Grakn is a knowledge graph for building intelligent systems. It provides a flexible schema for modeling complex domains and automated logical inference. Grakn also features a distributed analytics language for large-scale analytics. The knowledge graph foundation enables developments in areas like finance, life sciences, and more by providing a unified representation of knowledge.
GRAKN.AI: The Hyper-Relational Database for Knowledge-Oriented SystemsVaticle
AI systems process knowledge that is far too complex for current databases. They require more expressive data schemas and intelligent query languages to provide a strong abstraction over complex data and their relationships. In this talk, we will discuss how GRAKN.AI, a distributed hyper-relational database, enables knowledge-oriented systems to work with complex data that serves as a knowledge base.
We will discuss how Graql, Grakn's reasoning (through OLTP) and analytics (through OLAP) query language, provides a much higher-level abstraction over traditional query languages. And finally, we will review the challenges of data management when developing Cognitive and AI systems, and how we solve them using Grakn and Graql as the database and query language.
Logical Inference in a Hyper-Relational DatabaseVaticle
Inference is something we humans do all the time. Given a set of facts about the world, we derive new ones using some form of inference. Automated reasoning has been studied extensively but its value in providing a more powerful abstraction layer for database languages has been overlooked so far.
This talk explores deductive inference in Grakn, a hyper-relational database that has automated inference as one of its core features. Rather than defining SQL views or writing ad hoc code, in Grakn we can define logical rules that provide a more intuitive way to describe higher level domain concepts. In the talk we give a quick overview of computational logic semantics and of top-down and bottom-up inference algorithms. Then, after introducing some preliminary Grakn concepts, we show how logical rules are resolved in a query.
Deutsche Telecom Expert System - Router TroubleshootingVaticle
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.
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.
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.
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.
Knowledge graph convolutional networks - Berlin 2019Vaticle
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/kgcns-machine-learning-over-knowledge-graphs-with-tensorflow-a1d3328b8f02
Associated video:
https://www.youtube.com/watch?v=3adsYypRDsQ
This is a clip from the Grakn Berlin Meetup (Berlin 2019). Join the community: grakn.ai/community
Natural Language Processing and Text Mining with Knowledge GraphsVaticle
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
GRAKN.AI: The Hyper-Relational Database for Knowledge-Oriented SystemsVaticle
AI systems process knowledge that is far too complex for current databases. They require more expressive data schemas and intelligent query languages to provide a strong abstraction over complex data and their relationships. In this talk, we will discuss how GRAKN.AI, a distributed hyper-relational database, enables knowledge-oriented systems to work with complex data that serves as a knowledge base.
We will discuss how Graql, Grakn's reasoning (through OLTP) and analytics (through OLAP) query language, provides a much higher-level abstraction over traditional query languages. And finally, we will review the challenges of data management when developing Cognitive and AI systems, and how we solve them using Grakn and Graql as the database and query language.
Logical Inference in a Hyper-Relational DatabaseVaticle
Inference is something we humans do all the time. Given a set of facts about the world, we derive new ones using some form of inference. Automated reasoning has been studied extensively but its value in providing a more powerful abstraction layer for database languages has been overlooked so far.
This talk explores deductive inference in Grakn, a hyper-relational database that has automated inference as one of its core features. Rather than defining SQL views or writing ad hoc code, in Grakn we can define logical rules that provide a more intuitive way to describe higher level domain concepts. In the talk we give a quick overview of computational logic semantics and of top-down and bottom-up inference algorithms. Then, after introducing some preliminary Grakn concepts, we show how logical rules are resolved in a query.
Deutsche Telecom Expert System - Router TroubleshootingVaticle
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.
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.
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.
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.
Knowledge graph convolutional networks - Berlin 2019Vaticle
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/kgcns-machine-learning-over-knowledge-graphs-with-tensorflow-a1d3328b8f02
Associated video:
https://www.youtube.com/watch?v=3adsYypRDsQ
This is a clip from the Grakn Berlin Meetup (Berlin 2019). Join the community: grakn.ai/community
Natural Language Processing and Text Mining with Knowledge GraphsVaticle
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
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.
Using the KGLIB package of Grakn, we designed and trained a graph network for object classification, which is able to handle unknown objects. Data-driven insights based on image properties are combined with expert knowledge about class-hierarchies to classify images on multiple categories. We tested our network on a dataset of vehicles and predicted higher level categories. (For example 'land', 'air' or 'sea' vehicle). The graph network is used to predict interesting object characteristic, which require abstract knowledge predefined in a Grakn knowledge graph.
During this talk we will present our approach taken and discuss the design process we have taken. We will not only discuss the results, but also the difficulties and learning process we encountered.
Using Grakn to Analyse Protein Sequence AlignmentVaticle
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.
