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The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise boosts blood flow and levels of neurotransmitters and endorphins which elevate and stabilize mood.
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Systems that learn and reason | Frank Van Harmelen
After the amazing breakthroughs of machine learning (deep learning or otherwise) in the past decade, the shortcomings of machine learning are also becoming increasingly clear: unexplainable results, data hunger and limited generalisability are all becoming bottlenecks.
In this talk we will look at how the combination with symbolic AI (in the form of very large knowledge graphs) can give us a way forward, towards machine learning systems that can explain their results, that need less data, and that generalise better outside their training set.
--
Frank van Harmelen leads the Knowledge Representation & Reasoning group in the CS Department of the VU University Amsterdam. He is also Principal investigator of the Hybrid Intelligence Centre, a 20Μ€, 10 year collaboration between researchers at 6 Dutch universities into AI that collaborates with people instead of replacing them.
--
Graph Abstractions Matter by Ora Lassila
While mathematicians have used graph theory since the 18th century to solve problems, the software patterns for graph data are new to most developers. To enable "mass adoption" of graph technology, we need to establish the right abstractions, access APIs, and data models.
RDF triples, while of paramount importance in establishing RDF graph semantics, are a low-level abstraction, much like using assembly language. For practical and productive “graph programming” we need something different.
Similarly, existing declarative graph query languages (such as SPARQL and Cypher) are not always the best way to access graph data, and sometimes you need a simpler interface (e.g., GraphQL), or even a different approach altogether (e.g., imperative traversals such as with Gremlin).
Ora Lassila is a Principal Graph Technologist in the Amazon Neptune graph database group. He has a long experience with graphs, graph databases, ontologies, and knowledge representation. He was a co-author of the original RDF specification as well as a co-author of the seminal article on the Semantic Web.
Κnowledge Architecture: Combining Strategy, Data Science and Information Arch...
Κnowledge Architecture: Combining Strategy, Data Science and Information Arch...Connected Data World
"The most important contribution management needs to make in the 21st Century is to increase the productivity of knowledge work and the knowledge worker", said Peter F. Drucker in 1999, and time has proven him right.
Even NASA is no exception, as it faces a number of challenges. NASA has hundreds of millions of documents, reports, project data, lessons learned, scientific research, medical analysis, geospatial data, IT logs, and all kinds of other data stored nation-wide.
The data is growing in terms of variety, velocity, volume, value and veracity. NASA needs to provide accessibility to engineering data sources, whose visibility is currently limited. To convert data to knowledge a convergence of Knowledge Management, Information Architecture and Data Science is necessary.
This is what David Meza, Acting Branch Chief - People Analytics, Sr. Data Scientist at NASA, calls "Knowledge Architecture": the people, processes, and technology of designing, implementing, and applying the intellectual infrastructure of organizations.How to get started with Graph Machine Learning
A talk by Aleksa Gordic | Software - Deep Learning engineer, Microsoft | The AI Epiphany
What can you learn about Graph Machine Learning in 2 months?
Aleksa Gordic, Machine Learning engineer @ Microsoft and Founder @ The AI Epiphany, shares his journey in the world of Graph Machine Learning. Aleksa started exploring the basics in the world of Graph Machine Learning, and ended up implementing and open sourcing his own Graph Attention Network on PyTorch.
In this talk, Aleksa will share the fundamentals of Graph Machine Learning, provide real-world examples, resources, and everything his younger self would be grateful for. Aleksa will also be available to answer questions.
What is Graph Machine Learning? Simply put, Graph Machine Learning is a branch of machine learning that deals with graph data.
Graphs consist of nodes, that may have feature vectors associated with them, and edges, which again may or may not have feature vectors attached. The applications are endless. Massive-scale recommender systems, particle physics, computational pharmacology / chemistry / biology, traffic prediction, fake news detection, and the list goes on and on.
Graphs in sustainable finance
In recent years graphs have been increasingly adopted in financial services for everything from fraud detection to Know Your Customer (KYC) to regulatory requirements. At the same time Environmental Social Governance (ESG) investing has become the fastest growing segment of financial services. In this session James discusses how many of these historical graph techniques are now being enhanced for the era of sustainable investing. Going beyond definitions, let's identify use cases, discuss news and trends, and wrap up with an ask me anything session.
The years of the graph: The future of the future is here
What is graph all about, and why should you care? Graphs come in many shapes and forms, and can be used for different applications: Graph Analytics, Graph AI, Knowledge Graphs, and Graph Databases.
Talk by George Anadiotis. Connected Data London Meetup June 29th 2020.
Up until the beginning of the 2010s, the world was mostly running on spreadsheets and relational databases. To a large extent, it still does. But the NoSQL wave of databases has largely succeeded in instilling the “best tool for the job” mindset.
After relational, key-value, document, and columnar, the latest link in this evolutionary proliferation of data structures is graph. Graph analytics, Graph AI, Knowledge Graphs and Graph Databases have been making waves, included in hype cycles for the last couple of years.
The Year of the Graph marked the beginning of it all before the Gartners of the world got in the game. The Year of the Graph is a term coined to convey the fact that the time has come for this technology to flourish.
The eponymous article that set the tone was published in January 2018 on ZDNet by domain expert George Anadiotis. George has been working with, and keeping an eye on, all things Graph since the early 2000s. He was one of the first to note the continuing rise of Graph Databases, and to bring this technology in front of a mainstream audience.
The Year of the Graph has been going strong since 2018. In August 2018, Gartner started including Graph in its hype cycles. Ever since, Graph has been riding the upward slope of the Hype Cycle.
The need for knowledge on these technologies is constantly growing. To respond to that need, the Year of the Graph newsletter was released in April 2018. In addition, a constant flow of graph-related news and resources is being shared on social media.
To help people make educated choices, the Year of the Graph Database Report was released. The report has been hailed as the most comprehensive of its kind in the market, consistently helping people choose the most appropriate solution for their use case since 2018.
The report, articles, news stream, and the newsletter have been reaching thousands of people, helping them understand and navigate this landscape. We’ll talk about the Year of the Graph, the different shapes, forms, and applications for graphs, the latest news and trends, and wrap up with an ask me anything session.
