GRAKN.AI
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https://grakn.ai/ GRAKN.AI™ is a distributed knowledge graph that brings knowledge ontologies and transactional data together to enable intelligent querying of data. Querying is performed through the language: Graql, a declarative, knowledge-oriented graph query language for retrieving explicitly stored and implicitly derived information, as well as to perform graph analytics and automated reasoning. Grakn allows you to model your domain using the well-known Entity-Relationship model at its full expressivity. It is composed of entity types, relationship types, and attribute types. Unlike other modelling languages, Grakn allows you to define type hierarchies, hyper-entities, hyper-relations, and rules to build rich knowledge models. Grakn allows you to define rules in your knowledge schema, which extends the expressivity of your model as it enables the system to derive new conclusions when a certain logical form in your dataset is satisfied. Like functions in programming, that rules can chain itself to another, creating abstractions of behaviour at the data level. Grakn's inference facility translates one query into all of its other interpretations. This happens through two mechanisms: type-based and rule-based inference. Not only does this derive new conclusions and uncovers relationships that would otherwise be hidden, but it also enables the abstraction of complex patterns into simple queries. Analytics: Distributed analytics is a set of scalable algorithms that allows you to perform computation over large amounts of data in a distributed fashion. They tend to belong to the family of MapReduce or Pregel algorithms (BSP). Often, this requires the implementation of challenging algorithms. In Grakn, these distributed analytics algorithms are built-in as native functionalities of the language. Unlike other modelling languages, Grakn allows you to define type hierarchies, hyper-entities, hyper-relations, and rules to build rich knowledge models.