Precision Medicine Knowledge Graph with GRAKN.AIVaticle
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.
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.
This is a clip from the Grakn London Meetup in February 2019. Join the community: www.grakn.ai/community
Benchmarking for Neural Information Retrieval: MS MARCO, TREC, and BeyondBhaskar Mitra
The emergence of deep learning-based methods for information retrieval (IR) poses several challenges and opportunities for benchmarking. Some of these are new, while others have evolved from existing challenges in IR exacerbated by the scale at which deep learning models operate. In this talk, I will present a brief overview of what we have learned from our work on MS MARCO and the TREC Deep Learning track, and reflect on the road ahead.
Graph enhancements to Artificial Intelligence and Machine Learning are changing the landscape of intelligent applications. Beyond improving accuracy and modeling speed, graph technologies make building AI solutions more accessible. Join us to hear about 4 areas at the forefront of graph enhanced AI and ML, and find out which techniques are commonly used today and which hold the potential for disrupting industries. We'll provide examples and specifically look how: - Graphs provide better accuracy through connected feature extraction - Graphs provide better performance through contextual model optimization - Graphs provide context through knowledge graphs - Graphs add explainability to neural networks
Speakers: Jake Graham, Alicia Frame
Graph intelligence: the future of data-driven investigationsConnected Data World
RDF and graph databases are on the rise. The performances, flexibility, and scalability of these systems are attracting a large number of organizations struggling with complex and connected data. While the graph approach offers several advantages, finding insights into the enormous volume of data remains a challenge.
In this presentation, we will introduce Graph Intelligence, an advanced combination of human and computer-based intelligence to find insights faster in complex connected datasets. We will explain why we believe this approach is the future for teams of investigators fighting financial crime, national security threats or cyber attacks.
From this presentation, you will learn:
The nature and benefits of the Graph Intelligence approach
How to build a platform leveraging graph technology
Real-life examples of money laundering and financial crimes detection and investigation
Predictive Model and Record Description with Segmented Sensitivity Analysis (...Greg Makowski
Describing a predictive data mining model can provide a competitive advantage for solving business problems with a model. The SSA approach can also provide reasons for the forecast for each record. This can help drive investigations into fields and interactions during a data mining project, as well as identifying "data drift" between the original training data, and the current scoring data. I am working on open source version of SSA, first in R.
K anonymity for crowdsourcing database
In crowdsourcing database, human operators are embedded into the database engine and collaborate with other conventional database operators to process the queries. Each human operator publishes small HITs (Human Intelligent Task) to the crowdsourcing platform, which consists of a set of database records and corresponding questions for human workers.
Improving Machine Learning using Graph AlgorithmsNeo4j
Graph enhancements to AI and ML are changing the landscape of intelligent applications. In this session, we’ll focus on how using connected features can help improve the accuracy, precision, and recall of machine learning models. You’ll learn how graph algorithms can provide more predictive features as well as aid in feature selection to reduce overfitting. We’ll look at a link prediction example to predict collaboration with measurable improvement when including graph-based features.
Graph Gurus Episode 26: Using Graph Algorithms for Advanced Analytics Part 1TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-26
Have you ever wondered how routing apps like Google Maps find the best route from one place to another? Finding that route is solved by the Shortest Path graph algorithm. Today, graph algorithms are moving from the classroom to a host of important and valuable operational and analytical applications. This webinar will give you an overview of graph algorithms, how to use them, and the categories of problems they can solve, and then take a closer look at path algorithms. This webinar is the first part in a five-part series, each part examining a different type of problem to be solved.
Using Graph Algorithms for Advanced Analytics - Part 5 ClassificationTigerGraph
What atmospheric data will help you predict if it's going to rain, snow, or be windy? What position should that new athlete play? How well can you guess a person's demographic background, based on their chat activity? These are all classification problems -- trying to pick the right category or label for an entity, based on observable features. They can also be solved with machine learning.
Anonymization techniques are used to ensure the privacy preservation of the data owners, especially for personal and sensitive data. While in most cases, data reside inside the database management system; most of the proposed anonymization techniques operate on and anonymize isolated datasets stored outside the DBMS. Hence, most of the desired functionalities of the DBMS are lost, e.g., consistency, recoverability, and efficient querying. In this paper, we address the challenges involved in enforcing the data privacy inside the DBMS. We implement the k-anonymity algorithm as a relational operator that interacts with other query operators to apply the privacy requirements while querying the data. We study anonymizing a single table, multiple tables, and complex queries that involve multiple predicates. We propose several algorithms to implement the anonymization operator that allow efficient non-blocking and pipelined execution of the query plan. We introduce the concept of k-anonymity view as an abstraction to treat k-anonymity (possibly, with multiple k preferences) as a relational view over the base table(s). For non-static datasets, we introduce the materialized k-anonymity views to ensure preserving the privacy under incremental updates. A prototype system is realized based on PostgreSQL with extended SQL and new relational operators to support anonymity views. The prototype system demonstrates how anonymity views integrate with other privacy- preserving components, e.g., limited retention, limited disclosure, and privacy policy management. Our experiments, on both synthetic and real datasets, illustrate the performance gain from the anonymity views as well as the proposed query optimization techniques under various scenarios.
