The Grakn Warrior's Guide to Augmented Software Development
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Software and software development owe their roots to the field of logic. What began as an attempt to solve a major problem in first-order logic (Entscheidungsproblem), ended with a mathematical model of what it meant to be a computer (Turing machine). That, in turn, lead directly to the birth of software development, which has continued to evolve through the use of abstraction. For the field of software development to continue evolving, it is necessary that this abstraction must also continue. However, given the complex nature of software, this abstraction must now be handled dynamically by the computers themselves. Such is the goal of augmented software development, using artificial intelligence and machine learning to automate code preparation, validation, and generation. In this talk, we will discuss how augmented software development tools can be created using Grakn.
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Description:
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Tomás Sabat:
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The main differences between TypeDB and graph databases can be summarised as:
1. TypeDB provides a concept-level schema with a type system that fully implements the Entity-Relationship (ER) model. Graph databases, on the other hand, use vertices and edges without integrity constraints imposed in the form of a schema
2. TypeDB contains a built-in inference engine - graph databases don’t provide native inferencing capabilities
3. TypeDB is an abstraction over a graph, and leverages a graph database under the hood to create a higher-level model, while graph databases work at different levels of abstraction
Tomás Sabat
Tomás is the Chief Operating Officer at Vaticle. He works closely with TypeDB's open source and enterprise users who use TypeDB to build applications in a wide number of industries including financial services, life sciences, cyber security and supply chain management. A graduate of the University of Cambridge, Tomás has spent the last seven years founding and building businesses in the technology industry.
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Speaker: Jon Thompson
Jon has an MSc in Applied Mathematics and has worked for several years as a Data Scientist in high-throughput biological sequencing. He is the founder of Nodelab, which is on a mission to provide a fully-featured graphical user interface experience for TypeDB.
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2. Key challenges include dealing with uncertain and unknown observations from sensors in the real world and being able to reason about probabilistic information and novel situations.
3. The document proposes using techniques like Markov logic networks and Bayesian networks to help robots reason about uncertainties and handle novelty through probabilistic reasoning.
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Intelligent robotics operating in a dynamic environment require a resilient system. The system should be able to handle known and unknown situations in the world around them and from time to time, be able to assess their own functioning.
Today's systems, look at a task and only activate the specific configuration of components for what is needed to complete that task. The intelligent robotic system we envision however, does not have one specific task but should be able to handle a set of tasks, within a set of dynamic environments - likely to increase in number and complexity over time. This complexity might require a system to keep all components, necessary for all possible tasks, running at all times. Certainly not sustainable or efficient.
An automatic reconfiguration, adapting to environmental changes and tasks confronting the system is the solution that we set out to prove and test.
In this talk, we will take a closer look at how we used Grakn on real-life robotic systems enabling them to be self-aware and to assess and manage themselves at run time.
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How does a conversational assistant understand questions like “What is the most expensive transaction on food I have made?” and where does it get the data to answer this kind of questions correctly? Or imagine a conversation with a bot that helps you manage your bank accounts. A person might ask questions like “On which of those accounts do I have more money?” or “What is the IBAN of the second account you just mentioned?”. How do we give our bot access to the relevant domain knowledge?
In this talk I will explain how I solved this by integrating a conversational assistant, built with Rasa, with a knowledge graph, built with Grakn. Together, these open source libraries help me understand what my bot’s users are talking about.
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Applications running on production have much higher requirements. Not only do they need to be correct, they also need to be "always-on", handle a much bigger user load, and also be secure.
Meet TypeDB Cluster, the TypeDB database for production-scale, built using the Raft replication algorithm. Join us for a walk through the underlying architecture and what value it brings to developers running an application at scale.
Speaker: Ganeshwara Henanda
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He graduated with an MSc of Grid Computing from University of Amsterdam, and has built several large scale distributed and real-time systems throughout his career.
Enabling the Computational Future of Biology.pdf
Computational biology has revolutionised biomedicine. The volume of data it is generating is growing exponentially. This requires tools that enable computational and non-computational biologists to collaborate and derive meaningful insights. However, traditional systems are inadequate to accurately model and handle data at this scale and complexity.
In this talk, we discuss how TypeDB enables biologists to build a deeper understanding of life, and increase the probability of groundbreaking discoveries, across the life sciences.
