Connected data meetup group - introduction &amp; scope

The trajectory schema.org has taken, starting with a history that is less a retrospective than a narrative. I'll follow this narrative to the fortunately-timed emergence of JSON-LD, providing as it does a flexible, standards-based serialization of the vocabulary. This, I'll explain, helped fuel the popularity of schema.org, which in turn has caused a demand for more schemas, growing the vocabulary and its capabilities. I'll make the case that schema.org has started to resemble exactly what everyone involved in the initiative declared it shouldn't be: an ontology of everything. Whether or not that be the case, I'll say, the utility of having a relatively simple, well thought-out, well-understood and very broad vocabulary available has made schema.org (along with JSON-LD) a go-to tool for linked data modelers. Finally, and with a look at the many ways Google, in particular, has made use of schema.org, I'll explore to what extent its utility extends past being a convenient starting for point for back-of-the napkin knowledge graph development, or whether it's making a significant contribution to realizing the promise of a web of data.

RDF Data Quality Assessment - connecting the pieces

RDF and graph databases are steadily increasing their adoption and are no longer choices of niche-only communities. For almost 20 years, a constraint language for RDF was a big missing piece in the technology stack and a prohibiting factor for further adoption. Even though most RDF-based systems were performing data validation and quality assessment, there was no standardized way to define constraints. People were using ad-hoc solutions or schemas and languages that were not meant for validation. Thankfully, since 2017 there are 2 additions to the RDF technology stack: SHACL & ShEx. Both provide a high level RDF constraint language that people can use to define data constraints (a.k.a. Shapes), each with different strengths. This talk provides an outline of different types of RDF data quality issues and existing approaches to quality assessment. The goal is to give an overview of the existing RDF validation landscape and hopefully, inspire people on how to improve their RDF publishing workflows.

The Business Case for Semantic Web Ontology & Knowledge Graph

Personalized content recommendation systems enable users to overcome the information overload associated with rapidly changing deep and wide content streams such as news. This webinar discusses Ontotext’s latest improvements to its Dynamic Semantic Publishing (DSP) platform NOW (News on the Web). The Platform includes social data mining, web usage mining, behavioral and contextual semantic fingerprinting, content typing and rich relationship search.

Ephedra: efficiently combining RDF data and services using SPARQL federation

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Atanas Kiryakov's, Ontotext’s CEO, presentation at the first edition of Graphorum (http://graphorum2017.dataversity.net/) – a new forum that taps into the growing interest in Graph Databases and Technologies. Graphorum is co-located with the Smart Data Conference, organized by the digital publishing platform Dataversity. The presentation demonstrates the capabilities of Ontotext’s own approach to contributing to the discipline of more intelligent information gathering and analysis by: - graphically explorinh the connectivity patterns in big datasets; - building new links between identical entities residing in different data silos; - getting insights of what type of queries can be run against various linked data sets; - reliably filtering information based on relationships, e.g., between people and organizations, in the news; - demonstrating the conversion of tabular data into RDF. Learn more at http://ontotext.com/.

GraphQL and its schema as a universal layer for database access

GraphQL is a query language mostly used to streamline access to REST APIs. It is seeing tremendous growth and adoption, in organizations like Airbnb, Coursera, Docker, GitHub, Twitter, Uber, and Facebook, where it was invented. As REST APIs are proliferating, the promise of accessing them all through a single query language and hub, which is what GraphQL and GraphQL server implementations bring, is alluring. A significant recent addition to GraphQL was SDL, its schema definition language. SDL enables developers to define a schema governing interaction with the back-end that GraphQL servers can then implement and enforce. Prisma is a productized version of the data layer leveraging GraphQL to access any database. Prisma works with MySQL, Postgres, and MongoDB, and is adding to this list. Prisma sees the GraphQL community really coming together around the idea of schema-first development, and wants to use GraphQL SDL as the foundation for all interfaces between systems.