Webinar : Introduction to R Programming and Machine LearningEdureka!
'Business Analytics with 'R' at Edureka will prepare you to perform analytics and build models for real world data science problems. It is the world’s most powerful programming language for statistical computing and graphics making it a must know language for the aspiring Data Scientists. 'R' wins strongly on Statistical Capability, Graphical capability, Cost and rich set of packages.
The topics covered in the presentation are:
1.What is R
2.Domains and Companies in which R is used
3.Characteristics of R
4.Get an Overview of Machine Learning
5.Understand the difference between supervised and unsupervised learning
6.Learn Clustering and K-means Clustering
7.Implement K-means Clustering in R
8.Google Trends in R
Multiplaform Solution for Graph DatasourcesStratio
One of the top banks in Europe, needed a system to provide better performance, scaling almost linearly with the increase in information to be analyzed, and allowing to move the processes that were currently being executed in the Host to a Big Data infrastructure. During a year we've worked on a system which is able to provide greater agility, flexibility and simplicity for the user to view information when profiling and is now able to analyze the structure of profile data. It's a powerful way to make online queries to a graph database, which is integrated with Apache Spark and different graph libraries. Basically, we get all the necessary information through Cypher queries which are sent to a Neo4j database.
Using the last Big Data technologies like Spark Dataframe, HDFS, Stratio Intelligence or Stratio Crossdata, we have developed a solution which is able to obtain critical information for multiple datasources like text files o graph databases.
Hadoop clusters can store nearly everything in a cheap and blazingly fast way to your data lake. Answering questions and gaining insights out of this ever growing stream becomes the decisive part for many businesses. Increasingly data has a natural structure as a graph, with vertices linked by edges, and many questions arising about the data involve graph traversals or other complex queries, for which one does not have an a priori given bound on the length of paths.
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.
Using the KGLIB package of Grakn, we designed and trained a graph network for object classification, which is able to handle unknown objects. Data-driven insights based on image properties are combined with expert knowledge about class-hierarchies to classify images on multiple categories. We tested our network on a dataset of vehicles and predicted higher level categories. (For example 'land', 'air' or 'sea' vehicle). The graph network is used to predict interesting object characteristic, which require abstract knowledge predefined in a Grakn knowledge graph.
During this talk we will present our approach taken and discuss the design process we have taken. We will not only discuss the results, but also the difficulties and learning process we encountered.
Using Grakn to Analyse Protein Sequence AlignmentVaticle
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.
Precision Medicine Knowledge Graph with GRAKN.AIVaticle
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.
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.
This is a clip from the Grakn London Meetup in February 2019. Join the community: www.grakn.ai/community
Benchmarking for Neural Information Retrieval: MS MARCO, TREC, and BeyondBhaskar Mitra
The emergence of deep learning-based methods for information retrieval (IR) poses several challenges and opportunities for benchmarking. Some of these are new, while others have evolved from existing challenges in IR exacerbated by the scale at which deep learning models operate. In this talk, I will present a brief overview of what we have learned from our work on MS MARCO and the TREC Deep Learning track, and reflect on the road ahead.
Graph enhancements to Artificial Intelligence and Machine Learning are changing the landscape of intelligent applications. Beyond improving accuracy and modeling speed, graph technologies make building AI solutions more accessible. Join us to hear about 4 areas at the forefront of graph enhanced AI and ML, and find out which techniques are commonly used today and which hold the potential for disrupting industries. We'll provide examples and specifically look how: - Graphs provide better accuracy through connected feature extraction - Graphs provide better performance through contextual model optimization - Graphs provide context through knowledge graphs - Graphs add explainability to neural networks
Speakers: Jake Graham, Alicia Frame
Graph intelligence: the future of data-driven investigationsConnected Data World
RDF and graph databases are on the rise. The performances, flexibility, and scalability of these systems are attracting a large number of organizations struggling with complex and connected data. While the graph approach offers several advantages, finding insights into the enormous volume of data remains a challenge.
In this presentation, we will introduce Graph Intelligence, an advanced combination of human and computer-based intelligence to find insights faster in complex connected datasets. We will explain why we believe this approach is the future for teams of investigators fighting financial crime, national security threats or cyber attacks.
