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
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.
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.
Unifying Space Mission Knowledge with NLP & Knowledge GraphVaticle
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.
Graph Databases vs TypeDB | What you can't do with graphsVaticle
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.
The openCypher Project - An Open Graph Query LanguageNeo4j
We want to present the openCypher project, whose purpose is to make Cypher available to everyone – every data store, every tooling provider, every application developer. openCypher is a continual work in progress. Over the next few months, we will move more and more of the language artifacts over to GitHub to make it available for everyone.
openCypher is an open source project that delivers four key artifacts released under a permissive license: (i) the Cypher reference documentation, (ii) a Technology compatibility kit (TCK), (iii) Reference implementation (a fully functional implementation of key parts of the stack needed to support Cypher inside a data platform or tool) and (iv) the Cypher language specification.
We are also seeking to make the process of specifying and evolving the Cypher query language as open as possible, and are actively seeking comments and suggestions on how to improve the Cypher query language.
The purpose of this talk is to provide more details regarding the above-mentioned aspects.
We want to present the openCypher project, whose purpose is to make Cypher available to everyone – every data store, every tooling provider, every application developer. openCypher is a continual work in progress. Over the next few months, we will move more and more of the language artifacts over to GitHub to make it available for everyone.
openCypher is an open source project that delivers four key artifacts released under a permissive license: (i) the Cypher reference documentation, (ii) a Technology compatibility kit (TCK), (iii) Reference implementation (a fully functional implementation of key parts of the stack needed to support Cypher inside a data platform or tool) and (iv) the Cypher language specification.
We are also seeking to make the process of specifying and evolving the Cypher query language as open as possible, and are actively seeking comments and suggestions on how to improve the Cypher query language.
The purpose of this talk is to provide more details regarding the above-mentioned aspects.
Slides: Knowledge Graphs vs. Property GraphsDATAVERSITY
We are in the era of graphs. Graphs are hot. Why? Flexibility is one strong driver: Heterogeneous data, integrating new data sources, and analytics all require flexibility. Graphs deliver it in spades.
Over the last few years, a number of new graph databases came to market. As we start the next decade, dare we say “the semantic twenties,” we also see vendors that never before mentioned graphs starting to position their products and solutions as graphs or graph-based.
Graph databases are one thing, but “Knowledge Graphs” are an even hotter topic. We are often asked to explain Knowledge Graphs.
Today, there are two main graph data models:
• Property Graphs (also known as Labeled Property Graphs)
• RDF Graphs (Resource Description Framework) aka Knowledge Graphs
Other graph data models are possible as well, but over 90 percent of the implementations use one of these two models. In this webinar, we will cover the following:
I. A brief overview of each of the two main graph models noted above
II. Differences in Terminology and Capabilities of these models
III. Strengths and Limitations of each approach
IV. Why Knowledge Graphs provide a strong foundation for Enterprise Data Governance and Metadata Management
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.
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.
Unifying Space Mission Knowledge with NLP & Knowledge GraphVaticle
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.
Graph Databases vs TypeDB | What you can't do with graphsVaticle
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.
The openCypher Project - An Open Graph Query LanguageNeo4j
We want to present the openCypher project, whose purpose is to make Cypher available to everyone – every data store, every tooling provider, every application developer. openCypher is a continual work in progress. Over the next few months, we will move more and more of the language artifacts over to GitHub to make it available for everyone.
openCypher is an open source project that delivers four key artifacts released under a permissive license: (i) the Cypher reference documentation, (ii) a Technology compatibility kit (TCK), (iii) Reference implementation (a fully functional implementation of key parts of the stack needed to support Cypher inside a data platform or tool) and (iv) the Cypher language specification.
We are also seeking to make the process of specifying and evolving the Cypher query language as open as possible, and are actively seeking comments and suggestions on how to improve the Cypher query language.
The purpose of this talk is to provide more details regarding the above-mentioned aspects.
We want to present the openCypher project, whose purpose is to make Cypher available to everyone – every data store, every tooling provider, every application developer. openCypher is a continual work in progress. Over the next few months, we will move more and more of the language artifacts over to GitHub to make it available for everyone.
openCypher is an open source project that delivers four key artifacts released under a permissive license: (i) the Cypher reference documentation, (ii) a Technology compatibility kit (TCK), (iii) Reference implementation (a fully functional implementation of key parts of the stack needed to support Cypher inside a data platform or tool) and (iv) the Cypher language specification.