From Taxonomies and Schemas to Knowledge Graphs: Parts 1 & 2
Do you have experience in data modeling, or using taxonomies to classify things, and want to upgrade to modeling knowledge graphs? This hands-on workshop with one of the leading knowledge graph practitioners will help you get started.
Parts 1 & 2
From Taxonomies and Schemas to Knowledge Graphs: Part 3
Do you have experience in data modeling, or using taxonomies to classify things, and want to upgrade to modeling knowledge graphs? This hands-on workshop with one of the leading knowledge graph practitioners will help you get started.
Part 3
Recommended
Systems that learn and reason | Frank Van Harmelen
After the amazing breakthroughs of machine learning (deep learning or otherwise) in the past decade, the shortcomings of machine learning are also becoming increasingly clear: unexplainable results, data hunger and limited generalisability are all becoming bottlenecks.
In this talk we will look at how the combination with symbolic AI (in the form of very large knowledge graphs) can give us a way forward, towards machine learning systems that can explain their results, that need less data, and that generalise better outside their training set.
--
Frank van Harmelen leads the Knowledge Representation & Reasoning group in the CS Department of the VU University Amsterdam. He is also Principal investigator of the Hybrid Intelligence Centre, a 20Μ€, 10 year collaboration between researchers at 6 Dutch universities into AI that collaborates with people instead of replacing them.
--
Graph Abstractions Matter by Ora Lassila
While mathematicians have used graph theory since the 18th century to solve problems, the software patterns for graph data are new to most developers. To enable "mass adoption" of graph technology, we need to establish the right abstractions, access APIs, and data models.
RDF triples, while of paramount importance in establishing RDF graph semantics, are a low-level abstraction, much like using assembly language. For practical and productive “graph programming” we need something different.
Similarly, existing declarative graph query languages (such as SPARQL and Cypher) are not always the best way to access graph data, and sometimes you need a simpler interface (e.g., GraphQL), or even a different approach altogether (e.g., imperative traversals such as with Gremlin).
Ora Lassila is a Principal Graph Technologist in the Amazon Neptune graph database group. He has a long experience with graphs, graph databases, ontologies, and knowledge representation. He was a co-author of the original RDF specification as well as a co-author of the seminal article on the Semantic Web.
Κnowledge Architecture: Combining Strategy, Data Science and Information Arch...
Κnowledge Architecture: Combining Strategy, Data Science and Information Arch...Connected Data World
"The most important contribution management needs to make in the 21st Century is to increase the productivity of knowledge work and the knowledge worker", said Peter F. Drucker in 1999, and time has proven him right.
Even NASA is no exception, as it faces a number of challenges. NASA has hundreds of millions of documents, reports, project data, lessons learned, scientific research, medical analysis, geospatial data, IT logs, and all kinds of other data stored nation-wide.
The data is growing in terms of variety, velocity, volume, value and veracity. NASA needs to provide accessibility to engineering data sources, whose visibility is currently limited. To convert data to knowledge a convergence of Knowledge Management, Information Architecture and Data Science is necessary.
This is what David Meza, Acting Branch Chief - People Analytics, Sr. Data Scientist at NASA, calls "Knowledge Architecture": the people, processes, and technology of designing, implementing, and applying the intellectual infrastructure of organizations.How to get started with Graph Machine Learning
A talk by Aleksa Gordic | Software - Deep Learning engineer, Microsoft | The AI Epiphany
What can you learn about Graph Machine Learning in 2 months?
Aleksa Gordic, Machine Learning engineer @ Microsoft and Founder @ The AI Epiphany, shares his journey in the world of Graph Machine Learning. Aleksa started exploring the basics in the world of Graph Machine Learning, and ended up implementing and open sourcing his own Graph Attention Network on PyTorch.
In this talk, Aleksa will share the fundamentals of Graph Machine Learning, provide real-world examples, resources, and everything his younger self would be grateful for. Aleksa will also be available to answer questions.
What is Graph Machine Learning? Simply put, Graph Machine Learning is a branch of machine learning that deals with graph data.
Graphs consist of nodes, that may have feature vectors associated with them, and edges, which again may or may not have feature vectors attached. The applications are endless. Massive-scale recommender systems, particle physics, computational pharmacology / chemistry / biology, traffic prediction, fake news detection, and the list goes on and on.
Graphs in sustainable finance
In recent years graphs have been increasingly adopted in financial services for everything from fraud detection to Know Your Customer (KYC) to regulatory requirements. At the same time Environmental Social Governance (ESG) investing has become the fastest growing segment of financial services. In this session James discusses how many of these historical graph techniques are now being enhanced for the era of sustainable investing. Going beyond definitions, let's identify use cases, discuss news and trends, and wrap up with an ask me anything session.
The years of the graph: The future of the future is here
What is graph all about, and why should you care? Graphs come in many shapes and forms, and can be used for different applications: Graph Analytics, Graph AI, Knowledge Graphs, and Graph Databases.
Talk by George Anadiotis. Connected Data London Meetup June 29th 2020.
Up until the beginning of the 2010s, the world was mostly running on spreadsheets and relational databases. To a large extent, it still does. But the NoSQL wave of databases has largely succeeded in instilling the “best tool for the job” mindset.
After relational, key-value, document, and columnar, the latest link in this evolutionary proliferation of data structures is graph. Graph analytics, Graph AI, Knowledge Graphs and Graph Databases have been making waves, included in hype cycles for the last couple of years.
The Year of the Graph marked the beginning of it all before the Gartners of the world got in the game. The Year of the Graph is a term coined to convey the fact that the time has come for this technology to flourish.
The eponymous article that set the tone was published in January 2018 on ZDNet by domain expert George Anadiotis. George has been working with, and keeping an eye on, all things Graph since the early 2000s. He was one of the first to note the continuing rise of Graph Databases, and to bring this technology in front of a mainstream audience.
The Year of the Graph has been going strong since 2018. In August 2018, Gartner started including Graph in its hype cycles. Ever since, Graph has been riding the upward slope of the Hype Cycle.
The need for knowledge on these technologies is constantly growing. To respond to that need, the Year of the Graph newsletter was released in April 2018. In addition, a constant flow of graph-related news and resources is being shared on social media.
To help people make educated choices, the Year of the Graph Database Report was released. The report has been hailed as the most comprehensive of its kind in the market, consistently helping people choose the most appropriate solution for their use case since 2018.