From this presentation, you will learn:
The nature and benefits of the Graph Intelligence approach
How to build a platform leveraging graph technology
Real-life examples of money laundering and financial crimes detection and investigation
Predictive Model and Record Description with Segmented Sensitivity Analysis (...Greg Makowski
Describing a predictive data mining model can provide a competitive advantage for solving business problems with a model. The SSA approach can also provide reasons for the forecast for each record. This can help drive investigations into fields and interactions during a data mining project, as well as identifying "data drift" between the original training data, and the current scoring data. I am working on open source version of SSA, first in R.
K anonymity for crowdsourcing database
In crowdsourcing database, human operators are embedded into the database engine and collaborate with other conventional database operators to process the queries. Each human operator publishes small HITs (Human Intelligent Task) to the crowdsourcing platform, which consists of a set of database records and corresponding questions for human workers.
Improving Machine Learning using Graph AlgorithmsNeo4j
Graph enhancements to AI and ML are changing the landscape of intelligent applications. In this session, we’ll focus on how using connected features can help improve the accuracy, precision, and recall of machine learning models. You’ll learn how graph algorithms can provide more predictive features as well as aid in feature selection to reduce overfitting. We’ll look at a link prediction example to predict collaboration with measurable improvement when including graph-based features.
Graph Gurus Episode 26: Using Graph Algorithms for Advanced Analytics Part 1TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-26
Have you ever wondered how routing apps like Google Maps find the best route from one place to another? Finding that route is solved by the Shortest Path graph algorithm. Today, graph algorithms are moving from the classroom to a host of important and valuable operational and analytical applications. This webinar will give you an overview of graph algorithms, how to use them, and the categories of problems they can solve, and then take a closer look at path algorithms. This webinar is the first part in a five-part series, each part examining a different type of problem to be solved.
Using Graph Algorithms for Advanced Analytics - Part 5 ClassificationTigerGraph
What atmospheric data will help you predict if it's going to rain, snow, or be windy? What position should that new athlete play? How well can you guess a person's demographic background, based on their chat activity? These are all classification problems -- trying to pick the right category or label for an entity, based on observable features. They can also be solved with machine learning.
Anonymization techniques are used to ensure the privacy preservation of the data owners, especially for personal and sensitive data. While in most cases, data reside inside the database management system; most of the proposed anonymization techniques operate on and anonymize isolated datasets stored outside the DBMS. Hence, most of the desired functionalities of the DBMS are lost, e.g., consistency, recoverability, and efficient querying. In this paper, we address the challenges involved in enforcing the data privacy inside the DBMS. We implement the k-anonymity algorithm as a relational operator that interacts with other query operators to apply the privacy requirements while querying the data. We study anonymizing a single table, multiple tables, and complex queries that involve multiple predicates. We propose several algorithms to implement the anonymization operator that allow efficient non-blocking and pipelined execution of the query plan. We introduce the concept of k-anonymity view as an abstraction to treat k-anonymity (possibly, with multiple k preferences) as a relational view over the base table(s). For non-static datasets, we introduce the materialized k-anonymity views to ensure preserving the privacy under incremental updates. A prototype system is realized based on PostgreSQL with extended SQL and new relational operators to support anonymity views. The prototype system demonstrates how anonymity views integrate with other privacy- preserving components, e.g., limited retention, limited disclosure, and privacy policy management. Our experiments, on both synthetic and real datasets, illustrate the performance gain from the anonymity views as well as the proposed query optimization techniques under various scenarios.
Webinar : Introduction to R Programming and Machine LearningEdureka!
'Business Analytics with 'R' at Edureka will prepare you to perform analytics and build models for real world data science problems. It is the world’s most powerful programming language for statistical computing and graphics making it a must know language for the aspiring Data Scientists. 'R' wins strongly on Statistical Capability, Graphical capability, Cost and rich set of packages.
The topics covered in the presentation are:
1.What is R
2.Domains and Companies in which R is used
3.Characteristics of R
4.Get an Overview of Machine Learning
5.Understand the difference between supervised and unsupervised learning
6.Learn Clustering and K-means Clustering
7.Implement K-means Clustering in R
8.Google Trends in R
Multiplaform Solution for Graph DatasourcesStratio
One of the top banks in Europe, needed a system to provide better performance, scaling almost linearly with the increase in information to be analyzed, and allowing to move the processes that were currently being executed in the Host to a Big Data infrastructure. During a year we've worked on a system which is able to provide greater agility, flexibility and simplicity for the user to view information when profiling and is now able to analyze the structure of profile data. It's a powerful way to make online queries to a graph database, which is integrated with Apache Spark and different graph libraries. Basically, we get all the necessary information through Cypher queries which are sent to a Neo4j database.
Using the last Big Data technologies like Spark Dataframe, HDFS, Stratio Intelligence or Stratio Crossdata, we have developed a solution which is able to obtain critical information for multiple datasources like text files o graph databases.
