, differing data formats, am-
ambiguity and duplication, relationships between data and the sheer volume and
granularity of the information. As yet, there is no standard memorization and
query format for this kind of data, so each resource usually requires a different
approach to be properly handled.

we introduce BioGrakn, based on GRAKN.AI  which is a deductive
database in the form of a knowledge graph, allowing complex data modelling,
verification, scaling, querying and analysis.

The database behind GRAKN.AI uses an ontology to facilitate the modelling of extremely complex data sets, functioning as a data schema constraint to
guarantee information consistency. GRAKN.AI stores data in a way that allows
machines to understand the meaning of information in the complete context of
their relationships. Consequently, the semantic layer of Grakn allows computers
to process complex information more intelligently, with less human intervention

GRAKN.AI

GRAKN.AI is composed of two parts: Grakn (the storage), and Graql (a declar-
ative query language).

Grakn
Grakn is built using several graph computing and distributed computing plat-
forms, such as Apache TinkerPop and Apache Spark. Grakn is designed to be
sharded and replicated over a network of distributed machines. The underlying
data structure of Grakn is that of a labelled, directed hypergraph.




                                              Fig. 1. The GRAKN.AI Architecture

Grakn exposes a high-level knowledge model, allowing developers to repre-
sent their application domain as an ontology, specifying it in terms of entities,
resources, relations, and roles. Grakns ontology modelling constructs include,
but are not limited to, data type hierarchy, relation type hierarchy, bi-directional
relationships, multi-type relationships, N-ary relationships, relationships in re-
lationships, and so on. Therefore, Grakn can model the real world and all the
hierarchies and hyper-relationships contained within it.

Graql
Graql is a declarative, knowledge-oriented graph query language that uses ma-
chine reasoning to retrieve explicitly stored and implicitly derived knowledge
from Grakn.

When using legacy systems, database queries have to define explicitly the
data patterns they are looking for. Graql, on the other hand, will translate
a query pattern into all its logical equivalents and evaluate them against the
database. This includes, but is not limited to, the inference of types, relation-
ships, context, and pattern combination. In this way, Graql can derive implicit
information with concise and intuitive statements, reducing the complexity of
expressing intelligent questions

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