We are also seeking to make the process of specifying and evolving the Cypher query language as open as possible, and are actively seeking comments and suggestions on how to improve the Cypher query language.
The purpose of this talk is to provide more details regarding the above-mentioned aspects.
Slides: Knowledge Graphs vs. Property GraphsDATAVERSITY
We are in the era of graphs. Graphs are hot. Why? Flexibility is one strong driver: Heterogeneous data, integrating new data sources, and analytics all require flexibility. Graphs deliver it in spades.
Over the last few years, a number of new graph databases came to market. As we start the next decade, dare we say “the semantic twenties,” we also see vendors that never before mentioned graphs starting to position their products and solutions as graphs or graph-based.
Graph databases are one thing, but “Knowledge Graphs” are an even hotter topic. We are often asked to explain Knowledge Graphs.
Today, there are two main graph data models:
• Property Graphs (also known as Labeled Property Graphs)
• RDF Graphs (Resource Description Framework) aka Knowledge Graphs
Other graph data models are possible as well, but over 90 percent of the implementations use one of these two models. In this webinar, we will cover the following:
I. A brief overview of each of the two main graph models noted above
II. Differences in Terminology and Capabilities of these models
III. Strengths and Limitations of each approach
IV. Why Knowledge Graphs provide a strong foundation for Enterprise Data Governance and Metadata Management
JSON-LD is a set of W3C standards track specifications for representing Linked Data in JSON. It is fully compatible with the RDF data model, but allows developers to work with data entirely within JSON.
More information on JSON-LD can be found at http://json-ld.org/
Webinar in which Mike Bennett describes the unique approach Hypercube applies to modeling business semantics (the method used in creating the EDM Council's FIBO Business Conceptual Ontology). The end result of creating this kind of business conceptual ontology is that a firm will have a single, canonical source of meaning across all its data resources, like a golden copy but in the semantics space - so we sometimes refer to this a "Golden Ontology".
Mike explains the principles for creating an enterprise conceptual ontology. From this webinar you will learn:
3 things you need to know about ontologies
- Words are not Concepts
- Meaning is not Truth
- Syntax is not Semantics
3 things you need to do to build a Golden reference ontology:
- Classification
- Abstraction
- Partitioning
3 ways to use a Golden Ontology
- Querying across legacy data sources
- Mapping and data integration
- Reasoning with Semantic Web applications
Property graph vs. RDF Triplestore comparison in 2020Ontotext
This presentation goes all the way from intro "what graph databases are" to table comparing the RDF vs. PG plus two different diagrams presenting the market circa 2020
The perfect couple: Uniting Large Language Models and Knowledge Graphs for En...Neo4j
Large Language models are amazing but are also black-box models that often fail to capture and accurately represent factual knowledge. Knowledge graphs, by contrast, are structural knowledge models that explicitly represent knowledge and, indeed, allow us to detect implicit relationships. In this talk we will demonstrate how LLMs can be improved by Knowledge Graphs, and how LLM’s can augment Knowledge Graphs. A perfect couple!
Neptune is a Graph database, but what is a graph database, when should it be used and how? Come to this sessions and learn about Neptune and the different use cases for graph databases and how it can be used, including demos.
These are the slides for the Rego deep dive session from CloudNativeCon EU 2018: https://youtu.be/4mBJSIhs2xQ
These slides explain how the Open Policy Agent policy language works. The slides walk through the fundamentals of the language and then cover a few miscellaneous topics like composition, negation, etc.
Transforming Intelligence Analysis with Knowledge GraphsNeo4j
Transforming Intelligence Analysis with Knowledge Graphs
Vincent H. Bridgeman, Senior Vice President, National Security Services, Redhorse
Pelayo Fernandez, Research Analyst / Project Manager, United States Department of Defense
Intelligence Analysis is fundamentally a network problem. At different levels, the analyst must make sense of networks of related content, networks of related concepts, and ultimately networks of related targets that can only be understood in the context of other (even larger) networks. Examples of network problems in intelligence analysis include terrorism, sanctions evasion, global transnational organized crime, counterintelligence, and cyber security. Redhorse presents an integrated technology solution founded on Neo4j’s native graph database that brings a graphs-centered approach to intelligence analysis. The US Air Force provides an unclassified case study applying graphs to scientific forecasting. This project leverages temporal knowledge graphs, comprised of research article content and metadata, to learn and predict the trajectory of technological advancement, pushing the boundaries of graph-based intelligence analysis.