Hadoop clusters can store nearly everything in a cheap and blazingly fast way to your data lake. Answering questions and gaining insights out of this ever growing stream becomes the decisive part for many businesses. Increasingly data has a natural structure as a graph, with vertices linked by edges, and many questions arising about the data involve graph traversals or other complex queries, for which one does not have an a priori given bound on the length of paths.
At Data-centric Architecture Forum 2020 Thomas Cook, our Sales Director of AnzoGraph DB, gave his presentation "Knowledge Graph for Machine Learning and Data Science". These are his slides.
Fishing Graphs in a Hadoop Data Lake by Jörg Schad and Max Neunhoeffer at Big...Big Data Spain
Hadoop clusters can store nearly everything in a cheap and blazingly fast way to your data lake. Answering questions and gaining insights out of this ever growing stream becomes the decisive part for many businesses.
https://www.bigdataspain.org/2017/talk/fishing-graphs-in-a-hadoop-data-lake
Big Data Spain 2017
16th - 17th November Kinépolis Madrid
How Graph Databases used in Police Department?Samet KILICTAS
This presentation delivers basics of graph concept and graph databases to audience. It clearly explains how graph databases are used with sample use cases from industry and how it can be used for police departments. Questions like "When to use a graph DB?" and "Should I solve a problem with Graph DB?" are answered.
Hadoop clusters can store nearly everything in a cheap and blazingly fast way to your data lake. Answering questions and gaining insights out of this ever growing stream becomes the decisive part for many businesses. Increasingly data has a natural structure as a graph, with vertices linked by edges, and many questions arising about the data involve graph traversals or other complex queries, for which one does not have an a priori given bound on the length of paths.
Spark with GraphX is great for answering relatively simple graph questions which are worth starting a Spark job for, because they essentially involve the whole graph. But does it make sense to start one for every ad-hoc query or is it suitable for complex real-time queries?
In this talk I will introduce an alternative solution that adds those features to an existing Hadoop/Spark setup and enables real-time insights. I will address the following topics:
* Challenges in gaining deeper insights from large amounts of graph data
* Benefits and limitations of graph analysis with Spark
* Introduction to ArangoDB SmartGraphs
* Deployment of Hadoop, Spark and ArangoDB using DC/OS
* Performing complex queries on billions of nodes and vertices leveraging ArangoDB SmartGraphs (Live Demo)
AI, Knowledge Representation and Graph Databases - Key Trends in Data ScienceOptum
Knowledge Representation is a key focus for most modern AI texts. Many AI experts feel that over half of their work is understanding how to find the right knowledge structures to build intelligent agents that can continuously learn and respond to changing events in their world. In 2012, a paper published by Google started a consolidation of the many diverse forms of knowledge representation into a single general-purpose structure called a labeled property graph.
This talk will describe the key events behind this movement and show how a new generation of data scientist will be needed to build and maintain corporate knowledge graphs that contain a uniform, normalized and highly connected data sets for used by researchers and intelligent agents. We will also discuss the challenges of transferring siloed project-knowledge to reusable structures.
MuseoTorino, first italian project using a GraphDB, RDFa, Linked Open Data21Style
MuseoTorino, is the first italian project using Web 3.0 tecnologies. NOSQL-GraphDB (Neo4J), RDFa, Linked Open Data.
MuseoTorino is a 21style (www.21-style.com) project for the municipality of Torino, Italy.
These slides come from CodeMotion, the best Italian conference for developers and IT entusiast !
AnzoGraph DB: Driving AI and Machine Insights with Knowledge Graphs in a Conn...Cambridge Semantics
Thomas Cook, director of sales, Cambridge Semantics, offers a primer on graph database technology and the rapid growth of knowledge graphs at Data Summit 2020 in his presentation titled "AnzoGraph DB: Driving AI and Machine Insights with Knowledge Graphs in a Connected World".
aRangodb, un package per l'utilizzo di ArangoDB con RGraphRM
Lingua talk: Italiano.
Descrizione:
In questo talk parleremo di come integrare e utilizzare ArangoDB, un database multi-modello con supporto nativo ai grafi, con R. Presenteremo quindi aRangodb, il package che abbiamo sviluppato per interfacciarsi in modo più semplice e intuitivo al database. Nel corso del talk mostreremo come il package possa essere utilizzato in ambito data science usando alcuni case studies concreti.
Speaker:
Gabriele Galatolo - Data Scientist - Kode srl
Big data at AWS Chicago User Group - 2014AWS Chicago
Big data at AWS Chicago User Group
Most of the slides from the Sept 23rd 2014 AWS User Group in Chicago.
Talks:
"AWS Storage Options" Ben Blair, CTO at MarkITx @stochastic_code
"APIs and Big Data in AWS" - Kin Lane, API Evangelist @kinlane
[coming soon] "Democratizing Data Analysis with Amazon Redshift" - Bill Wanjohi @billwanjohi and Michelangelo D'Agostino @MichelangeloDA, Civis Analytics
Sponsored by Cohesive and CivisAnalytics.