The report, articles, news stream, and the newsletter have been reaching thousands of people, helping them understand and navigate this landscape. We’ll talk about the Year of the Graph, the different shapes, forms, and applications for graphs, the latest news and trends, and wrap up with an ask me anything session.
From Taxonomies and Schemas to Knowledge Graphs: Parts 1 & 2
Do you have experience in data modeling, or using taxonomies to classify things, and want to upgrade to modeling knowledge graphs? This hands-on workshop with one of the leading knowledge graph practitioners will help you get started.
Parts 1 & 2
From Taxonomies and Schemas to Knowledge Graphs: Part 3
Do you have experience in data modeling, or using taxonomies to classify things, and want to upgrade to modeling knowledge graphs? This hands-on workshop with one of the leading knowledge graph practitioners will help you get started.
Part 3
In Search of the Universal Data Model
For as long as people have been thinking about thinking, we have imagined that somewhere in the inner reaches of our minds there are ghostly, intangible things called ideas which can be linked together to create representations of the world around us — a world that has a certain structure, conforms to certain rules, and to a certain extent, can be predicted and manipulated on the basis of our ideas.
Rationalist philosophers have struggled for centuries to make a solid case for this intuitive, almost inborn view of human experience, but it is only with the advent of modern computing that we have the opportunity to build machines which truly think the way we think we think.
For the first time, we can give concrete form to our mental representations as graphs or hypergraphs, explicitly specify our mental schemas as ontologies, and formally define the rules by which we reason and act on new information. If we so choose, we can even use these human-like building blocks to construct systems that carry far more information than any single human brain, and that connect and serve millions of people in real time.
As enterprise knowledge graphs become increasingly mainstream, we appear to be headed in that direction, although there is no guarantee that the momentum will continue unless actively sustained. Where knowledge graphs are likely to be the most essential, in the long run, is at the interface between human and machine; mental representation versus formal knowledge representation.
In this talk, we will take a step back from the many practical and social challenges of building large-scale knowledge graphs, which at this point are well-known. Instead, we will take up the quest for an ideal data model for knowledge representation and data integration, seeking common ground among the most popular data models used in industry and open source software, surveying what we suspect to be true of our own inner models, and previewing structure and process in Apache TinkerPop, version 4. We will also take a tentative step forward into the world of augmented perception via graph stream processing.
Graph in Apache Cassandra. The World’s Most Scalable Graph Database
Graph databases are everywhere right now. The explosive growth in the graph market coupled with the hype of solving graph problems is causing both excitement and confusion. From labeled property graphs to RDF to pure graph analytics to multi-model databases, the breadth of graph offerings is staggering.
The good news? DataStax has been listening—and building.
In this session, we’ll show you how DataStax Graph is architected into Apache Cassandra to deliver the world’s most scalable graph database. You’ll learn how to integrate Cassandra data into mixed workloads, design scalable property graphs, and even turn your existing tables into graphs.
With your high throughput time series data distributed next to its relationships, what will you build next?
Enterprise Data Governance: Leveraging Knowledge Graph & AI in support of a d...
Enterprise Data Governance: Leveraging Knowledge Graph & AI in support of a d...Connected Data World
As one of the largest financial institutions worldwide, JP Morgan is reliant on data to drive its day-to-day operations, against an ever evolving regulatory regime. Our global data landscape possesses particular challenges of effectively maintaining data governance and metadata management.
The Data strategy at JP Morgan intends to:
a) generate business value
b) adhere to regulatory & compliance requirements
c) reduce barriers to access
d) democratize access to data
In this talk, we show how JP Morgan leverages semantic technologies to drive the implementation of our data strategy. We demonstrate how we exploit knowledge graph capabilities to answer:
1) What Data do I need?
2) What Data do we have?
3) Where does my Data come from?
4) Where should my Data come from?
5) What Data should be shared most?Graph Realities
Graph applications were once considered “exotic” and expensive. Until recently, few software engineers had much experience putting graphs to work. However, the use cases are now becoming more commonplace.
This talk explores a practical use case, one which addresses key issues of data governance and reproducible research, and depends on sophisticated use of graph technology.
Consider: some academic disciplines such as astronomy enjoy a wealth of data — mostly open data. Popular machine learning algorithms, open source Python libraries, and distributed systems all owe much to those disciplines and their history of big data.
Other disciplines require strong guarantees for privacy and security. Datasets used in social science research involve confidential details about human subjects: medical histories, wages, home addresses for family members, police records, etc.
Those cannot be shared openly, which impedes researchers from learning about related work by others. Reproducibility of research and the pace of science in general are limited. Nonetheless, social science research is vital for civil governance, especially for evidence-based policymaking (US federal law since 2018).
Even when data may be too sensitive to share openly, often the metadata can be shared. Constructing knowledge graphs of metadata about datasets — along with metadata about authors, their published research, methods used, data providers, data stewards, and so on — that provides effective means to tackle hard problems in data governance.
Knowledge graph work supports use cases such as entity linking, discovery and recommendations, axioms to infer about compliance, etc. This talk reviews the Rich Context AI competition and the related ADRF framework used now by more than 15 federal agencies in the US.
We’ll explore knowledge graph use cases, use of open standards and open source, and how this enhances reproducible research. Social science research for the public sector has much in common with data use in industry.
Issues of privacy, security, and compliance overlap, pointing toward what will be required of banks, media channels, etc., and what technologies apply. We’ll look at comparable work emerging in other parts of industry: open source projects, open standards emerging, and in particular a new set of features in Project Jupyter that support knowledge graphs about data governance.
Powering Question-Driven Problem Solving to Improve the Chances of Finding Ne...
Powering Question-Driven Problem Solving to Improve the Chances of Finding Ne...Connected Data World
Making true “molecule”-“mechanism”-“observation” relationship connections is a time consuming, iterative and laborious process. In addition, it is very easy to miss critical information that affects key decisions or helps make plausible scientific connections.
The current practice for deciphering such relationships frequently involves subject matter experts (SMEs) requesting resource from resource-constrained data science departments to refine and redo highly similar ad hoc searches. The result of this is impairment of both the pace and quality of scientific reviews.
