Deutsche Telecom Expert System - Router Troubleshooting
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The presentation given at a Grakn meetup, held at Deutsche Telecom's hubraum in Berlin on 29 September 2018. The presentation details one example of how to construct an expert system at the database level.
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Knowledge graph convolutional networks - London 2018
As humans we use our knowledge, our reasoning and our understanding of situational context to make accurate predictions about the world around us; machine learning doesn’t typically make use of any of this rich information.
The ability to leverage highly interrelated data will yield a step-change in the quality and complexity of predictions that can be made for the same volume of data.
We present Knowledge Graph Convolutional Networks: a method for performing machine learning over a Grakn Knowledge Graph, which captures micro-context and macro-context for any Concept within the graph.
This methodology demonstrates how we can usably combine knowledge, learning and reasoning to build systems that start to look truly intelligent.
Associated blog post:
https://blog.grakn.ai/knowledge-graph-convolutional-networks-machine-learning-over-reasoned-knowledge-9eb5ce5e0f68
Associated video:
https://youtu.be/Jx_Twc75ka0
This is a clip from the Grakn London Meetup at the Royal Academy of Engineering (November 2018). Join the community: grakn.ai/community
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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 Meetup event, we will introduce GRAKN.AI, a distributed hyper-relational database for knowledge engineering, to Amsterdam's engineering community.
Grakn provides the knowledge base foundation for intelligent systems to manage complex data. We will also introduce Graql: Grakn's reasoning (through OLTP) and analytics (through OLAP) query language. Graql provides the tools required to do knowledge engineering: an expressive schema for knowledge modelling, reasoning transactions for real-time inference, distributed algorithms for large-scale analytics, and optimisation of query execution. And finally, we will discuss how Graql’s language serves as unified data representation of data for cognitive systems.
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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.
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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.
This is a clip from the Grakn London Meetup at the Royal Academy of Engineering (March 2019). Join the community: www.grakn.ai/community
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Cognitive and 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.
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It has become increasingly difficult to design models for complex diseases which accommodate genetic individual variabilities. We need efficient and successful techniques to integrate, manage, maintain and visualise sizeable datasets. These datasets can be from a multitude of sources, having many various formats and levels of confidentiality. This forms the need to accumulate all this knowledge in one single structured architecture - a knowledge graph.
In this talk, we aspire to inspire a strategy, motivated by translational bioinformatics, to demonstrate how to fulfil the promises of Precision Medicine using Grakn.
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Artificial Intelligence in real world applications needs the notion of an open world assumption; they need to be able to work in unknown situations. However, most current image processing application cannot handle unknown situations and objects. Unknown objects are classified as background and systems are only able to classify images into pretrained and predefined object classes.
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The ability to leverage highly interrelated data will yield a step-change in the quality and complexity of predictions that can be made for the same volume of data.
We present Knowledge Graph Convolutional Networks: a method for performing machine learning over a Grakn Knowledge Graph, which captures micro-context and macro-context for any Concept within the graph.
This methodology demonstrates how we can usably combine knowledge, learning and reasoning to build systems that start to look truly intelligent.
Associated blog post:
https://blog.grakn.ai/knowledge-graph-convolutional-networks-machine-learning-over-reasoned-knowledge-9eb5ce5e0f68
Associated video:
https://youtu.be/Jx_Twc75ka0
This is a clip from the Grakn London Meetup at the Royal Academy of Engineering (November 2018). Join the community: grakn.ai/community
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Grakn provides the knowledge base foundation for intelligent systems to manage complex data. We will also introduce Graql: Grakn's reasoning (through OLTP) and analytics (through OLAP) query language. Graql provides the tools required to do knowledge engineering: an expressive schema for knowledge modelling, reasoning transactions for real-time inference, distributed algorithms for large-scale analytics, and optimisation of query execution. And finally, we will discuss how Graql’s language serves as unified data representation of data for cognitive systems.
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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.
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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.
This is a clip from the Grakn London Meetup at the Royal Academy of Engineering (March 2019). Join the community: www.grakn.ai/community
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Cognitive and AI applications consume data that is far too complex for current databases. These systems require an expressive data model and an intelligent query language to perform knowledge engineering over complex datasets. GRAKN.AI is a database to organise such complex networks of data.