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.
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.
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.
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.
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.
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 DesignVaticle
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 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.
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.
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.
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.
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
For more Tendenci AMS events, check out www.tendenci.com/events
Strategies for Successful Data Migration Tools.pptxvarshanayak241
Data migration is a complex but essential task for organizations aiming to modernize their IT infrastructure and leverage new technologies. By understanding common challenges and implementing these strategies, businesses can achieve a successful migration with minimal disruption. Data Migration Tool like Ask On Data play a pivotal role in this journey, offering features that streamline the process, ensure data integrity, and maintain security. With the right approach and tools, organizations can turn the challenge of data migration into an opportunity for growth and innovation.
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
Why React Native as a Strategic Advantage for Startup Innovation.pdfayushiqss
Do you know that React Native is being increasingly adopted by startups as well as big companies in the mobile app development industry? Big names like Facebook, Instagram, and Pinterest have already integrated this robust open-source framework.
In fact, according to a report by Statista, the number of React Native developers has been steadily increasing over the years, reaching an estimated 1.9 million by the end of 2024. This means that the demand for this framework in the job market has been growing making it a valuable skill.
But what makes React Native so popular for mobile application development? It offers excellent cross-platform capabilities among other benefits. This way, with React Native, developers can write code once and run it on both iOS and Android devices thus saving time and resources leading to shorter development cycles hence faster time-to-market for your app.
Let’s take the example of a startup, which wanted to release their app on both iOS and Android at once. Through the use of React Native they managed to create an app and bring it into the market within a very short period. This helped them gain an advantage over their competitors because they had access to a large user base who were able to generate revenue quickly for them.
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
Designing for Privacy in Amazon Web ServicesKrzysztofKkol1
Data privacy is one of the most critical issues that businesses face. This presentation shares insights on the principles and best practices for ensuring the resilience and security of your workload.
Drawing on a real-life project from the HR industry, the various challenges will be demonstrated: data protection, self-healing, business continuity, security, and transparency of data processing. This systematized approach allowed to create a secure AWS cloud infrastructure that not only met strict compliance rules but also exceeded the client's expectations.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
Advanced Flow Concepts Every Developer Should KnowPeter Caitens
Tim Combridge from Sensible Giraffe and Salesforce Ben presents some important tips that all developers should know when dealing with Flows in Salesforce.
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
First Steps with Globus Compute Multi-User Endpoints
GRAKN.AI - The Knowledge Graph
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
The Knowledge Graph
for Intelligent Systems
By Haikal Pribadi
Founder and CEO of GRAKN.AI
@graknlabs
@haikalpribadi
2. Follow us @GraknLabs
1960 1970 1980 1990 2000 2010 2020 2030
Punch cards
& Tapes
Record Keeping
A BRIEF HISTORY OF DATABASES
3. Follow us @GraknLabs
1960 1970 1980 1990 2000 2010 2020 2030
Punch cards
& Tapes
Navigational
Databases
SCALE
Record Keeping
A BRIEF HISTORY OF DATABASES
4. Follow us @GraknLabs
1960 1970 1980 1990 2000 2010 2020 2030
Business Intelligence (BI)
Punch cards
& Tapes
Navigational
Databases
SCALE
Record Keeping
“Wouldn’t it be nice if you could express the question at a higher level and
let the system figure out how to do the navigation?”
Edgar F. Codd, Inventor of Relational Databases
A BRIEF HISTORY OF DATABASES
5. Follow us @GraknLabs
1960 1970 1980 1990 2000 2010 2020 2030
Relational/SQL
Databases
Business Intelligence (BI)
SCALE
COMPLEXITY
RELATIONAL DB WAS INVENTED TO SOLVE COMPLEXITY
“Wouldn’t it be nice if you could express the question at a higher level and
let the system figure out how to do the navigation?”
Edgar F. Codd, Inventor of Relational Databases
Punch cards
& Tapes
Navigational
Databases
Record Keeping
6. Follow us @GraknLabs
1960 1970 1980 1990 2000 2010 2020 2030
Relational/SQL
Databases
Business Intelligence (BI)
Web Applications
COMPLEXITY
A BRIEF HISTORY OF DATABASES
Punch cards
& Tapes
Navigational
Databases
Record Keeping
SCALE
7. Follow us @GraknLabs
1960 1970 1980 1990 2000 2010 2020 2030
Relational/SQL
Databases
NoSQL & NewSQL
Databases
SCALE
Business Intelligence (BI)
Web Applications
COMPLEXITY
A BRIEF HISTORY OF DATABASES
Punch cards
& Tapes
Navigational
Databases
Record Keeping
SCALE
8. Follow us @GraknLabs
1960 1970 1980 1990 2000 2010 2020 2030
Relational/SQL
Databases
NoSQL & NewSQL
Databases
SCALE
Business Intelligence (BI)
Web Applications
Artificial Intelligence (AI)
COMPLEXITY
A BRIEF HISTORY OF DATABASES
Punch cards
& Tapes
Navigational
Databases
Record Keeping
SCALE
9. Follow us @GraknLabs
1960 1970 1980 1990 2000 2010 2020 2030
Relational/SQL
Databases
NoSQL & NewSQL
Databases
SCALE
COMPLEXITY
COMPLEXITY
Business Intelligence (BI)
Web Applications
Intelligent Systems
?