Speaker: Tomás Sabat
Tomás is the Chief Operating Officer at Vaticle. He works closely with TypeDB's open source and enterprise users who use TypeDB to build applications in a wide number of industries including financial services, life sciences, cybersecurity and supply chain management. A graduate of the University of Cambridge, Tomás has spent the last seven years founding and building businesses in the technology industry.
TypeDB Academy | Inference with Rules
Build your skills and learn how TypeDB's native inference engine works.
Good for:
- Beginners to TypeDB and TypeQL
- Those who have been using TypeDB and want a refresher on inference in TypeDB
- Experienced software engineers
- Those who want to better represent their domain in a model that allows for logical reasoning at the database level
Description:
TypeDB is capable of reasoning over data via pre-defined rules. TypeQL rules look for a given pattern in the database and when found, infer the given queryable fact. The inference provided by rules is performed at query (run) time. Rules not only allow shortening and simplifying of commonly-used queries, but also enable knowledge discovery and implementation of business logic at the database level.
Takeaways:
- Understanding of fundamental components of TypeDB's inference engine and how to write rules for your domain
- Write at least 1 rule for your use case
- Utilise the rule you wrote in a query
Tomás Sabat:
Tomás is the Chief Operating Officer at Vaticle, dedicated to building a strongly-typed database for intelligent systems. He works directly with TypeDB's open source and enterprise users so they can fulfil their potential with TypeDB and change the world. He focuses mainly in life sciences, cyber security, finance and robotics.
TypeDB Academy | Modelling Principles
Join the TypeDB community to learn how we think about data modelling, and how TypeDB's expressivity allows you to model your domain based on logical and object-oriented programming principles.
Good for:
- Engineers, scientists, and technical executives
- Those in a technical field working with complex datasets, and building intelligent systems
- Anyone curious to learn about the expressive power of TypeDB's data model
Description:
We open this training with an exploration into what a schema looks like in TypeDB, starting with clarifying the motivation for the conceptual model in TypeDB, and its relationship to the Enhanced Entity-Relationship model.
Then we break things down a bit more philosophically, delving into: what does it mean to represent data in TypeDB, and how TypeDB allows you to think higher-level, as opposed to join-tables, columns, documents, vertices, edges, and properties.
Takeaways:
- Be able to articulate why TypeDB's data model is so beneficial for complex data, and why we use it to build intelligent systems
- Write a TypeDB schema in TypeQL
- Practice modelling one of your own domains
Tomás Sabat:
Tomás is the Chief Operating Officer at Vaticle, dedicated to building a strongly-typed database for intelligent systems. He works directly with TypeDB's open source and enterprise users so they can fulfil their potential with TypeDB and change the world. He focuses mainly in life sciences, cyber security, finance and robotics.
Beyond SQL - Comparing SQL to TypeQL
Return all employee IDs who sold to a customer based in London, of customer demographic ‘x’. The query traverses the hypergraph to match an employee entity with a given employee ID, a company entity located in London, and a sale relation connecting them as the seller and customer respectively. The employee ID is then returned.
TypeDB Academy- Getting Started with Schema Design
In this TypeDB Academy, we start by gaining an understanding of the fundamental components of TypeDB's type system and what makes it unique. We will see how we can download, install, and run TypeDB, and learn to perform basic database operations.
We'll then explore what a schema looks like in TypeDB, starting with clarifying the motivation for schema, the conceptual schema of TypeDB, and its relationship to the Enhanced Entity-Relationship model.
Good for:
- Beginners to TypeDB and TypeQL
- Those who have been using TypeDB and want a refresher on schema and TypeQL
- Experienced database administrators and software engineers
Takeaways:
- Understanding of fundamental components of TypeDB
- How to download, install, and run TypeDB on your computer
- Be able to articulate why schema is so beneficial when using TypeDB, why we use one, and how it enables a more expressive model
- Write a TypeDB schema in TypeQL
Comparing Semantic Web Technologies to TypeDB
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 does one use rdfs:domain and rdfs:range in TypeDB? And how do the modelling philosophies compare?
Speaker: Tomás Sabat
Tomás is the Chief Operating Officer at Vaticle. He works closely with TypeDB's open source and enterprise users who use TypeDB to build applications in a wide number of industries including financial services, life sciences, cyber security and supply chain management. A graduate of the University of Cambridge, Tomás has spent the last seven years founding and building businesses in the technology industry.
