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Upcoming events (4)
Semantic Web technologies enable us to represent and query for very complex and heterogeneous datasets. We can add semantics and reason over large bodies of data on the web. However, despite a lot of educational material available, they have failed to achieve mass adoption outside academia.
TypeDB works at a higher level of abstraction and enables developers to be more productive when working with complex data. TypeDB is easier to learn, reducing the barrier to entry and enabling more developers to access semantic technologies. Instead of using a myriad of standards and technologies, we just use one language - TypeQL.
In this talk:
- we will look at how TypeQL compares to Semantic Web standards, specifically RDF, SPARQL RDFS, OWL and SHACL.
- cover questions such as, how do we represent hyper-relations in TypeDB? How to use rdfs:domain and rdfs:range in TypeDB? And how do the modelling philosophies compare?
Graph databases have matured into mainstream technologies and deliver tremendous value to organisations across any industry. They are more flexible than traditional relational databases as they enable us to leverage the relationships in our data in a way relational databases cannot do. In the time of AI and Big Data, this creates opportunities for any organisation.
However, developing with graph databases requires us to overcome plenty of challenges when it comes to data modelling, maintaining consistency of our data among others.
In this talk, we discuss:
- how TypeDB compares to labelled property graphs and how it addresses these challenges. While both technologies share similarities, they are fundamentally different.
- We'll cover how to read & write data
- how to model complex domains
- TypeDB's ability to perform machine reasoning at scale
Building on previous success in this area, the BioCorteX team have used TypeDB to map the therapeutic patent landscape providing unique insights and opportunities. We are able to establish the patent structure for potential therapeutics in a matter of seconds. Importantly, at BioCorteX we are able to quickly highlight the gaps that we refer to as the undiscovered country.
# About the Speaker:
Nik Sharma is the CEO/Co-founder of BioCorteX. He is a clinician scientist at UCL with a specialist interest in neurodegenerative disease and the microbiome. Nik leads the first clinical trial of direct microbiome manipulation in people living with motor neuron disease (MND also known as ALS). The unique multidisciplinary team at BioCorteX combines expertise from neuroscience and aerospace with the explicit aim of developing a new approach to therapeutic optimisation. The four BioCorteX engines are purpose-built to develop enhanced therapeutics to address a range of disorders at scale. BioCorteX’s mission is to cure neurodegenerative diseases by hacking the microbiome and delivering enhanced therapeutics.
Can we improve machines ability to reason by merging traditional knowledge graphs with causal graphs?
Jeff and his team are out to answer this question, using TypeDB. In this talk, Jeff will set out to cover:
- What is a domain knowledge graph?
- What is a causal graph?
- Why it is important to use both causal graphs and domain knowledge graph when building reasoning systems.
- How we are building a tool in node-red that will allow modellers to model knowledge graphs and causal graphs and the output of the model will be TypeDB schema (GQL)