INTELLIGENT SYSTEMS PROCESS DATA THAT IS TOO COMPLEX FOR CURRENT DATABASES
Punch cards
& Tapes
Navigational
Databases
Record Keeping
SCALE
10. Follow us @GraknLabs
1960 1970 1980 1990 2000 2010 2020 2030
Relational/SQL
Databases
NoSQL & NewSQL
Databases
Business Intelligence (BI)
Web Applications
Artificial Intelligence (AI)
SCALE
COMPLEXITY
SCALE
COMPLEXITY
WHAT RELATIONAL DID FOR BI, IS WHAT GRAKN WILL DO FOR AI
Punch cards
& Tapes
Navigational
Databases
Record Keeping
11. Follow us @GraknLabs
What is the problem with complex data?
Too complex to model
Current modelling
techniques only based on
binary relationships
Could not model complex
domains
Too complex to query
Current languages only allow
you to query for explicitly
stored data
Could not simplify verbose
queries
Too expensive analytics
Automated distributed
algorithms (BSP) expensive
and not reusable
Could not reuse analytics
algorithms
DB QLs are too low-level
Strong abstraction over low-
level constructs and
complex relationships
Difficult to work with complex
data
12. Follow us @GraknLabs
GRAKN.AI the knowledge base
foundation for intelligent systems
i.e.
GRAKN.AI is a knowledge graphKnowledge Storage System
Novel Knowledge Representation System based on Hypergraph
Theory
Knowledge Inference
OLTP Reasoning Engine
Knowledge Analytics
OLAP Distributed Analytics
13. Follow us @GraknLabs
What is a knowledge graph?
Knowledge schema
Flexible Entity-Relationship
concept-level schema to
build knowledge models
Model complex
domains
Logical Inference
Automated deductive
reasoning of data points
during runtime (OLTP)
Derive implicit facts &
simplification
Distributed Analytics
Automated distributed
algorithms (BSP) as a
language (OLAP)
Automated large scale
analytics
Higher-Level Language
Strong abstraction over low-
level constructs and
complex relationships
Easier to work with
complex data
14. Follow us @GraknLabs
THE KNOWLEDGE SCHEMA
A knowledge base needs to be able to model the real world and all the
type hierarchies, hyper-relationships and rules contained within it.
15. Follow us @GraknLabs
Schema Example: Basic Model
Employ-
ment
Person CompanyName
Employee Employer
has has
relates relates
plays plays
16. Follow us @GraknLabs
Schema Example: Type-Hierarchy
Employ-
ment
Person
Customer
Company
Startup
Name
Employee Employer
has has
sub sub
relates relates
plays plays
plays plays
17. Follow us @GraknLabs
Schema Example: Type-Hierarchy
Employ-
ment
Person
Customer
Company
Startup
Name
Employee Employer
has has
sub sub
relates relates
plays plays
Husband
Wife
Marriage
plays
plays
relates
relates
18. Follow us @GraknLabs
Valid Data Insertion
Alice Bob
IBM
Grakn
mar
emp
emp
employer
employer
wife husband
em
ployee
em
ployee
✓ Write commit success
customerperson
startup
19. Follow us @GraknLabs
Invalid Data insertions – [intelligent] Schema Constraints are Back!
Charlie Applemar
husband wife
companyperson
Write commit fails
Invalid relationship
20. Follow us @GraknLabs
Hyper-Relationship Example: Nested-Relationship
Alice Bob
Austin
mar
loc
locating
wife husband
located
personperson
City
07/01/2017
has
date
22. Follow us @GraknLabs
Rule Example: Transitive Relationship
Kings
Cross London
loc
countryward
UK
loc
city
loc
located locating
locating
locating
located
located
23. Follow us @GraknLabs
Rule Example: Simple Business Rule
Schedule A
Schedule B
A Start B Start A End B end
24. Follow us @GraknLabs
THE INFERENCE OLTP LANGUAGE
A knowledge-oriented query language should not only be able to
retrieve explicitly stored data, but also implicitly derived information.