Building Identity Graphs over Heterogeneous DataDatabricks
In today’s world, customers and service providers (e.g., Social networks, ad targeting, retail, etc.) interact in a variety of modes and channels such as browsers, apps, devices, etc. In each such interaction, users are identified using a token (possibly different token for each mode/channel). Examples of such identity tokens include cookies, app IDs etc. As the user engages more with these services, linkages are generated between tokens belonging to the same user; linkages connect multiple identity tokens together.
Introduction to Knowledge Graphs with Grakn and Graql Vaticle
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.
A Data Modelling Framework to Unify Cyber Security KnowledgeVaticle
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.
Neo4j is a powerful and expressive tool for storing, querying and manipulating data. However modeling data as graphs is quite different from modeling data under a relational database. In this talk, Michael Hunger will cover modeling business domains using graphs and show how they can be persisted and queried in Neo4j. We'll contrast this approach with the relational model, and discuss the impact on complexity, flexibility and performance.
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.
Gremlin is the graph traversal language of Apache TinkerPop, an open source graph computing framework, that is implemented by a great many graph databases, including DSE Graph. Even the most novice Gremlin user will recognize the Gremlin statement of "g.V()", but in this presentation we will stop to take a moment to understand the elements of that ubiquitous statement and the elements of the steps that append to it. With the foundational knowledge of "Gremlin's Anatomy" firmly held, we will perform an autopsy on an advanced Gremlin traversal and thus expose techniques for examining and taming the most complex and confusing Gremlin one might come across.
What is dimension modeling? ,
Difference between ER modeling and dimension modeling,
What is a Dimension? ,
What is a Fact?
Start Schema ,
Snow Flake Schema ,
Difference between Star and snow flake schema ,
Fact Table ,
Different types of facts
Dimensional Tables,
Fact less Fact Table ,
Confirmed Dimensions ,
Unconfirmed Dimensions ,
Junk Dimensions ,
Monster Dimensions ,
Degenerative Dimensions ,
What are slowly changing Dimensions? ,
Different types of SCD's,
JSON-LD is a set of W3C standards track specifications for representing Linked Data in JSON. It is fully compatible with the RDF data model, but allows developers to work with data entirely within JSON.
More information on JSON-LD can be found at http://json-ld.org/
Webinar in which Mike Bennett describes the unique approach Hypercube applies to modeling business semantics (the method used in creating the EDM Council's FIBO Business Conceptual Ontology). The end result of creating this kind of business conceptual ontology is that a firm will have a single, canonical source of meaning across all its data resources, like a golden copy but in the semantics space - so we sometimes refer to this a "Golden Ontology".
Mike explains the principles for creating an enterprise conceptual ontology. From this webinar you will learn:
3 things you need to know about ontologies
- Words are not Concepts
- Meaning is not Truth
- Syntax is not Semantics
3 things you need to do to build a Golden reference ontology:
- Classification
- Abstraction
- Partitioning
3 ways to use a Golden Ontology
- Querying across legacy data sources
- Mapping and data integration
- Reasoning with Semantic Web applications
Property graph vs. RDF Triplestore comparison in 2020Ontotext
This presentation goes all the way from intro "what graph databases are" to table comparing the RDF vs. PG plus two different diagrams presenting the market circa 2020
The perfect couple: Uniting Large Language Models and Knowledge Graphs for En...Neo4j
Large Language models are amazing but are also black-box models that often fail to capture and accurately represent factual knowledge. Knowledge graphs, by contrast, are structural knowledge models that explicitly represent knowledge and, indeed, allow us to detect implicit relationships. In this talk we will demonstrate how LLMs can be improved by Knowledge Graphs, and how LLM’s can augment Knowledge Graphs. A perfect couple!