In this presentation, I show how semantic integration can be made to ultimately become part of an integrated learning framework for more informed scientific decision making. I will take the audience through our pilot journey and highlight practical learnings that should inform subsequent endeavours.Semantic similarity for faster Knowledge Graph delivery at scale
Knowledge graphs promise a novel platform for better holistic decision making and analytics. Many projects fail to reach their full potential because of the prohibitively high cost of integrating new knowledge from the required information sources.
The talk explains the concept of semantic similarity as a tool for efficient entity clustering and matching based on graph and text embeddings. It will demonstrate the underlying scalable and easy to understand algorithm of Random Indexing.
This work is part of the Ontotext Platform, which increases productivity in developing and maintaining large scale knowledge graphs. The platform enables enterprises to develop and operate on top of such mission-critical systems for decision support, information discovery and metadata management.
Knowledge Graphs and AI to Hyper-Personalise the Fashion Retail Experience at...
Knowledge Graphs and AI to Hyper-Personalise the Fashion Retail Experience at...Connected Data World
What is the key to the holistic success of the fastest growing and most successful companies of our time globally? Well, often the key is the rapid increase in collected and analysed data. Graph databases provide a way to organise semantically by classes, not tables, are web-aware, and are superior for handling deep, complex relationships than traditional relational or NoSQL data stores.
It is these deep, complex relationships that can provide the rich context for hyper-personalising your product offering, inspiring consumers to purchase. In this talk, we describe how we are using artificial intelligence at Farfetch to not only help build a knowledge graph but also to evolve our insights with state-of-the-art graph-based AI.Schema, Google & The Future of the Web
A world of structured data promises us an incredible future. But most websites struggle to even implement basic schema.org markup. Fewer still represent and connect their pages and content in sophisticated, structured graphs. We can’t reach that incredible future without increasing and improving adoption.
To move forward, we need to make constructing rich structured data as easy as writing a recipe. This isn’t a pipe dream: at Yoast, we think we’ve solved schema for everybody, everywhere. We’d love to share our story.
RAPIDS cuGraph – Accelerating all your Graph needs
The relationships between data sets matter. Discovering, analyzing, and learning those relationships is a central part to expanding our understand, and is a critical step to being able to predict and act upon the data. Unfortunately, these are not always simple or quick tasks.
To help the analyst we introduce RAPIDS, a collection of open-source libraries, incubated by NVIDIA and focused on accelerating the complete end-to-end data science ecosystem. Graph analytics is a critical piece of the data science ecosystem for processing linked data, and RAPIDS is pleased to offer cuGraph as our accelerated graph library.
Simply accelerating algorithms only addressed a portion of the problem. To address the full problem space, RAPIDS cuGraph strives to be feature-rich, easy to use, and intuitive. Rather than limiting the solution to a single graph technology, cuGraph supports Property Graphs, Knowledge Graphs, Hyper-Graphs, Bipartite graphs, and the basic directed and undirected graph.
A Python API allows the data to be manipulated as a DataFrame, similar and compatible with Pandas, with inputs and outputs being shared across the full RAPIDS suite, for example with the RAPIDS machine learning package, cuML.
This talk will present an overview of RAPIDS and cuGraph. Discuss and show examples of how to manipulate and analyze bipartite and property graph, plus show how data can be shared with machine learning algorithms. The talk will include some performance and scalability metrics. Then conclude with a preview of upcoming features, like graph query language support, and the general RAPIDS roadmap.
Elegant and Scalable Code Querying with Code Property Graphs
Programming is an unforgiving art form in which even minor flaws can cause rockets to explode, data to be stolen, and systems to be compromised. Today, a system tasked to automatically identify these flaws not only faces the intrinsic difficulties and theoretical limits of the task itself, it must also account for the many different forms in which programs can be formulated and account for the awe-inspiring speed at which developers push new code into CI/CD pipelines. So much code, so little time.
The code property graph – a multi-layered graph representation of code that captures properties of code across different abstractions – (application code, libraries and frameworks) – has been developed over the last six years to provide a foundation for the challenging problem of identifying flaws in program code at scale, whether it is high-level dynamically-typed Javascript, statically-typed Scala in its bytecode form, the syntax trees generated by Roslyn C# compiler, or the bitcode that flows through LLVM.
Based on this graph, we define a common query language based on formal code property graph specification to elegantly analyze code regardless of the source language. Paired with the formulation of a state-of-the-art data flow tracker based on code property graphs, we arrive at a distributed cloud native powerful code analysis. This talk provides an introduction to the technology.
From Knowledge Graphs to AI-powered SEO: Using taxonomies, schemas and knowle...
From Knowledge Graphs to AI-powered SEO: Using taxonomies, schemas and knowle...Connected Data World
Do you want to learn how to use the low-hanging fruit of knowledge graphs — schema.org and JSON-LD — to annotate content and improve your SEO with semantics and entities? This hands-on workshop with one of the leading Semantic SEO practitioners will help you get started.Graph for Good: Empowering your NGO
May graph technology improve the deployment of humanitarian projects? The goal of using what we call “Graphs for good at Action Against Hunger” is to be more efficient and transparent, and this can have a crucial impact on people’s lives.
Is there common behaviour factors between different projects? Can elements of different resources or projects be related? For example, security incidents in a city could influence the way other projects run in there.
The explained use case data comes from a project called Kit For Autonomous Cash Transfer in Humanitarian Emergencies (KACHE) whose goal is to deploy electronic cash transfers in emergency situations when no suitable infrastructure is available.
It also offers the opportunity to track transactions in order to better recognize crisis-affected population behaviours, understanding goods distribution network to improve recommendations, identifying the role of culture in transactional patterns, as well as most required items for every place.
What are we Talking About, When we Talk About Ontology?
In this talk we will go back to basics with ontologies and from that project forwards to their future. I’ll base most of what I talk about on my experience in bio-ontologies, but most experience will be applicable in many domains; most domains are not as special as they think.
When it comes down to the basics, we need to know what our data represent or mean; that is where ontologies come into play; We need to know what we’re talking about. Once we have that clear we can proceed. There is much that one can do with data once we know what it means.
We can exploit those data through knowing what it represents. We can exploit these data better if our ontologies are also better. In taking this simple point of view forwards, I will use this talk to establish a set of principles for ontologists.