Reasoner, Meet Actors | TypeDB's Native Reasoning Engine
How might we utilise an actor-based execution model to build a powerful yet elegant reasoning engine?
Actors are an asynchronous, inherently parallel framework that form the basis of some of the most computationally heavy systems in the world. By leveraging this in an event-driven model, we can build an execution engine that makes efficient use of all available hardware resources to answer your reasoning queries.
We'll visit the key ideas behind actors, and then walk through how we break reasoning into neat, actor-sized building blocks. As we do this, it will become clear how our marriage of reasoning and actors naturally produces a scalable and elegant execution engine. By examining the problem of reasoning from an actor-based lens, we'll be able to better understand the complexities of reasoning and visualise bottlenecks and optimisations.
Intro to TypeDB and TypeQL | A strongly-typed database
The document introduces TypeDB, a strongly typed database, and its query language TypeQL. It discusses how TypeDB addresses the complexity of modeling and querying data through its use of a powerful type system. The type system provides expressivity to model complex domains while ensuring safety through type validation. It also enables type inference and rule inference to optimize queries over complex data networks.
Graph Databases vs TypeDB | What you can't do with graphs
Developing with graph databases has a number of challenges, such as the modelling of complex schemas, and maintaining data consistency in your database.
In this talk, we discuss how TypeDB addresses these challenges, as well as how it compares to property graph databases. We’ll look at how to read and write data, how to model complex domains, and TypeDB’s ability to infer new data.
The main differences between TypeDB and graph databases can be summarised as:
1. TypeDB provides a concept-level schema with a type system that fully implements the Entity-Relationship (ER) model. Graph databases, on the other hand, use vertices and edges without integrity constraints imposed in the form of a schema
2. TypeDB contains a built-in inference engine - graph databases don’t provide native inferencing capabilities
3. TypeDB is an abstraction over a graph, and leverages a graph database under the hood to create a higher-level model, while graph databases work at different levels of abstraction
Tomás Sabat
Tomás is the Chief Operating Officer at Vaticle. He works closely with TypeDB's open source and enterprise users who use TypeDB to build applications in a wide number of industries including financial services, life sciences, cyber security and supply chain management. A graduate of the University of Cambridge, Tomás has spent the last seven years founding and building businesses in the technology industry.
Pandora Paper Leaks With TypeDB
In this seminar we use TypeDB to open a window on the Pandora Papers, a massive 'data tsunami' based on 11.9 million leaked source documents obtained by the International Consortium of Investigative Journalists (ICIJ).
We will use an automated query builder to get an initial set of results, and then hop from node to node, exploring neighbours and mapping out a suspicious-looking network of offshore shell companies, officers and intermediaries.
Speaker: Jon Thompson
Jon has an MSc in Applied Mathematics and has worked for several years as a Data Scientist in high-throughput biological sequencing. He is the founder of Nodelab, which is on a mission to provide a fully-featured graphical user interface experience for TypeDB.
Strongly Typed Data for Machine Learning
Heterogenous data holds significant inherent context. We would like our machine learning models to understand this context, and utilise this ancillary but critical information to improve the accuracy and versatility of our models.
How can we systematically make use of context in Machine Learning?
We delve in and investigate the knowledge modelling techniques, which applied with the right ML strategies, give us a promising approach for robustly handling heterogeneous data in large knowledge models. We aim to do this in a way that allows us to build any Machine Learning models, including graph learning models like our KGCN.
Speaker: James Fletcher, Vaticle
James comes from a background of Computer Vision, specialising in automated diagnostics. As Principal Scientist at Vaticle, his mission is to demonstrate to the world how traditional symbolic approaches to AI, built-in to TypeDB, can be combined with present-day research in machine learning.
Open World Robotics
1. The document discusses the challenges of developing autonomous robotic systems that can operate in the real, open world as opposed to closed, prepped environments.
2. Key challenges include dealing with uncertain and unknown observations from sensors in the real world and being able to reason about probabilistic information and novel situations.
3. The document proposes using techniques like Markov logic networks and Bayesian networks to help robots reason about uncertainties and handle novelty through probabilistic reasoning.