Neptune is a Graph database, but what is a graph database, when should it be used and how? Come to this sessions and learn about Neptune and the different use cases for graph databases and how it can be used, including demos.
These are the slides for the Rego deep dive session from CloudNativeCon EU 2018: https://youtu.be/4mBJSIhs2xQ
These slides explain how the Open Policy Agent policy language works. The slides walk through the fundamentals of the language and then cover a few miscellaneous topics like composition, negation, etc.
Transforming Intelligence Analysis with Knowledge GraphsNeo4j
Transforming Intelligence Analysis with Knowledge Graphs
Vincent H. Bridgeman, Senior Vice President, National Security Services, Redhorse
Pelayo Fernandez, Research Analyst / Project Manager, United States Department of Defense
Intelligence Analysis is fundamentally a network problem. At different levels, the analyst must make sense of networks of related content, networks of related concepts, and ultimately networks of related targets that can only be understood in the context of other (even larger) networks. Examples of network problems in intelligence analysis include terrorism, sanctions evasion, global transnational organized crime, counterintelligence, and cyber security. Redhorse presents an integrated technology solution founded on Neo4j’s native graph database that brings a graphs-centered approach to intelligence analysis. The US Air Force provides an unclassified case study applying graphs to scientific forecasting. This project leverages temporal knowledge graphs, comprised of research article content and metadata, to learn and predict the trajectory of technological advancement, pushing the boundaries of graph-based intelligence analysis.
Building Identity Graphs over Heterogeneous DataDatabricks
In today’s world, customers and service providers (e.g., Social networks, ad targeting, retail, etc.) interact in a variety of modes and channels such as browsers, apps, devices, etc. In each such interaction, users are identified using a token (possibly different token for each mode/channel). Examples of such identity tokens include cookies, app IDs etc. As the user engages more with these services, linkages are generated between tokens belonging to the same user; linkages connect multiple identity tokens together.
Introduction to Knowledge Graphs with Grakn and Graql Vaticle
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.
A Data Modelling Framework to Unify Cyber Security KnowledgeVaticle
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.
Neo4j is a powerful and expressive tool for storing, querying and manipulating data. However modeling data as graphs is quite different from modeling data under a relational database. In this talk, Michael Hunger will cover modeling business domains using graphs and show how they can be persisted and queried in Neo4j. We'll contrast this approach with the relational model, and discuss the impact on complexity, flexibility and performance.
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.
Gremlin is the graph traversal language of Apache TinkerPop, an open source graph computing framework, that is implemented by a great many graph databases, including DSE Graph. Even the most novice Gremlin user will recognize the Gremlin statement of "g.V()", but in this presentation we will stop to take a moment to understand the elements of that ubiquitous statement and the elements of the steps that append to it. With the foundational knowledge of "Gremlin's Anatomy" firmly held, we will perform an autopsy on an advanced Gremlin traversal and thus expose techniques for examining and taming the most complex and confusing Gremlin one might come across.
What is dimension modeling? ,
Difference between ER modeling and dimension modeling,
What is a Dimension? ,
What is a Fact?
Start Schema ,
Snow Flake Schema ,
Difference between Star and snow flake schema ,
Fact Table ,
Different types of facts
Dimensional Tables,
Fact less Fact Table ,
Confirmed Dimensions ,
Unconfirmed Dimensions ,
Junk Dimensions ,
Monster Dimensions ,
Degenerative Dimensions ,
What are slowly changing Dimensions? ,
Different types of SCD's,
Digital publishing has changed. Understand the base components that allow modern publishers to more easily publish content in multiple formats across multiple platforms.
Presentation originally developed by Apex VP and Principal Consultant Bill Kasdorf for a university press in June 2016, based on presentations on this subject that he has given to many organizations over the past ten years. Learn more at www.apexcovantage.com.
George McGeachie's Favourite PowerDesigner featuresGeorge McGeachie
These are the slides from my presentation at Data Modelling Zone in Dusseldorf, in September 2018. These are all features that differentiate the tool from the other players in the market.
Building Biomedical Knowledge Graphs for In-Silico Drug DiscoveryVaticle
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 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 GraphVaticle
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.
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 GraphVaticle
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.pdfVaticle
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.pdfVaticle
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.pdfVaticle
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.
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.
