Grakn Poland Engineers

Good for: Engineers, scientists, analysts and strategists Those in a technical field working with or close to complex datasets, models and domains Anyone curious about the power of Grakn’s knowledge base (knowledge graph) for their domain Innovators and builders who want to model knowledge, the world around them, in a way and in a system that allows for logical reasoning and inferred relationships at the database level Description: We start this training with an exploration into what schema looks like within Grakn, starting with clarifying the motivation for schema, the conceptual schema of Grakn, and its relationship to the Enhanced Entity-Relationship model. Then we break things down a bit more philosophically. What does it mean to model a knowledge domain - specifically when modelling in Grakn which allows for a much closer representation to true domain. Takeaways: - Be able to articulate why schema is so beneficial when using Grakn, why we use one and how it enables a more expressive model. - Write a Grakn schema in Graql. - Practice modelling one of your own domains and begin to write the model in Graql

Covid has had a massive impact on the financial services industry. Existing changes in technology, regulation, and geopolitics are radically changing the data landscape. Faced with this environment, financial services firms can take full strategic advantage of the most cutting-edge data infrastructure technologies to thrive in these unprecedented times. In this talk, we’ll explore how Grakn can be used to make the most of current challenges. We’ll explore how to unite data silos into a federated model and analyse data across an enterprise in real-time, enabling use cases such as customer 360, risk & compliance and anti-money laundering.

A Knowledge Graph is as valuable as the insights we can derive from it. So, what do we do when our Knowledge Graph doesn’t contain the answers? We need to complete it. We know that Grakn’s logical reasoner can help us to deduce insights. However, when our answers can’t be deduced we need to turn to statistical methods to infer new facts - making predictions inductively, by example. This could be relations, attributes or even rules. In this talk, we will delve into the advanced graph learning systems that we can construct and use on top of Grakn to create intelligent systems. This is the core of the research that we conduct at Grakn Labs - all of which is made available in KGLIB.

Cognitive/AI systems process knowledge that is far too complex for current databases. They require an expressive data model and an intelligent query language to perform knowledge engineering over complex datasets. In this talk, we will discuss how Grakn, a database to organise complex networks of data and make it queryable, provides the knowledge graph foundation for intelligent systems to manage complex data. We will discuss how Graql, Grakn's reasoning (through OLTP) and analytics (through OLAP) query language, provides the tools required to do the job: a knowledge schema, a logical inference language, a distributed analytics framework. And finally, we will discuss how Graql’s language serves as unified data representation of data for cognitive systems.