25. Follow us @GraknLabs
Complex Query Example
drive
drive
drive
travel
travel
travel
Alice
Full-time Emp
Bob
Part-time Emp
Charlie
Temporary Emp
AB123
Bus
BC234
Van
CD345
Truck
Kings
Cross
Ward
London
City
UK
Country
loc
loc
Who are all the
drivers that will be
arriving in the UK?
driver
driver
locating
located
locatedlocating
driver
driven
driven
driven
destination
destination
destination
travellertraveller
The query would be very
long and complex in SQL,
NoSQL or even Graphs
26. Follow us @GraknLabs
Complex Query Example: Type and Relationship Inference
drive
drive
drive
travel
travel
travel
Alice
Full-time Emp
Bob
Part-time Emp
Charlie
Temporary Emp
AB123
Bus
BC234
Van
CD345
Truck
Kings
Cross
Ward
London
City
UK
Country
loc
loc
Who are all the
drivers that will be
arriving in the UK?
driver
driver
locating
located
locatedlocating
driver
driven
driven
driven
destination
destination
destination
travellertraveller
27. Follow us @GraknLabs
THE ANALYTICS OLAP LANGUAGE
Large-scale analytics is like teenage sex: everyone talks about it,
nobody really knows how to do it, everyone thinks everyone else is
doing it, so everyone claims they are doing it too.
At the end of the day, very few people know how to code it.
28. Follow us @GraknLabs
Example of a Distributed Analytics Algorithm
For each vertex V,
Superstep 1:
V sends its own id via both out going and incoming edges
V sets its own id as cluster label
Do superstep n:
For every received message m of V, compare it to its current cluster label L:
If m > L, set the label to m;
If the cluster label has not changed in this super step, vote to halt;
Else, send the new cluster label via all edges;
Global operation:
While not every vertex votes to halt, and n < N, do another superstep n + 1.
Connected Component: a clustering algorithm (pseudocode)
An efficient implementation
of this algorithm is about
200 lines of code in Java
29. Follow us @GraknLabs
Example of a Distributed Analytics Algorithm
For each vertex V,
Superstep 1:
V sends its own id via both out going and incoming edges
V sets its own id as cluster label
Do superstep n:
For every received message m of V, compare it to its current cluster label L:
If m > L, set the label to m;
If the cluster label has not changed in this super step, vote to halt;
Else, send the new cluster label via all edges;
Global operation:
While not every vertex votes to halt, and n < N, do another superstep n + 1.
Connected Component: a clustering algorithm (pseudocode)
An efficient implementation
of this algorithm is about
200 lines of code in Java
30. Follow us @GraknLabs
Graql Distributed Analytics Queries
And we’ll continue to add more
algorithms into the language,
such as PageRank, K-Core, Triangle
Count, Density, Cliques, Centrality,
and so on
32. Follow us @GraknLabs
G R A K N
G R A Q L
Grakn is the distributed knowledge base to store complex data. It contains a knowledge
representation system built on top of distributed computing technology stacks.
Graql is a query language that uses machine reasoning to interpret complex relationships &
retrieve implicitly derived knowledge from Grakn. It has a reasoning and analytics engine.
Reasoning Engine
Real-time inference for OLTP
Analytics Engine
Distributed analytics for OLAP
Knowledge Representation System
Novel approach based on hypergraph theory
Automated Reasoning OLTP query language
Interprets complex relationships and infer implicit information
Guarantees logical integrity, like SQL
Real time validation of data wrt. a more expressive schema constraint
Distributed Analytics OLAP query language
Interprets complex relationships and infer implicit information
Expressive Knowledge Representation System
Contains types, subtypes, hyper-relations, rules and instances
High Scale of Relationships, like Graph DBs
Relationships are first class citizens and easy to query without joins
Scales Horizontally, like NoSQL
Scaling by sharding and replication, with linear query throughput
What makes Grakn a Knowledge Graph?
33. 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?
34. 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."
35. 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."
36. Follow us @GraknLabs
THE ARCHITECTURE OF A COGNITIVE SYSTEM
Natural Language Processing
Knowledge Base Machine LearningKnowledge Acquisition
37. 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
39. Follow us @GraknLabs
GRAKN IS ENABLING DEVELOPMENTS OF AI IN FINANCE & LIFE SCIENCE
FINANCIAL MARKET
KNOWLEDGE BASE
Building a financial market knowledge
base by aggregating information of
real world events to predict the price
movements of different asset classes
CROP SCIENCE
KNOWLEDGE BASE
Building a crop science knowledge
base from half a million field crop trials
data to understand the performance of
different crop varietals and strains
HUMAN GENOMICS
KNOWLEDG BASE
Building a life science knowledge base
by aggregating public & proprietary bio
datasets to drive scientific discovery in
the fields of human genomics