This project introduces a novel model: the Knowledge Graph Convolutional Network (KGCN). The principal idea of this work is to forge a bridge between knowledge graphs, automated logical reasoning, and machine learning, using Grakn as the knowledge graph. A KGCN can be used to create vector representations, embeddings, of any labelled set of Grakn Things via supervised learning. There are many benefits to storing complex and interrelated data in a knowledge graph, not least that the context of each datapoint can be stored in full. However, many existing machine learning techniques rely upon the existence of an input vector for each example.

Aiden.ai - Founded in 2016, Aiden.ai is an AI powered virtual marketing analyst to enable mobile app marketers to focus on what matters most. Aiden has raised a total of $2.4m to date over 2 rounds. The latest funding came from a Seed round announced in November 2018, led by Partech along with angels Sophia Bendz and Nicolas Pinto. The company was co-founded by CEO Marie Outtier, who has 8 years of ad tech startup experience, and CTO PJ Camillieri, who previously spent 10 years at Apple. Auquan - Recently graduated from Techstars London, Auquan is a platform to crowdsource data-driven trading strategies from a community of data scientists, developers, and machine learning engineers.

Aiden - Founded in 2016, Aiden is an AI powered virtual marketing analyst to enable mobile app marketers to focus on what matters most. Aiden uses machine reading to analyse campaign data and offers one-click optimisation to proactively make changes to improve ROI and streamline reporting. Aiden has raised a total of $2.4m to date over 2 rounds. The latest funding came from a Seed round announced in November 2018, led by Partech along with angels Sophia Bendz and Nicolas Pinto. The company was co-founded by CEO Marie Outtier, who has 8 years of ad tech startup experience, and CTO PJ Camillieri, who previously spent 10 years at Apple.

It is a distributed knowledge base designed specifically to handle complex data in knowledge-oriented system -- a task for which traditional database technologies are not the best fit. To ensure that their internal knowledge is the most up-to-date and relevant, AI systems are always hungry for newly updated data. Working seamlessly with streaming data is therefore useful for building knowledge-oriented systems. In this blog post, we will look at how to stream public tweets into Grakn's distributed knowledge base. In my previous post, we covered data insertion aspects, such as defining a schema as well as retrieving and inserting Twitter data.

Chatbots are programs that simulate talking to a human. They are usually designed with a goal in mind -- in our case, finding movies. Finding the right combination of tools to build a chatbot can be challenging. There are, of course, many chatbot platforms that already exist. These tend to be bot-as-a-service platforms, where you can build, adapt and deploy your service in the cloud.

GRAKN.AI joins the Google Cloud Technology Partner Program GRAKN.AI is pleased to announce that we have recently joined the Google Cloud Technology Partner Program to provide GRAKN.AI Cloud. AI systems need a knowledge base to manage their data because they produce and consume more complex information than average software. GRAKN.AI is a database in the form of a distributed knowledge base with a reasoning query language that allows you to model, verify, scale, query and analyse complex data easily. By providing a query language that uses machine reasoning to uncover knowledge too complex for human cognition to infer, GRAKN.AI allows organisations to grow their competitive advantage, all the while reducing engineering time, cost, and complexity. Google Cloud Platform is a cloud computing service by Google that offers hosting on the same supporting infrastructure that Google uses internally for end-user products like Google Search and YouTube. It is the engine behind many innovative cloud solutions, such as Spotify and Feedly.