Systems biology is one of the domains that produces huge amounts of data which presents integration challenges due to their complex nature. As understanding the complex relationships among these biological data is one of the key goals in biology, solutions are necessary that speed up the integration and querying of such data.
However, analysing large volumes of this biological data through traditional database systems is troublesome and challenging. In this talk, we will demonstrate how integrating a sequencing algorithm with a Grakn knowledge graph leads to valuable new insights of our data at scale.
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The success or failure of any modern organisation relies greatly on the way they leverage their data. However, most institutions and organisations have no way to aggregate the magnitude and complexity of their disparate data catalogs. They require a unified representation of their data which represents their specific domain truthfully as well as conceptually. In this talk, we introduce how using a knowledge graph addresses these problems in the field of Precision Medicine.
Precision medicine aims at establishing personalised context-centred therapies and diagnostics. This is done by integrating complex and disparate data repositories relating to environmental and molecular origins of diseases.
It has become increasingly difficult to design models for complex diseases which accommodate genetic individual variabilities. We need efficient and successful techniques to integrate, manage, maintain and visualise sizeable datasets. These datasets can be from a multitude of sources, having many various formats and levels of confidentiality. This forms the need to accumulate all this knowledge in one single structured architecture - a knowledge graph.
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This is a clip from the Grakn London Meetup in February 2019. Join the community: www.grakn.ai/community
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https://bigscience.huggingface.co/
EN: Presentation of the BigScience project: a research initiative launched by HuggingFace and aiming to build a large language model (inspired by OpenAI and GPTx) over multiple languages and a very large processing cluster. The participants plan to investigate the dataset and the model from all angles: bias, social impact, capabilities, limitations, ethics, potential improvements, specific domain performances, carbon impact, general AI/cognitive research landscape.
FR : Présentation du projet Bigscience : un projet de recherche ouvert lancé par HuggingFace et qui a pour objectif de contruire un modèle de langue (ie un peu comme openAI et GPT-3) mais en explorant les problèmes liés au jeux de données et au modèle selon les angles des biais cognitifs, de l'impact social et environemental, des limites éthiques, des possibles gain de performance et de l'impact général de ce type d'approche lorsque le but n'est pas seulement "d'avoir un plus gros modèle".
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Building Biomedical Knowledge Graphs for In-Silico Drug Discovery
The rapid development and spread of analytical tools in the biomedical sciences has produced a variety of information about all sorts of biological components and their functions. Though important individually, their biological characteristics need to be understood in relation to the interactions they have with other biological components, which requires the integration of vast amounts of complex, semantically-rich, heterogenous data.
Traditional systems are inadequate at accurately modelling and handling data at this scale and complexity, making solutions that speed up the integration and querying of such data a necessity.
In this talk, we present various approaches being used in organisations to build biomedical computational pipelines to address these problems using tools such as Machine Learning and TypeDB. In particular, we discuss how to create an accurate and scalable semantic representation of molecular level biomedical data by presenting examples from drug discovery, precision medicine and competitive intelligence.
Speaker: 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.
Loading Huge Amounts of Data
Loading a lot of data into a graph database is not a trivial exercise. TypeDB Loader (formerly known as GraMi) was developed to allow large-scale data import into TypeDB, a strongly-typed database. Recent improvements have immensely simplified the configuration interface to allow for easier data importing, while maintaining features and the promise of loading huge amounts of data into TypeDB as fast as possible.
Natural Language Interface to Knowledge Graph
Natural language interfaces (NLI) offer end-users an easy and convenient way to query ontology-based knowledge graphs. They automatically generate database queries based on their natural language inputs, avoiding the need for the end user to learn different query languages. NLIs can be used with REST APIs to facilitate and enrich the interactions with knowledge graphs, in domains such as interactive root cause analysis (RCA), dynamic dashboard generation, and Online Transactional Processing (OLTP).
