4 episodes

Welcome to the Connected Data Podcast, powered by Connected Data London, the leading conference for those who use the relationships, meaning and context in Data to achieve great things






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The CDL Team

The Connected Data Podcast | CDL CDL Team

    • Technology

Welcome to the Connected Data Podcast, powered by Connected Data London, the leading conference for those who use the relationships, meaning and context in Data to achieve great things






Stay tuned and dive into our diverse content. Learn & share!






The CDL Team

    Connecting data, decentralizing the web, making it sustainable: can the semantic web do this? | Panel Discussion - Connected Data London 2019

    Connecting data, decentralizing the web, making it sustainable: can the semantic web do this? | Panel Discussion - Connected Data London 2019

    Whether we call it Semantic Web or Linked Data, Tim Berner Lee’s vision never really caught on among users and developers. Although part of this vision is about decentralization, and this is something a few people are working on, Semantic Web technology remains largely underutilized by them. In this panel, we will explore how the Semantic Web and decentralization can benefit each other.






    Getting together people from both communities, and exploring questions such as:






    Is the Semantic Web technological stack really as complex as it is perceived to be? How can it be made more accessible, and align better with today’s realities in software development?






    What are the issues facing people working in decentralization, and how could Semantic Web technology provide solutions?






    What about sustainability? How can efforts aiming to provide services to the public at large find a way to sustain themselves, navigating a challenging business landscape?






    Andre Garzia from Mozilla, Sebastian Hellman from DBpedia, Ruben Verborgh from Ghent University, moderated by Jonathan Holtby from Hub of All Things

    • 41 min
    Facilitating COVID-19 research with Graph Analytics and Knowledge Graphs

    Facilitating COVID-19 research with Graph Analytics and Knowledge Graphs

    Scientists, health researchers and policymakers are using all the tools they can get their hands on to try and beat the current global pandemic.


    Germany’s National Centre for Diabetes Research (DZD) is one of the organizations turning to Artificial Intelligence, advanced visualisation techniques and other tools to aid the search for a vaccine and effective treatments. 


    Graph technology is key in this effort. DZD is integrating data from various sources and linking them in a dedicated COVID-19 Knowledge Graph to help researchers and scientists quickly and efficiently find their way through the more than 40,000 publications out there on the problem.  


    DZD's Head of Data Management and Knowledge Management, Dr. Alexander Jarasch, notes that, “Graph enables a new dimension of data analysis by helping us to connect highly heterogeneous data from various disciplines.” 


    The COVID GRAPH project is a voluntary initiative of graph enthusiasts and companies with the goal to build a knowledge graph with relevant information about the COVID-19 virus. It's a knowledge graph on COVID-19 that integrates various public datasets. This includes relevant publications, case statistics, genes and functions, molecular data and much more. 


    Still, the global scientific knowledge base is little more than a collection of documents. It is written by humans for humans, and we have done so for a long time. This makes perfect sense, after all it is people that make up the audience, and researchers in particular. 


    Yet, with the monumental progress in information technologies over the more recent decades, one may wonder why it is that the scientific knowledge communicated in scholarly literature remains largely inaccessible to machines. Surely it would be useful if some of that knowledge is more available to automated processing. 


    The Open Research Knowledge Graph (ORKG) project is working on answers and solutions. The project, recently initiated, and coordinated by TIB (Leibniz Information Centre for Science and Technology and University Library) is open to the community. ORKG actively engages research infrastructures and research communities in the development of technologies and use cases for open graphs about research knowledge. 


    Dr. Sören Auer, TIB Director and ORKG Lead, states that "Knowledge Graphs..allow us to interlink, interconnect and integrate heterogeneous data from various sources in various formats, modalities, levels of structuredness, governance schemes etc. As a result the effort required for preparing and integrating data for answering specific research questions is dramatically reduced, and AI techniques can more directly applied". 


    Join us as George Anadiotis hosts Alexander Jarasch and Sören Auer in a discussion that will go over: 


    The chronic issues that plague scientific research, and how they apply to life sciences, and SARS-CoV-2 research in specific The way data, analytics, and AI can help deal with the issues and facilitate research The COVID GRAPH project. What is the goal? Who set it up? What does it include? Whom is it for? How does it work? Differences and similarities between property graphs and knowledge graphs, and how that applies in the COVID GRAPH project. Can ORKG and COVID GRAPH work together? What are next steps / outlook? How can people get involved? 

    • 52 min
    In Search of the Universal Data Model | Joshua Shinavier (Uber) | Connected Data London 2019

    In Search of the Universal Data Model | Joshua Shinavier (Uber) | Connected Data London 2019

    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.






    Keynote by Joshua Shinavier, Uber Research Scientist, Apache TinkerPop co-founder, at Connected Data London 2019

    • 31 min
    Graph Databases Will Rule the World in the 2020s. But Why, and How? | Panel Discussion - Connected Data London 2019

    Graph Databases Will Rule the World in the 2020s. But Why, and How? | Panel Discussion - Connected Data London 2019

    Some of us may have been saying that for years, but now the Gartners of the world are picking up on it too. So, the oracles have spoken:






    “The application of graph processing and graph DBMSs will grow at 100 percent annually through 2022 to continuously accelerate data preparation and enable more complex and adaptive data science”.






    That all sounds great, in theory. In practice, however, things are messy. If you’re out to shop for a graph database, you will soon realize that there are no universally supported standards, performance evaluation is a dark art, and the vendor space seems to be expanding by the minute.






    Recently, the W3C initiated an effort to brings the various strands of graph databases closer together, but it’s still a long way from fruition.






    So, what’s all the fuss about? What are some of the things graph databases are being used for, what are they good at, and what are they not so good at?






    Property graphs and RDF are the 2 prevalent ways to model the world in graph; What is each of these good at, specifically? What problems does each of these have, and how are they being addressed?






    RDF* is a proposal that could help bridge graph models across property graphs and RDF. What is it, how does it work, and when will it be available to use in production?






    What about query languages? In the RDF world, SPARQL rules, but what about property graphs? Can Gremlin be the one graph virtual machine to unite them all?






    What about the future of graph databases? Could graph turn out to be a way to model data universally?






    Moderated by George Anadiotis.






    Panelists:






    Geoffrey Horrell


    Director of Applied Innovation, London Lab at Refinitiv






    Steve Sarsfield


    VP of product, Cambridge Semantics






    Joshua Shinavier


    Research scientist, Uber

    • 25 min

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