Comparing Semantic Web Technologies to TypeDBVaticle
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.
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 databaseVaticle
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.
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.
AI offers enormous potential in terms of improving the effectiveness and efficiency of robots. In recent years, data-driven AI has achieved remarkable success in specialised tasks such as speech recognition, machine translation and object detection. Despite these successes, there are also some clear signs of the limitations.
On finding a solution to these limitations, we study the following three challenges:
1) How may robots operate under real-world conditions, which are dynamic and packed with unknown objects and situations?
2) How may robots be able to execute multiple tasks, instead of just one?
3) How can robots cooperate with other robots and with human team-mates?
In this talk the first two challenges will be addressed. Also, we will show how the knowledge-base of TypeDB enables us to tackle such challenges.
Speaker: Joris Sijs, Scientist @ TNO
Joris is a team-lead at TNO, where he develops and integrates software modules for the perception, awareness and planning of autonomous systems and autonomous robots. He recently started to extend this work with the development of knowledge-graphs (or cognitive databases), and how to combine this type of AI with the machine- and deep-learning solutions in AI.
Combining Causal and Knowledge Modeling for Digital TransformationVaticle
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.
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 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.
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.
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.
Grounding Conversational AI in a Knowledge BaseVaticle
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.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
2. Schema Design
• Motivation for schema
• Conceptual schema of TypeDB, and its relationship to the enhanced ER-Diagram
• How to write schema in TypeQL
3. Coming to terms with schema-less
To better understand the value that schema plays in our ability to be
expressive and efficient in working with a database, we need to come to
terms with what we already know about using NoSQL databases. . .
4. Coming to terms with schema-less
In the chat, drop your answer to the below:
How would we go about implementing logical
consistency to your data in a schema-less database?
5. Motivation for the schema
Schema declares the permissible structure of your data. We do this using
expressive schema constructs, which can constrain our data without
getting in the way.
• logical integrity at insertion-time: constraints on the data inserted
• knowledge representation: expressive querying in near natural language
• store domain context: enables rule based inference
• true-to-life representation of the domain: understanding context
6. 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.
7. TypeDB’s Data Model
TypeDB is a database with a rich and logical type system, based on the Enhanced
Entity Relationship Model.
The model embeds context with:
• typing: entities, attributes, relations, roles
• type inheritance: single, not multiple
• fully labeled hyper-relations: including composition via named rules
8. Concept architecture
As entities, relations, attributes are all
sub-types of thing, we should sub-type
each to build a schema for our domain.
These schema concepts define our
types, which can then be instantiated by
our data.
Therefore, we refer to data model and
schema interchangeably.
Terminology:
• type: refers to a sub-type of entity, attribute or relation, as defined
by a a schema
• thing: refers to an instance of any type
• concept: refers to any thing or type
11. Visualise the model for our domain
Having seen the TypeDB model, how would you translate this to your domain?
Exercise- 6 minutes:
1) Thinking about your domain and use case, begin to visually map out your
model based on the TypeDB Knowledge Model.
2) What are the entities and their attributes - then what relations between
the data do you recognise or foresee?
3) Draw your model on a scrap piece of paper or an online modelling tool,
we’ll turn it into TypeDB schema next.
13. Visualise the model for our domain
Let’s share what we came up with and see what
differences we found between us.
14. entity – a thing with a distinct existence in the domain
syntax:
The keyword define is used to precede declarations of our schema. We can
define schema as many times as we want, not just once .
Here person is the entity type. We have declared that we want this type to
inherit from entity using sub . We can refer to the type itself using its type-
name, in this case person.
Defining schema types
define
person sub entity;
15. relation – describes how two or more things are connected to each other
syntax:
define
employment sub relation,
relates employer,
relates employee;
person sub entity,
plays employment:employee;
company sub entity,
plays employment:employer;
Watch Out!
A role must be declared for a
type to play it, either: inside a
relation; or explicitly, outside
a relation
Defining schema types
16. relation – in general, any type can be defined to play a role
<schema-type> could be entity , relation , attribute , or your own subtype of one of these. Therefore, relations
can relate anything. These are very useful constructs for building schemas that reflect the real domain well.