Ontology Services for the Biomedical Sciences
Ontologies and Semantic Web technologies play an important role in the life sciences to help make data more interoperable and reusable. There are now many publicly available ontologies that enable biologists to describe everything from gene function through to animal physiology and disease.
Various efforts such as the Open Biomedical Ontologies (OBO) foundry provide central registries for biomedical ontologies and ensure they remain interoperable through a set of common shared development principles.
At EMBL-EBI we contribute to the development of biomedical ontologies and make extensive use of them in the annotation of public datasets. Biological data typically comes with rich and often complex metadata, so the ontologies provide a standard way to capture “what the data is about” and gives us hooks to connect to more data about similar things.
These ontology annotations have been put to good use in a number of large-scale data integration efforts and there’s an increasing recognition of the need for ontologies in making data FAIR (Findable, Accessible, Interoperable and Reusable).
EMBL-EBI build a number of integrative data platforms where ontologies are at the core of our domain models. One example is the Open Targets platform, where data about disease from 18 different databases can be aggregated and grouped based on therapeutic areas in the ontology and used to identify potential drug targets.
The ontologies team at EMBL-EBI provide a suite of services that are aimed at making ontologies more accessible for both humans and machines. We work with scientific data curators and software developers to integrate ontologies and semantics into both the data generation and data presentation workflows. We provide:
– An ontology lookup service (OLS) that provides search and visualisation services to over 200+ ontologies
– Services for automating the annotation of metadata and learning from previous annotations (Zooma)
– An ontology mapping and alignment service (OXO)
– Tools for working with metadata and ontologies in spreadsheets (Webulous)
– Software for enriching documents in search engines to support “semantic” query expansion
I’ll present how we are using these services at EMBL-EBI to scale up the semantic annotation of metadata. I’ll talk about our open source technology stack and describe how we utilise a polyglot persistence approach (graph databases, triples stores, document stores etc) to optimize how we deliver ontologies and semantics to our users.
Develop A Basic Recommendation System using Cypher
Recommendation systems deliver more ROI than any other investment in Data Analytics. This talk will introduce the most basic but effective Recommendation System called Collaborative Filtering and show how to implement it using the Cypher Graph Query Language.
The mathematics behind collaborative filtering will be explained as will the usefulness of Graph in implementing such an engine. We will use AgensGraph, which adds graph capability to PostgreSQL, for the talk.
This talk is aimed at those who are new to either Cypher or basic recommendation system theory.
A Semi-Automatic Tool for Linked Data Integration
Linked Data is a set of best practices to publish data in RDF format. Despite the advantages of Linked Data (i.e., discoverability, interoperability, reusability, etc.) many datasets are not published in RDF.
Transformation of existing structured datasets into RDF is possible thanks to RDF Mappings. To be able to define such mappings, it is necessary to be familiar with the Linked Data practices and to know perfectly the datasets concerned.
An obstacle to the Linked Data democratization is that few people satisfy these two conditions. Tools making the process of Linked Data integration easier can foster Linked Open Data growth.
Opendatasoft proposes a chatbot-like tool that can semi-automatically generate RDF mappings for existing structured datasets. The challenge is to automate part of the integration process that requires getting familiar with Linked Data practices.
One Ontology, One Data Set, Multiple Shapes with SHACL
Data integration, data interoperation and data quality are major challenges that continue to haunt enterprises. Every enterprise either by choice or by chance has created massive silos of data in different formats, with duplications and quality issues.
Knowledge graphs have proven to be a viable solution to address the integration and interoperation problem. Semantic technologies in particular provide an intelligent way of creating an abstract layer for the enterprise data model and mapping of siloed data to that model, allowing a smooth integration and a common view of the data.
Technologies like OWL (Web Ontology Language) and RDF (Resource Description Framework) are the back bone of semantics for knowledge graph implementation. Enterprises use OWL to build an ontology model to create a common definition for concepts and how they are connected to each other in their specific domain.
They then use RDF to create a triple format representation of their data by mapping it to the Ontology. This approach makes their data smart and machine understandable.
But how can enterprises control and validate the quality of this mapped data? Furthermore, how can they use this one abstract representation of data to meet all their different business requirements? Different departments, different LoBs and different business branches all have their own data needs, creating a new challenge to be tackled by the enterprise.
In this talk we will look at how the power of SHACL (SHAPES and Constraints Language), a W3C standard for defining constraint sets over data; complements the two core semantic technologies OWL and RDF. What are the similarities, the overlaps and the differences.
We will talk about how SHACL gives enterprises the power to reuse, customize and validate their data for various scenarios, uses cases and business requirements; making the application of semantics even more practical.
Dow Jones: Reimagining the News as a Knowledge Graph
AI is transforming the financial media industry – impacting everything from content creation to consumption trends. Clancy Childs, general manager of Dow Jones’ knowledge enablement unit, will share insights into how Dow Jones is reimagining what the news looks like.
Learn how Dow Jones’ knowledge graph platform – powered by Stardog – enables the company to unify structured and unstructured data from a vast range of news sources and deliver cutting-edge insights for customers and partners globally.
How Graphs Continue to Revolutionize The Prevention of Financial Crime & Frau...
How Graphs Continue to Revolutionize The Prevention of Financial Crime & Frau...Connected Data World
Financial crime prevention is something that affects everyone in one way or another. From the Deutsche Banks of the world to small and medium online merchants, regulations for anti-money laundering, know your customer, and customer due diligence apply.
Failing to comply with such regulations can bring on substantial fines. Even more importantly, it can hurt the bottom line and reputation of businesses, having far-reaching side effects. Complying with such regulations, and actively cracking down on financial crime, however, is not easy.
Cross-referencing interconnected data across various datasets, and trying to apply detection rules and to discover patterns in the data is complicated. It takes expertise, effort, and the right technology to be able to do this efficiently.
A natural and efficient way of looking for patterns and applying rules in troves of interconnected data is to model and view that data as a graph. By modeling data as a graph, and applying graph-based algorithms such as PageRank or Centrality, traversing paths, discovering connections and getting insights becomes possible.
Graphs and graph databases are the fastest-growing area of data management technology for a number of reasons. One of the reasons is because they are a perfect match for use cases involving interconnected data.
Queries that would be very complicated to express and very slow to execute using relational databases or other NoSQL database technology, are feasible using graph databases. With the rise in complexity of modern financial markets, financial crimes require going 4 to 11 levels deep into the account – payment graph: this requires a different solution than either relational or NoSQL databases.