Enabling Low-cost Open Data Publishing and Reuse

In the space of just a few years we’ve seen the transformational power of open data; both for transparency and accountability in public data, and efficiency and innovation with businesses in private data. In its first year, institutions and individuals throughout Europe have supported public sector bodies in releasing data and numerous start-ups, developers and SMEs in reusing this data for economic benefit. However, we are still at the beginning of the open data movement, and there is still more that can be done to make open data simpler to use and to make it available to a wider audience. The core goal of the DaPaaS project is to provide a Data- and Platform-as-a-Service environment, where 3rd parties (such as governmental organisations, SMEs, developers and larger companies) can publish and host both data sets and data-intensive applications, which can then be accessed by end-user applications in a cross-platform manner. You can find out more about DaPaaS on the detailed about page. Essentially, DaPaaS aims to make publishing, consumption, and reuse of open data, as well as deploying open data applications, easier and cheaper for SMEs and small public bodies which otherwise may not have sufficient technical expertise, infrastructure and resources required to do so. see also http://www.slideshare.net/eswcsummerschool/wed-roman-tutopendatapub-38742186

Linked Data Experiences at Springer Nature

Michele Pasin

ESWC 2017 Tutorial Knowledge Graphs

Robert Isele | eccenca CorporateMemory - Semantically integrated Enterprise D...

How LinkedIn Democratizes Big Data Visualization

Chi-Yi Kuan

Speakers: Jonathan Wu (LinkedIn), Praveen Neppalli Naga (LinkedIn), Chi-Yi Kuan (LinkedIn) Category: Hadoop in Action LinkedIn processes enormous amounts of events each day. This data is of critical importance for data analysts, engineers, business experts, and data scientists that seek deep understanding of the interactions within LinkedIn’s professional social graph. They use this data to derive insights and performance metrics, which lead to better business decisions on products, marketing, sales, and other functional areas. Areas of interest include Email, Growth, Engagement, and Trending metrics. Development of internal tools has traditionally been based on specific need, optimized for the business use case, and non-interoperable. The engineering challenge is to allow business users to easily access and organize huge amounts of data in a comprehensive way and to be able to flexible and quickly get to the insights through graphs and charts that they need. The data needs to be sufficiently granular to work for different needs, the interface needs to be intuitive and simple, and the infrastructure needs to be high performance allowing users to manipulate large amounts of data quickly. The solution to this challenge was realized by the LinkedIn Business Analytics and Data Analytics Infrastructure teams utilizing an integrated stack that includes an interactive analytics infrastructure and a self-serve data visualization front-end solution. The user interface provides a customizable ability to build charts, tables, and queries to suit highly customized reporting needs on any devices. The back-end infrastructure is based on Hadoop; which leverages LinkedIn’s investment in high scalable, data rich systems. The combined solution brings the ability to visualize, slice, dice, and drill through billions of records and hundreds of dimensions at fast scale. In this talk, you will learn the background of the data challenges that LinkedIn faced, how the teams came together to construct the solution, and the underlying stack structure powering this solution.

Ontos NLP Stack, Sep. 2016

Martin Voigt

Sören Auer | Enterprise Knowledge Graphs

Getting Started with Knowledge Graphs

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The Evolution of Search and Big Data

Search Technologies

Fireside Chat with Bloor Research: State of the Graph Database Market 2020

Mastering Customer Data on Apache Spark

During this Big Data Warehousing Meetup, Caserta Concepts and Databricks addressed the number one operational and analytic goal of nearly every organization today – to have complete view of every customer. Customer Data Integration (CDI) must be implemented to cleanse and match customer identities within and across various data systems. CDI has been a long-standing data engineering challenge, not just one of logic and complexity but also of performance and scalability. The speakers brought together best practice techniques with Apache Spark to achieve complete CDI. Speakers: Joe Caserta, President, Caserta Concepts Kevin Rasmussen, Big Data Engineer, Caserta Concepts Vida Ha, Lead Solutions Engineer, Databricks The sessions covered a series of problems that are adequately solved with Apache Spark, as well as those that are require additional technologies to implement correctly. Topics included: · Building an end-to-end CDI pipeline in Apache Spark · What works, what doesn’t, and how do we use Spark we evolve · Innovation with Spark including methods for customer matching from statistical patterns, geolocation, and behavior · Using Pyspark and Python’s rich module ecosystem for data cleansing and standardization matching · Using GraphX for matching and scalable clustering · Analyzing large data files with Spark · Using Spark for ETL on large datasets · Applying Machine Learning & Data Science to large datasets · Connecting BI/Visualization tools to Apache Spark to analyze large datasets internally The speakers also touched on data governance, on-boarding new data rapidly, how to balance rapid agility and time to market with critical decision support and customer interaction. They also shared examples of problems that Apache Spark is not optimized for. For more information on the services offered by Caserta Concepts, visit our website: http://casertaconcepts.com/