We scope the <role-type> a particular <schema-type> can play, in <my-relation-type>. This allows for a
more simple and efficient model, which we can make use of once we begin to query the database.
syntax:
Defining schema types
define
my-schema-type sub <schema-type>,
plays <my-relation-type>:<role-type>;
17. attribute – a piece of information, describing a property of a thing in a domain
syntax:
Attribute values
long, double, string, Boolean, date – the specifications for these are the same as in Java
Defining schema types
define
full-name sub attribute,
value string;
person sub entity,
owns full-name;
18. attribute – can only be instantiated at the leaves
Any time we create a new sub-type of an existing <my-attribute-type>, the parent type must be abstract and
defined as such in the schema.
attribute, is already abstract and so sub-types of attribute can be instantiated.
syntax:
Defining schema types
define
name sub attribute,
abstract,
value string;
full-name sub name;
19. type hierarchies – ”type Y is a subtype of type X, if and only if, every Y is necessarily an X”
syntax:
This means that any charity, company, and university is necessarily an organisation and inherits all
attributes and role-types from the parent.
define
organisation sub entity,
owns name;
charity sub organisation,
plays <relation-type>:group;
company sub organisation,
plays <relation-type>:employer;
university sub organisation,
plays <relation-type>:employer;
:: watch out ::
When sub-typing an
attribute, the parent
must be abstract.
Defining schema types
20. Defining schema types
Also…
syntax:
define
person sub entity,
owns email @key;
organisation sub entity,
abstract;
request sub relation,
abstract,
relates requestor;
friendship sub request,
relates friend as requester;
:: sneak peak ::
All roles defined to relate
to the parent relation
must also be defined to
relate to the child relation
using the as keyword.
21. Visualise the model for our domain
Now, let’s build off our “hand-made” model….
Exercise- 10 minutes individually:
1) Iterate on the model you drew and take a run at writing your schema in
TypeQL.
2) Define the entity(s), relation(s), and attribute(s), for your own domain or
use case
3) Once you have gotten to a ”complete” spot, drop your response to:
What was most challenging about this? What caught you up?
22. Schema syntax reminders
• start with define
• a sub b to subtype a from b
• value string to declare the datatype of an attribute
• owns to declare something can own an attribute
• relates to declare a role in a relation
• plays to declare something can play a role within a specific relation-type
• abstract to declare a <schema-type> to be abstract
• Commas between properties, and semicolon to end a statement
23. Define
role hierarchies – as is used to sub-type a role inside a relation
syntax:
define
ownership sub relation,
relates owner,
relates owned;
group-ownership sub ownership,
relates group-owner as owner,
relates owned-group as owned;
person sub entity,
plays group-ownership:group-owner;
social-group sub entity,
plays group-ownership:owned-group;
:: note ::
To be able to use both
the sub-type role and the
parent role, we need to
redeclare the parent role.
24. Schema flexibility
Schema file (.tql) – schema can be added to trivially, at any point.
and then later…
define
person sub entity;
name sub attribute,
value string;
define
person owns name;
## OR
person sub entity,
owns name;
25. Undefine schema types
Order of undefining – first no instances of that type (to protect our data)
syntax:
It's important to note that undefine [label] sub [type] owns [attribute's label]; undefines the label itself,
rather than its association with the attribute.
undefine
<my-schema-type> sub <schema-type>;
person owns social-handle;
rule co-workers;
employment sub relation;
commit
26. TypeQL keywords
define – Declare that a statement will create schema types
undefine – Declare that a statement will delete schema types
sub – Declare a subtype
owns – Attaches an attribute type to a type
relates – Declares a role to be involved in a relation (creates the role if it isn’t
otherwise defined)
plays – Declares that a type can be connected through a role’
as – Subtypes a role inside a relation
@key – Declares an attribute to be used as a unique identifier for a type
abstract – Declares that a type cannot have instances
27. Iterative modelling approach
You can create you schema by writing a TypeQL file.
1) Write/update your .tql file. This can contain define statements, and also
insert and match...insert statements (the latter two are to add example data)
2) Load the .tql file into your database
3) Identify the flaws in your model by:
- Trying to query the model for the insights you need. If the insights cannot be found
correctly, then try to understand why your model is limited. Assessing whether the data you
have can be stored in the model.
4) Clean the database
5) Go back to step (1.) and repeat