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Upcoming events (5)
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.
Text is the medium used to store the tremendous wealth of scientific knowledge regarding the world we live in. However, with its ever-increasing magnitude and throughput, analysing this unstructured data has become a tedious task. This has led to the rise of Natural Language Processing (NLP), as the go-to for examining and processing large amounts of natural language data. This involves the automatic extraction of structured semantic information from unstructured machine-readable text. The identification of these explicit concepts and relationships help in discovering multiple insights contained in text in a scalable and effective way. A major challenge is the mapping of unstructured information from raw texts into entities, relationships and attributes in the knowledge graph. In this talk, we demonstrate how Grakn can be used to create a text mining knowledge graph capable of modelling, storing, and exploring beneficial information extracted from medical literature.
Combinatorial chemistry has produced a huge amount of chemical libraries and data banks which include prospective drugs. Despite all of this progress, the fundamental problem still remains: how do we take advantage of this data to identify the prospective nature of a compound as a vital drug? Traditional methodologies fail to provide a solution to this. Knowledge graphs, however, provide the framework which can make drug discovery much more efficient, effective and approachable. This radical advancement in technology can model biological knowledge complexity as it is found at its core. With concepts such as hyper relationships, type hierarchies, automated reasoning and analytics we can finally model, represent, and query biological knowledge at an unprecedented scale.
Using SQL to query relational databases is easy. As a declarative language, it’s straightforward to write queries and build powerful applications. However, relational databases struggle when working with complex data. When querying such data in SQL, challenges especially arise in the modelling and querying of the data. For example, due to the large number of necessary JOINs, it forces us to write long and verbose queries. Such queries are difficult to write and prone to mistakes. Graql is the query language used in Grakn. Just as SQL is the standard query language in relational databases, Graql is Grakn’s query language. It’s a declarative language, and allows us to model, query and reason over our data. In this talk, we will look at how Graql compares to SQL. Why and when should you use Graql over SQL? How do we do outer/inner joins in Graql? We'll look at the common concepts, but mostly talk about the differences between the two.