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Nowada ys , the amount of biological data av ailable 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 v olume and granularity of the information. As y et, 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 deductiv e 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 wa y...

GRAKN.AI is an open-source, distributed knowledge graph for knowledge-oriented systems. It is an evolution of the relational database for highly interconnected data as it provides a concept-level schema that fully implements the Entity-Relationship (ER) model.  Grakn provides the knowledge foundation for cognitive and intelligent (e.g. AI) systems, by providing an intelligent language for modelling, transactions and analytics. Being a distributed database, Grakn is designed to scale over a network of computers through partitioning and replication Let me explain more on this Grakn DB. I have Country, language and City tables where it has below columns for City. | Column | Type | Null | Key | Default | Extra | +-------------+----------+------+-----+---------+----------------+ | ID | int(11) | NO | PRI | NULL | auto_increment | | Name | char(35) | NO | | | | | CountryCode | char(3) | NO | MUL | ...