Combining Causal and Knowledge Modeling for Digital Transformation
Geminos has created a low-code digital transformation platform that combines causal and knowledge modeling. It uses TypeDB as its internal repository. Initial projects are in supply chain and smart manufacturing, with a focus on sustainability.
Speakers: Stuart Frost (CEO), Owen Frost (Analyst)
Stu is the CEO and founder of Geminos. Their focus is on building AI-driven solutions for mid-sized Smart Manufacturing and Logistics companies, that are frustrated by their inability to digitalize their operations at sensible cost.
How can we complete a Knowledge Graph?
A Knowledge Graph is as valuable as the insights we can derive from it. So, what do we do when our Knowledge Graph doesn’t contain the answers? We need to complete it.
We know that Grakn’s logical reasoner can help us to deduce insights. However, when our answers can’t be deduced we need to turn to statistical methods to infer new facts - making predictions inductively, by example. This could be relations, attributes or even rules.
In this talk, we will delve into the advanced graph learning systems that we can construct and use on top of Grakn to create intelligent systems. This is the core of the research that we conduct at Grakn Labs - all of which is made available in KGLIB.
Text-Mined Data in a Knowledge Graph
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.
Introduction to Knowledge Graphs with Grakn and Graql
Cognitive/AI systems process knowledge that is far too complex for current databases. They require an expressive data model and an intelligent query language to perform knowledge engineering over complex datasets.
In this talk, we will discuss how Grakn, a database to organise complex networks of data and make it queryable, provides the knowledge graph foundation for intelligent systems to manage complex data.
We will discuss how Graql, Grakn's reasoning (through OLTP) and analytics (through OLAP) query language, provides the tools required to do the job: a knowledge schema, a logical inference language, a distributed analytics framework.
And finally, we will discuss how Graql’s language serves as unified data representation of data for cognitive systems.
Power of the Run Graph
We explain how we use Grakn as part of a wider solution to deliver next generation Data Operations (Data Ops) tooling, enabling us to deliver sophisticated "Run Graph Analytics".
The Run Graph is a component to passively track and trace our data assets as they move across the organisation, and is used to quickly reverse engineer our global flows of data to better plan change and understand hidden dependencies. When operational failures do arise, we demonstrate how Grakn quickly allows us to assess the inferred impacts downstream, and to prioritise and communicate the impacts of outages to stakeholders.
Increase Self Awareness on Robotic Systems
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Today's systems, look at a task and only activate the specific configuration of components for what is needed to complete that task. The intelligent robotic system we envision however, does not have one specific task but should be able to handle a set of tasks, within a set of dynamic environments - likely to increase in number and complexity over time. This complexity might require a system to keep all components, necessary for all possible tasks, running at all times. Certainly not sustainable or efficient.
An automatic reconfiguration, adapting to environmental changes and tasks confronting the system is the solution that we set out to prove and test.
In this talk, we will take a closer look at how we used Grakn on real-life robotic systems enabling them to be self-aware and to assess and manage themselves at run time.
Grounding Conversational AI in a Knowledge Base
How does a conversational assistant understand questions like “What is the most expensive transaction on food I have made?” and where does it get the data to answer this kind of questions correctly? Or imagine a conversation with a bot that helps you manage your bank accounts. A person might ask questions like “On which of those accounts do I have more money?” or “What is the IBAN of the second account you just mentioned?”. How do we give our bot access to the relevant domain knowledge?
In this talk I will explain how I solved this by integrating a conversational assistant, built with Rasa, with a knowledge graph, built with Grakn. Together, these open source libraries help me understand what my bot’s users are talking about.
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TypeDB Academy- Getting Started with Schema Design
TypeDB Academy- Getting Started with Schema Design
Reasoner, Meet Actors | TypeDB's Native Reasoning Engine
Reasoner, Meet Actors | TypeDB's Native Reasoning Engine
Intro to TypeDB and TypeQL | A strongly-typed database
Intro to TypeDB and TypeQL | A strongly-typed database
Graph Databases vs TypeDB | What you can't do with graphs
Graph Databases vs TypeDB | What you can't do with graphs
Combining Causal and Knowledge Modeling for Digital Transformation
Combining Causal and Knowledge Modeling for Digital Transformation
Introduction to Knowledge Graphs with Grakn and Graql
Introduction to Knowledge Graphs with Grakn and Graql
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- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
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