Text Mined Knowledge Graphs

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#### Text Mining Knowledge Graphs
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 un-structured 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.

#### Syed Irtaza Raza, Software and Biomedical Engineer @ Grakn Labs
Syed is a Software and Biomedical Engineer at Grakn, primarily working on introducing the world on how to use a knowledge graph such as Grakn to build cognitive/intelligent systems in the Biomedical domain. To achieve this, he is implementing innovative examples as templates and ideas for how clients and community members may apply in their own specific projects of any field.

With a background in Electrical, Software and Biomedical Engineering, Syed’s mission is to discover and implement intelligent biomedical tools that are only possible with Grakn as a knowledge graph.

Timings: 19.30 - 20.00: Networking & Drinks

From 20.00: Modelling and Working with Text Mining Data