In this talk, you'll learn about a natural language interface built with a TypeDB server running on Raspberry Pi4. This application offers a conversational bot assistant with Cisco Webex for an efficient and flexible way to facilitate human-machine interactions. In particular, this talk will demonstrate how natural language inputs are translated into TypeQL queries using Abstract Syntax Trees that represent the syntactic structure discovered during the Named Entity Recognition (NER) analysis of the textual inputs provided by Rasa 2.X running on an Intel Celeron J3455 miniPC.
A Data Modelling Framework to Unify Cyber Security Knowledge
Cyber security companies collect massive amounts of heterogenous data coming from a huge number of sources. These describe hundreds of different data types, such as vulnerabilities, observables, incidents, and malwares. While this data is highly complex (with many types of relations, type hierarchies, and rules), its structure doesn't significantly change between organisations. However, without a publicly available data model, organisations end up modelling the same data in different ways: in other words, reinventing the wheel, and wasting their resources. This modelling complexity makes scaling cyber security applications extremely difficult.
That's why efforts are underway to provide ready-made solutions for typical cyber security use cases which provide the flexibility to expand for specific requirement of individual setups. The combination of those efforts have created a lot of inter-related knowledge silos (e.g. CVE, CAPEC, CWE, CVSS, Cocoa, MITRE, VERIS, STIX, MAEC). To unify these silos, various ontologies have been proposed by researchers, with different levels of granularity - from specific use cases like defence exercises, to more comprehensive cases like the UCO project.
During this talk, you’ll learn about the OmnibusCyber Project, an open-source, ready-made solution that aggregates cyber security knowledge silos, based on TypeDB. TypeDB’s framework offers the expressivity, safety, and inference properties required to implement a knowledge graph without the complexity associated with the OWL/RDF semantic frameworks.
Unifying Space Mission Knowledge with NLP & Knowledge Graph
Synopsis
The number of space missions being designed and launched worldwide is growing exponentially. Information on these missions, such as their objectives, orbit, or payload, is disseminated across various documents and datasets. Facilitating access to this information is key to accelerating the design of future missions, enabling experts to link an application to a mission, and following various stakeholders' activities.
This presentation introduces recent research done at the ESA to combine the latest Language Models with Knowledge Graphs, unifying our knowledge on space missions. Language Models such as GPT-3 and BERT are trained to understand the patterns of human (natural) language. These models have revolutionised the field of NLP, the branch of AI enabling machines to understand human language in all its complexity. In this work, key information on a mission is parsed from documents with the GPT-3 model, and the parsed data is then migrated to a TypeDB Knowledge Graph to be easily queried. Although this work focuses on an application in the space sector, the method can be transferred to other engineering fields.
Presenters
Dr. Audrey Berquand is a Research Fellow at the ESA. Her research aims at enhancing space mission design and knowledge management with text mining, NLP, and Knowledge Graphs. She was awarded her PhD in 2021 from the University of Strathclyde (Scotland) for her thesis on “Text Mining and Natural Language Processing for the Early Stages of Space Mission Design”. Audrey has a background in space systems engineering, she holds an MSc in Aerospace Engineering from the Royal Institute of Technology KTH (Sweden), and a diplôme d'ingénieur from the EPF Graduate School of Engineering (France). Before diving into the world of AI, she spent 3 years at ESA being involved in the early design phases of future Earth Observation missions.
Ana Victória Ladeira works with Knowledge Management at the ESA, using automated methods to exploit the information contained in the piles and piles of documents that ESA generates every day. With a Masters degree in Data Science from Maastricht University, Ana is particularly excited about how NLP methods can help large organizations connect different documents and highlight the bigger picture over a big universe of data sources, as well as using Knowledge Graphs to help connect people to the expertise and information they need.
The Next Big Thing in AI - Causality
Talk Summary:
State of the art AI approaches can struggle to create solutions which provide accurate results that stand the test of time. They are also plagued by problems such as bias and a lack of explainability. Causal AI addresses these key problems and is at the center of the Geminos Causeway platform, which is built on TypeDB.
This webinar will give you an introduction to why causal AI is so important, and how you can start to use it to drive more value for your organisation.