Introducing Neo4j

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Semantics and Machine Learning

"Semantic Integration Is What You Do Before The Deep Learning". dev.bg Machine Learning seminar, 13 May 2019. It's well known that 80\% of the effort of a data scientist is spent on data preparation. Semantic integration is arguably the best way to spend this effort more efficiently and to reuse it between tasks, projects and organizations. Knowledge Graphs (KG) and Linked Open Data (LOD) have become very popular recently. They are used by Google, Amazon, Bing, Samsung, Springer Nature, Microsoft Academic, AirBnb… and any large enterprise that would like to have a holistic (360 degree) view of its business. The Semantic Web (web 3.0) is a way to build a Giant Global Graph, just like the normal web is a Global Web of Documents. IEEE already talks about Big Data Semantics. We review the topic of KGs and their applicability to Machine Learning.

Graph all the things - PRathle

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Architecting Data For The Modern Enterprise - Data Summit 2017, Closing Keynote

The “Big Data era” has ushered in an avalanche of new technologies and approaches for delivering information and insights to business users. What is the role of the cloud in your analytical environment? How can you make your migration as seamless as possible? This closing keynote, delivered by Joe Caserta, a prominent consultant who has helped many global enterprises adopt Big Data, provided the audience with the inside scoop needed to supplement data warehousing environments with data intelligence—the amalgamation of Big Data and business intelligence. This presentation was given as the closing keynote at DBTA's annual Data Summit in NYC.

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Delivering a Linked Data warehouse and realising the power of graphs

Ben Gardner

Linklaters is one of the world’s leading global law firms. The firm has a wealth of high value information held within our systems however due to the nature of these systems it is not always easy to leverage this value. Our goal was to improve decision making across the firm by transforming access to and ability to query data. To do this we wanted a solution that would combine our information, was easy to extend in an iterative fashion and would leverage our existing investment in business intelligence. To achieve this we chose to create a graph based warehouse using Linked Data. Data from our SAP Business Warehouse was combined with flat file and XML feeds from our systems of record and transformed into RDF via ETL services that loaded it into a triple store. To provide simple integration with our existing environment a SPARQL to OData service was deployed creating an OData compliant endpoint. Finally a model driven, mobile friendly, user interface was created allowing users to query, review results and explore the underlying graph. This talk will describe the approach we took and the lessons learnt.

Accelerate Self-Service Analytics with Data Virtualization and Visualization

Watch full webinar here: https://bit.ly/39AhUB7 Enterprise organizations are shifting to self-service analytics as business users need real-time access to holistic and consistent views of data regardless of its location, source or type for arriving at critical decisions. Data Virtualization and Data Visualization work together through a universal semantic layer. Learn how they enable self-service data discovery and improve performance of your reports and dashboards. In this session, you will learn: - Challenges faced by business users - How data virtualization enables self-service analytics - Use case and lessons from customer success - Overview of the highlight features in Tableau

Crawlable Spatial Data - #Geo4Web research topic #3

Dimitri van Hees

APIs and Linked Data: A match made in Heaven

Michael Petychakis

Introduction: Relational to Graphs

Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market. A graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL. Join this webinar to learn why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships. Ryan Boyd, Developer Relations at Neo4j Ryan is a SF-based software engineer focused on helping developers understand the power of graph databases. Previously he was a product manager for architectural software, built applications and web hosting environments for higher education, and worked in developer relations for twenty products during his 8 years at Google. He enjoys cycling, sailing, skydiving, and many other adventures when not in front of his computer.