How are organizations such as Alibaba, OpenCorporates, and Visa using graph database technology to not just stay on top of regulation, but be one step ahead in the race against financial crime?
Is it possible to do this in real time?
What do graph query languages have to do with this?Graph intelligence: the future of data-driven investigations
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
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In Search of the Universal Data Model
For as long as people have been thinking about thinking, we have imagined that somewhere in the inner reaches of our minds there are ghostly, intangible things called ideas which can be linked together to create representations of the world around us — a world that has a certain structure, conforms to certain rules, and to a certain extent, can be predicted and manipulated on the basis of our ideas.
Rationalist philosophers have struggled for centuries to make a solid case for this intuitive, almost inborn view of human experience, but it is only with the advent of modern computing that we have the opportunity to build machines which truly think the way we think we think.
For the first time, we can give concrete form to our mental representations as graphs or hypergraphs, explicitly specify our mental schemas as ontologies, and formally define the rules by which we reason and act on new information. If we so choose, we can even use these human-like building blocks to construct systems that carry far more information than any single human brain, and that connect and serve millions of people in real time.
As enterprise knowledge graphs become increasingly mainstream, we appear to be headed in that direction, although there is no guarantee that the momentum will continue unless actively sustained. Where knowledge graphs are likely to be the most essential, in the long run, is at the interface between human and machine; mental representation versus formal knowledge representation.
In this talk, we will take a step back from the many practical and social challenges of building large-scale knowledge graphs, which at this point are well-known. Instead, we will take up the quest for an ideal data model for knowledge representation and data integration, seeking common ground among the most popular data models used in industry and open source software, surveying what we suspect to be true of our own inner models, and previewing structure and process in Apache TinkerPop, version 4. We will also take a tentative step forward into the world of augmented perception via graph stream processing.
Graph in Apache Cassandra. The World’s Most Scalable Graph Database
Graph databases are everywhere right now. The explosive growth in the graph market coupled with the hype of solving graph problems is causing both excitement and confusion. From labeled property graphs to RDF to pure graph analytics to multi-model databases, the breadth of graph offerings is staggering.
The good news? DataStax has been listening—and building.
In this session, we’ll show you how DataStax Graph is architected into Apache Cassandra to deliver the world’s most scalable graph database. You’ll learn how to integrate Cassandra data into mixed workloads, design scalable property graphs, and even turn your existing tables into graphs.
With your high throughput time series data distributed next to its relationships, what will you build next?
Enterprise Data Governance: Leveraging Knowledge Graph & AI in support of a d...
Enterprise Data Governance: Leveraging Knowledge Graph & AI in support of a d...Connected Data World
As one of the largest financial institutions worldwide, JP Morgan is reliant on data to drive its day-to-day operations, against an ever evolving regulatory regime. Our global data landscape possesses particular challenges of effectively maintaining data governance and metadata management.
The Data strategy at JP Morgan intends to:
a) generate business value
b) adhere to regulatory & compliance requirements
c) reduce barriers to access
d) democratize access to data
In this talk, we show how JP Morgan leverages semantic technologies to drive the implementation of our data strategy. We demonstrate how we exploit knowledge graph capabilities to answer:
1) What Data do I need?
2) What Data do we have?
3) Where does my Data come from?
4) Where should my Data come from?
5) What Data should be shared most?Graph Realities
Graph applications were once considered “exotic” and expensive. Until recently, few software engineers had much experience putting graphs to work. However, the use cases are now becoming more commonplace.
This talk explores a practical use case, one which addresses key issues of data governance and reproducible research, and depends on sophisticated use of graph technology.
Consider: some academic disciplines such as astronomy enjoy a wealth of data — mostly open data. Popular machine learning algorithms, open source Python libraries, and distributed systems all owe much to those disciplines and their history of big data.
Other disciplines require strong guarantees for privacy and security. Datasets used in social science research involve confidential details about human subjects: medical histories, wages, home addresses for family members, police records, etc.
Those cannot be shared openly, which impedes researchers from learning about related work by others. Reproducibility of research and the pace of science in general are limited. Nonetheless, social science research is vital for civil governance, especially for evidence-based policymaking (US federal law since 2018).
Even when data may be too sensitive to share openly, often the metadata can be shared. Constructing knowledge graphs of metadata about datasets — along with metadata about authors, their published research, methods used, data providers, data stewards, and so on — that provides effective means to tackle hard problems in data governance.
Knowledge graph work supports use cases such as entity linking, discovery and recommendations, axioms to infer about compliance, etc. This talk reviews the Rich Context AI competition and the related ADRF framework used now by more than 15 federal agencies in the US.
We’ll explore knowledge graph use cases, use of open standards and open source, and how this enhances reproducible research. Social science research for the public sector has much in common with data use in industry.
Issues of privacy, security, and compliance overlap, pointing toward what will be required of banks, media channels, etc., and what technologies apply. We’ll look at comparable work emerging in other parts of industry: open source projects, open standards emerging, and in particular a new set of features in Project Jupyter that support knowledge graphs about data governance.
Powering Question-Driven Problem Solving to Improve the Chances of Finding Ne...
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Making true “molecule”-“mechanism”-“observation” relationship connections is a time consuming, iterative and laborious process. In addition, it is very easy to miss critical information that affects key decisions or helps make plausible scientific connections.
The current practice for deciphering such relationships frequently involves subject matter experts (SMEs) requesting resource from resource-constrained data science departments to refine and redo highly similar ad hoc searches. The result of this is impairment of both the pace and quality of scientific reviews.
In this presentation, I show how semantic integration can be made to ultimately become part of an integrated learning framework for more informed scientific decision making. I will take the audience through our pilot journey and highlight practical learnings that should inform subsequent endeavours.Semantic similarity for faster Knowledge Graph delivery at scale
Knowledge graphs promise a novel platform for better holistic decision making and analytics. Many projects fail to reach their full potential because of the prohibitively high cost of integrating new knowledge from the required information sources.
The talk explains the concept of semantic similarity as a tool for efficient entity clustering and matching based on graph and text embeddings. It will demonstrate the underlying scalable and easy to understand algorithm of Random Indexing.
