Inference is something we humans do all the time. Given a set of facts about the world, we derive new ones using some form of inference. Automated reasoning has been studied extensively but its value in providing a more powerful abstraction layer for database languages has been overlooked so far.
This talk explores deductive inference in Grakn, a hyper-relational database that has automated inference as one of its core features. Rather than defining SQL views or writing ad hoc code, in Grakn we can define logical rules that provide a more intuitive way to describe higher level domain concepts. In the talk we give a quick overview of computational logic semantics and of top-down and bottom-up inference algorithms. Then, after introducing some preliminary Grakn concepts, we show how logical rules are resolved in a query.
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Logical Inference in a Hyper-Relational database
Domenico Corapi is a Lead Engineer at Grakn. He previously worked at Skyscanner, Microsoft, Citi, and Bloomberg specialising in distributed systems, web services, and large scale data processing. During his years in academia he received a PhD in Computing at Imperial College London and he has authored a number of publications on computational logic, multi-agent systems, and rule-based machine learning.