Speaker: Stuart Frost
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. Stu has 30 years’ experience in founding and leading successful data management and analytics startups, starting at 26 when he founded SELECT Software Tools, and led the company to a NASDAQ IPO in 1996. He then founded DATAllegro in 2003 which was acquired by Microsoft.
Building a Cyber Threat Intelligence Knowledge Graph
Knowledge of cyber threats is a key focus in cyber security. In this talk, we present TypeDB CTI, which is an open source threat intelligence platform to store and manage such knowledge. It enables Cyber Security Intelligence (CTI) professionals to bring together their disparate CTI information into one platform, enabling them to more easily manage such data and discover new insights about cyber threats.
We will describe how we use TypeDB to represent STIX 2.1, the most widely used language and serialization format used to exchange cyber threat intelligence. We cover how we leverage TypeDB's modelling constructs such as type hierarchies, nested relations, hyper relations, unique attributes, and logical inference to build this threat intelligence platform.
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.
Knowledge Graphs for Supply Chain Operations.pdf
Agility in supply chain operations has never been so important, especially with today's nonlinear and complex world. That is why companies with supply chains need knowledge graphs.
So how do enterprises unleash the power of their own supply chain data to make smarter decisions? This is where bops comes into play. Bops activates supply chain data from existing operating systems (ERPs, Pos, OMS, etc) simplifying how operators optimize working capital in every decision.
In this session, bops will showcase a few use cases that portray the power of a knowledge graph to represent a supply chain network composed of an end to end product flow driven by actions among plants, customers and suppliers.
Supply chain operations visibility:
- Story of a Product and an SKU: from raw material to finished goods track trace & bill of material deviations
- Story of a Supplier – risk assessments – “the most influential supplier”
- Story of a Process – anomaly detection – “what went wrong?”
Join us for a lively discussion to learn how using knowledge graphs is already helping supply chain companies to better collect, unify, and activate their data.
Speaker: Jorge Risquez
Jorge is the Co-founder and CEO of bops, a headless supply chain intelligence platform helping manufacturers and distributors source, make, and deliver their products, and unlock working capital. Previously, Jorge spent a decade as a Supply Chain Consultant for Deloitte, where he worked with Fortune 500 companies such as Tyson and Cargill. In his spare time, he enjoys going for a run in Central Park and spending time with family and friends.
Building a Distributed Database with Raft.pdf
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
Ganesh leads the development of TypeDB Cluster while also managing other aspects such as infrastructure and project management. His day-to-day work involves building concurrent and distributed algorithms such as Raft and the Actor Model.
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
Using SQL to query relational databases is easy. As a declarative language, it’s straightforward to write queries and build powerful applications. However, relational databases struggle when working with complex data. When querying such data in SQL, challenges especially arise in the modelling and querying of the data.
For example, due to the large number of necessary JOINs, it forces us to write long and verbose queries. Such queries are difficult to write and prone to mistakes.
TypeQL is the query language used in TypeDB. Just as SQL is the standard query language in relational databases, TypeQL is TypeDB's query language. It’s a declarative language, and allows us to model, query and reason over our data.
In this talk, we will look at how TypeQL compares to SQL. Why and when should you use TypeQL over SQL? How do we do outer/inner joins in TypeQL? We'll look at the common concepts, but mostly talk about the differences between the two.
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- 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
TypeDB is a strongly-typed database. It provides a rich and logical type system which breaks down complex problems into meaningful and logical systems, using TypeQL as its query language.
TypeDB allows you to model your domain based on logical and object-oriented principles. Composed of entity, relationship, and attribute types, as well as type hierarchies, roles, and rules, TypeDB allows you to think higher-level, as opposed to join-tables, columns, documents, vertices, and edges.
Types describe the logical structures of your data, allowing TypeDB to validate that your code inserts and queries data correctly. Query validation goes beyond static type-checking, and includes logical validation of meaningless queries. With strict type-checking errors, you have a dataset that you can trust.
Finally, TypeDB encodes your data for logical interpretation by its reasoning engine. It enables type-inference and rule-inference, which create logical abstractions of data. This allows for the discovery of facts and patterns that would otherwise be too hard to find.