This work is part of the Ontotext Platform, which increases productivity in developing and maintaining large scale knowledge graphs. The platform enables enterprises to develop and operate on top of such mission-critical systems for decision support, information discovery and metadata management.
Knowledge Graphs and AI to Hyper-Personalise the Fashion Retail Experience at...
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What is the key to the holistic success of the fastest growing and most successful companies of our time globally? Well, often the key is the rapid increase in collected and analysed data. Graph databases provide a way to organise semantically by classes, not tables, are web-aware, and are superior for handling deep, complex relationships than traditional relational or NoSQL data stores.
It is these deep, complex relationships that can provide the rich context for hyper-personalising your product offering, inspiring consumers to purchase. In this talk, we describe how we are using artificial intelligence at Farfetch to not only help build a knowledge graph but also to evolve our insights with state-of-the-art graph-based AI.Schema, Google & The Future of the Web
A world of structured data promises us an incredible future. But most websites struggle to even implement basic schema.org markup. Fewer still represent and connect their pages and content in sophisticated, structured graphs. We can’t reach that incredible future without increasing and improving adoption.
To move forward, we need to make constructing rich structured data as easy as writing a recipe. This isn’t a pipe dream: at Yoast, we think we’ve solved schema for everybody, everywhere. We’d love to share our story.
RAPIDS cuGraph – Accelerating all your Graph needs
The relationships between data sets matter. Discovering, analyzing, and learning those relationships is a central part to expanding our understand, and is a critical step to being able to predict and act upon the data. Unfortunately, these are not always simple or quick tasks.
To help the analyst we introduce RAPIDS, a collection of open-source libraries, incubated by NVIDIA and focused on accelerating the complete end-to-end data science ecosystem. Graph analytics is a critical piece of the data science ecosystem for processing linked data, and RAPIDS is pleased to offer cuGraph as our accelerated graph library.
Simply accelerating algorithms only addressed a portion of the problem. To address the full problem space, RAPIDS cuGraph strives to be feature-rich, easy to use, and intuitive. Rather than limiting the solution to a single graph technology, cuGraph supports Property Graphs, Knowledge Graphs, Hyper-Graphs, Bipartite graphs, and the basic directed and undirected graph.
A Python API allows the data to be manipulated as a DataFrame, similar and compatible with Pandas, with inputs and outputs being shared across the full RAPIDS suite, for example with the RAPIDS machine learning package, cuML.
This talk will present an overview of RAPIDS and cuGraph. Discuss and show examples of how to manipulate and analyze bipartite and property graph, plus show how data can be shared with machine learning algorithms. The talk will include some performance and scalability metrics. Then conclude with a preview of upcoming features, like graph query language support, and the general RAPIDS roadmap.
Elegant and Scalable Code Querying with Code Property Graphs
Programming is an unforgiving art form in which even minor flaws can cause rockets to explode, data to be stolen, and systems to be compromised. Today, a system tasked to automatically identify these flaws not only faces the intrinsic difficulties and theoretical limits of the task itself, it must also account for the many different forms in which programs can be formulated and account for the awe-inspiring speed at which developers push new code into CI/CD pipelines. So much code, so little time.
The code property graph – a multi-layered graph representation of code that captures properties of code across different abstractions – (application code, libraries and frameworks) – has been developed over the last six years to provide a foundation for the challenging problem of identifying flaws in program code at scale, whether it is high-level dynamically-typed Javascript, statically-typed Scala in its bytecode form, the syntax trees generated by Roslyn C# compiler, or the bitcode that flows through LLVM.
Based on this graph, we define a common query language based on formal code property graph specification to elegantly analyze code regardless of the source language. Paired with the formulation of a state-of-the-art data flow tracker based on code property graphs, we arrive at a distributed cloud native powerful code analysis. This talk provides an introduction to the technology.
From Knowledge Graphs to AI-powered SEO: Using taxonomies, schemas and knowle...
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Do you want to learn how to use the low-hanging fruit of knowledge graphs — schema.org and JSON-LD — to annotate content and improve your SEO with semantics and entities? This hands-on workshop with one of the leading Semantic SEO practitioners will help you get started.Graph for Good: Empowering your NGO
May graph technology improve the deployment of humanitarian projects? The goal of using what we call “Graphs for good at Action Against Hunger” is to be more efficient and transparent, and this can have a crucial impact on people’s lives.
Is there common behaviour factors between different projects? Can elements of different resources or projects be related? For example, security incidents in a city could influence the way other projects run in there.
The explained use case data comes from a project called Kit For Autonomous Cash Transfer in Humanitarian Emergencies (KACHE) whose goal is to deploy electronic cash transfers in emergency situations when no suitable infrastructure is available.
It also offers the opportunity to track transactions in order to better recognize crisis-affected population behaviours, understanding goods distribution network to improve recommendations, identifying the role of culture in transactional patterns, as well as most required items for every place.
What are we Talking About, When we Talk About Ontology?
In this talk we will go back to basics with ontologies and from that project forwards to their future. I’ll base most of what I talk about on my experience in bio-ontologies, but most experience will be applicable in many domains; most domains are not as special as they think.
When it comes down to the basics, we need to know what our data represent or mean; that is where ontologies come into play; We need to know what we’re talking about. Once we have that clear we can proceed. There is much that one can do with data once we know what it means.
We can exploit those data through knowing what it represents. We can exploit these data better if our ontologies are also better. In taking this simple point of view forwards, I will use this talk to establish a set of principles for ontologists.
Ontology Services for the Biomedical Sciences
Ontologies and Semantic Web technologies play an important role in the life sciences to help make data more interoperable and reusable. There are now many publicly available ontologies that enable biologists to describe everything from gene function through to animal physiology and disease.
Various efforts such as the Open Biomedical Ontologies (OBO) foundry provide central registries for biomedical ontologies and ensure they remain interoperable through a set of common shared development principles.
At EMBL-EBI we contribute to the development of biomedical ontologies and make extensive use of them in the annotation of public datasets. Biological data typically comes with rich and often complex metadata, so the ontologies provide a standard way to capture “what the data is about” and gives us hooks to connect to more data about similar things.
These ontology annotations have been put to good use in a number of large-scale data integration efforts and there’s an increasing recognition of the need for ontologies in making data FAIR (Findable, Accessible, Interoperable and Reusable).