Knowledge Graph for Machine Learning and Data Science

Big Data Analytics with Microsoft

Slides from a recent Big Data Warehousing Meetup titled, Big Data Analytics with Microsoft. See Power Pivot/ Power Query/ Power View/ Power Maps and Azure Machine Learning be used to analyze Big Data. One challenge of dealing with Big Data project is to acquire both structured and instructed information in order to find the right correlation. During the event, we explained all the steps to build your model and enhance your existing data through Microsoft's Power BI. We had an in-depth discussion about the innovations built into the latest stack of Microsoft Business Intelligence, and practical tips from Technology Specialist’s from Microsoft. The session also featured demos to help you see the technology as an end-to-end solution. For more information, visit www.casertaconcepts.com

Powering Real-Time Analytics with Data Virtualization on AWS (ASEAN & ANZ)

Watch full webinar here: https://bit.ly/3h2yLnb Presented at AWS Summit Online 2021 (ASEAN & ANZ) Is your organization challenged with modernizing analytics in the cloud, while driving smarter data integration capabilities? With a logical data warehouse powered by data virtualization, you can combine all of the data across the enterprise and make it available to analytical and visualization tools that facilitate timely, insightful, and impactful decisions. In this session, you will learn how data virtualization helps enterprises gain a unified view of the data across AWS, multi-cloud and hybrid cloud easily with a virtual data layer that abstracts business users from the technical details of where data resides.

How to Empower Your Business Users with Oracle Data Visualization

Perficient, Inc.

With Oracle Data Visualization Cloud Service, your business users can perform self-service analytics, spot patterns, trends, correlations, and construct visual data stories for greater insight into how your product, service, or organization is performing. In this webinar, we demonstrated how easily users can explore their data in new and different ways through stunning visualizations automatically, promoting self-service discovery. Discussion included: -In-depth review of Oracle Data Visualization Cloud Service -Connecting different data sets like HCM, ERP, Sales Cloud and more -Mobile and security -Demo taking a real-world business use case from end to end

Accelerating Data-Driven Enterprise Transformation in Banking, Financial Serv...

Watch full webinar here: https://bit.ly/3c6v8K7 Banking, Financial Services and Insurance (BFSI) organizations are globally accelerating their digital journey, making rapid strides with their digitization efforts, and adding key capabilities to adapt and innovate in the new normal. Many companies find digital transformation challenging as they rely on established systems that are often not only poorly integrated but also highly resistant to modernization without downtime. Hear how the BFSI industry is leveraging data virtualization that facilitates digital transformation via a modern data integration/data delivery approach to gain greater agility, flexibility, and efficiency. In this session from Denodo, you will learn: - Industry key trends and challenges driving the digital transformation mandate and platform modernization initiatives - Key concepts of Data Virtualization, and how it can enable BFSI customers to develop critical capabilities for real-time / near real-time data integration - Success Stories on organizations who already use data virtualization to differentiate themselves from the competition.

Thwart Fraud Using Graph-Enhanced Machine Learning and AI

Fraudsters are becoming increasingly sophisticated, organized and adaptive; traditional, rule-based solutions are not broad or nimble enough to deal with this reality. This session will cover several demonstrations and real-world technical examples including preventing credit card fraud, identifying money laundering and reducing false positives. LEARN ABOUT: - Reference Architecture – See a framework for building intelligent applications that can sense and respond to increasingly complex fraud attempts. - Boosting machine learning – Find out how you can combine machine learning with graph technology to improve predictive lift - Graph algorithms – Hear an overview of algorithms to get started with and uses for fraud analysis

Denodo: Enabling a Data Mesh Architecture and Data Sharing Culture at Landsba...

Sylvain Dutilh, INFORMATION INTELLIGENCE SPECIALIST, Landsbankinn Traditional data processing leaves large pools of replicated and unsynchronized data sets behind. In an era when data grows exponentially and is disconnected and spread across silos, it has never been more unnecessary to replicate data. In this session, Sylvain from Landsbankinn will walk us through his organization's journey of implementing a Logical Data Warehouse and a data-sharing program by leveraging Data Virtualization capability that allowed it to build a central, secure business rules repository and an agile, modern data mesh architecture.

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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...

"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

From Taxonomies and Schemas to Knowledge Graphs: 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...

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...

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...

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.

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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.

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