With these abstractions, queries in the tens to hundreds of lines in SQL or NoSQL databases can be written in just a few lines in TypeQL – collapsing code complexity by orders of magnitude.
Join Tomás from the Vaticle team where he'll discuss the origins of TypeDB, the impetus for inventing a new query language, TypeQL, and why we are so excited about the future of software and intelligent systems.
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.
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.
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Deutsche Telecom Expert System - Router Troubleshooting
- 1. T H E K N O W L E D G E G R A P H Join our community at grakn.ai/community Deutsche Telecom PoC By James Fletcher Principal Researcher at GRAKN.AI @graknlabs @jmsfltchr
- 2. Follow us @GraknLabs “For a computer to pass a Turing Test, it needs to possess: Natural Language Processing, Knowledge Representation, Automated Reasoning and Machine Learning” Peter Norvig (Research Director, Google) and Stuart J. Russell (CS Professor, UC Berkeley), “Artificial Intelligence: A Modern Approach”, 1994 Wait, why do we need a knowledge base/graph?
- 3. Follow us @GraknLabs The Architecture of Cognition Comprehension and production of language: communication Natural Language Processing Reasoning, problem solving, logical deduction, and decision making Automated Reasoning Expression, Conceptualisation, memory and understanding Knowledge Representation Judgment and evaluation: To adapt to new circumstances and to detect and extrapolate new patterns Machine Learning Information Retrieval, Natural Language Understanding: User data, Enterprise data, Financial data, Web data, etc. Knowledge Acquisition COGNITION is "the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses."
- 4. Follow us @GraknLabs Knowledge Base/Graph The Architecture of Cognition Comprehension and production of language: communication Judgment and evaluation: To adapt to new circumstances and to detect and extrapolate new patterns Information Retrieval, Natural Language Understanding: User data, Enterprise data, Financial data, Web data, etc. Storage of knowledge (i.e. complex information), and retrieval of explicitly stored data and derive new conclusions. Natural Language Processing Machine LearningKnowledge Acquisition COGNITION is "the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses."
- 5. Follow us @GraknLabs THE ARCHITECTURE OF A COGNITIVE SYSTEM Natural Language Processing Knowledge Base Machine LearningKnowledge Acquisition
- 6. Follow us @GraknLabs VALUE TO AI: BE THE UNIFIED REPRESENTATION OF KNOWLEDGE Inference of low-level patterns and automation of analytics algorithms Machine translation for parsed query interpretation Expressive and extensible knowledge model INPUT SYSTEMS e.g. Information Retrieval, Entity Extraction, Natural Language Understanding LEARNING SYSTEMS e.g. Neural Networks, Bayesian Networks, Kernel Machines, Genetics Programming OUTPUT SYSTEMS e.g. Natural Language Query, Natural Language Generation
- 7. Deutsche Telekom - Project Scope Aim Demonstrate Grakn’s usefulness for customer support system Method Prove Grakn can solve a complex domain: router connection troubleshooting Why? Router troubleshooting follows complex logic, and a full Excel sheet of the procedure was available to us
- 9. Slack Expert System Demo
- 11. Follow us @GraknLabs Decision Tree Speedport W 701V P=GR D=GR O=? D=OFF C P=OFF A P=GR_ BLINK _FAST ST=GR K
- 12. Follow us @GraknLabs The Model
- 13. Schema Walkthrough Plain English declarations – easy to debug
- 14. Model Walkthrough How to define router behaviour
- 15. Example Query – inferring the next action
- 16. Follow us @GraknLabs Infer next action diagnosis action
- 17. Slack Expert System Demo cont.
- 18. Follow us @GraknLabs Decision Tree Speedport W 701V P=GR D=GR O=GR WL=GR
- 19. Going further Deduce LED statuses from the given information
- 20. Example Query – finding the possibilities
- 21. Workbase demo
- 22. Follow us @GraknLabs Infer next action diagnosis action
- 24. Follow us @GraknLabs How do we implement a real system? • Native language drivers
- 25. Schema Designer