EMBL-EBI build a number of integrative data platforms where ontologies are at the core of our domain models. One example is the Open Targets platform, where data about disease from 18 different databases can be aggregated and grouped based on therapeutic areas in the ontology and used to identify potential drug targets.
The ontologies team at EMBL-EBI provide a suite of services that are aimed at making ontologies more accessible for both humans and machines. We work with scientific data curators and software developers to integrate ontologies and semantics into both the data generation and data presentation workflows. We provide:
– An ontology lookup service (OLS) that provides search and visualisation services to over 200+ ontologies
– Services for automating the annotation of metadata and learning from previous annotations (Zooma)
– An ontology mapping and alignment service (OXO)
– Tools for working with metadata and ontologies in spreadsheets (Webulous)
– Software for enriching documents in search engines to support “semantic” query expansion
I’ll present how we are using these services at EMBL-EBI to scale up the semantic annotation of metadata. I’ll talk about our open source technology stack and describe how we utilise a polyglot persistence approach (graph databases, triples stores, document stores etc) to optimize how we deliver ontologies and semantics to our users.
Develop A Basic Recommendation System using Cypher
Recommendation systems deliver more ROI than any other investment in Data Analytics. This talk will introduce the most basic but effective Recommendation System called Collaborative Filtering and show how to implement it using the Cypher Graph Query Language.
The mathematics behind collaborative filtering will be explained as will the usefulness of Graph in implementing such an engine. We will use AgensGraph, which adds graph capability to PostgreSQL, for the talk.
This talk is aimed at those who are new to either Cypher or basic recommendation system theory.
A Semi-Automatic Tool for Linked Data Integration
Linked Data is a set of best practices to publish data in RDF format. Despite the advantages of Linked Data (i.e., discoverability, interoperability, reusability, etc.) many datasets are not published in RDF.
Transformation of existing structured datasets into RDF is possible thanks to RDF Mappings. To be able to define such mappings, it is necessary to be familiar with the Linked Data practices and to know perfectly the datasets concerned.
An obstacle to the Linked Data democratization is that few people satisfy these two conditions. Tools making the process of Linked Data integration easier can foster Linked Open Data growth.
Opendatasoft proposes a chatbot-like tool that can semi-automatically generate RDF mappings for existing structured datasets. The challenge is to automate part of the integration process that requires getting familiar with Linked Data practices.
One Ontology, One Data Set, Multiple Shapes with SHACL
Data integration, data interoperation and data quality are major challenges that continue to haunt enterprises. Every enterprise either by choice or by chance has created massive silos of data in different formats, with duplications and quality issues.
Knowledge graphs have proven to be a viable solution to address the integration and interoperation problem. Semantic technologies in particular provide an intelligent way of creating an abstract layer for the enterprise data model and mapping of siloed data to that model, allowing a smooth integration and a common view of the data.
Technologies like OWL (Web Ontology Language) and RDF (Resource Description Framework) are the back bone of semantics for knowledge graph implementation. Enterprises use OWL to build an ontology model to create a common definition for concepts and how they are connected to each other in their specific domain.
They then use RDF to create a triple format representation of their data by mapping it to the Ontology. This approach makes their data smart and machine understandable.
But how can enterprises control and validate the quality of this mapped data? Furthermore, how can they use this one abstract representation of data to meet all their different business requirements? Different departments, different LoBs and different business branches all have their own data needs, creating a new challenge to be tackled by the enterprise.
In this talk we will look at how the power of SHACL (SHAPES and Constraints Language), a W3C standard for defining constraint sets over data; complements the two core semantic technologies OWL and RDF. What are the similarities, the overlaps and the differences.
We will talk about how SHACL gives enterprises the power to reuse, customize and validate their data for various scenarios, uses cases and business requirements; making the application of semantics even more practical.
Dow Jones: Reimagining the News as a Knowledge Graph
AI is transforming the financial media industry – impacting everything from content creation to consumption trends. Clancy Childs, general manager of Dow Jones’ knowledge enablement unit, will share insights into how Dow Jones is reimagining what the news looks like.
Learn how Dow Jones’ knowledge graph platform – powered by Stardog – enables the company to unify structured and unstructured data from a vast range of news sources and deliver cutting-edge insights for customers and partners globally.
How Graphs Continue to Revolutionize The Prevention of Financial Crime & Frau...
How Graphs Continue to Revolutionize The Prevention of Financial Crime & Frau...Connected Data World
Financial crime prevention is something that affects everyone in one way or another. From the Deutsche Banks of the world to small and medium online merchants, regulations for anti-money laundering, know your customer, and customer due diligence apply.
Failing to comply with such regulations can bring on substantial fines. Even more importantly, it can hurt the bottom line and reputation of businesses, having far-reaching side effects. Complying with such regulations, and actively cracking down on financial crime, however, is not easy.
Cross-referencing interconnected data across various datasets, and trying to apply detection rules and to discover patterns in the data is complicated. It takes expertise, effort, and the right technology to be able to do this efficiently.
A natural and efficient way of looking for patterns and applying rules in troves of interconnected data is to model and view that data as a graph. By modeling data as a graph, and applying graph-based algorithms such as PageRank or Centrality, traversing paths, discovering connections and getting insights becomes possible.
Graphs and graph databases are the fastest-growing area of data management technology for a number of reasons. One of the reasons is because they are a perfect match for use cases involving interconnected data.
Queries that would be very complicated to express and very slow to execute using relational databases or other NoSQL database technology, are feasible using graph databases. With the rise in complexity of modern financial markets, financial crimes require going 4 to 11 levels deep into the account – payment graph: this requires a different solution than either relational or NoSQL databases.
How are organizations such as Alibaba, OpenCorporates, and Visa using graph database technology to not just stay on top of regulation, but be one step ahead in the race against financial crime?
Is it possible to do this in real time?
What do graph query languages have to do with this?Graph intelligence: the future of data-driven investigations
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
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Graph in Apache Cassandra. The World’s Most Scalable Graph Database
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Semantic similarity for faster Knowledge Graph delivery at scale
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Dow Jones: Reimagining the News as a Knowledge Graph
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Bob Boule
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https://alandix.com/academic/papers/synergy2024-epistemic/
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Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
GraphRAG is All You need? LLM & Knowledge Graph
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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