Knowledge Graphs, Machine Learning and AI, Linked Data and Semantic Technology and Graph Databases are redefining how data works. Data is redefining how everything works. Connected Data London is the go-to event for the latest developments in these key technologies.
We curated our program on the intersection of these key technologies that are shaping the data-driven world of today and tomorrow. We published all the slides and videos from the presentations. We also shared a photo gallery to give you an idea of the atmosphere, the energy and the excitement of the day. Let us now revisit our key themes and see how Connected Data London 2018 helped evolve our perception.
For some, these technologies represent different camps. We beg to differ, as we feel there is a thread that connects them all. Data with meaning, and leveraging connections, can lead to a plethora of benefits.
The feedback we got from attendants highlights precisely what we wanted to achieve: a focused event, with the creme de la creme in our domain. A setting that facilitates knowledge transfer and networking, and shapes the future of data with the leaders and innovators of today and tomorrow.
The Future of Data is Connected and Open Minded.
Knowledge Graphs are more than hype. Since Gartner included Knowledge Graphs in its Hype Cycle in August 2018, interest in them has peaked. Unlike other hyped technologies however, Knowledge Graphs have been around for about 20 years. They are being used having an impact in top organizations today. Information covered by Knowledge Graphs has grown exponentially, and now we have Knowledge Graphs containing trillions of facts.
Google introduced the term Knowledge Graph to the world. The scale at which Google operates dictates a pragmatic approach towards Semantics, powering many innovative applications behind the scenes. Markus Lanthaler, Software Engineer at Google, shared how initiatives such as Schema.org allow search engines to extract, understand and integrate data, and create knowledge graphs to improve their services.
In BBC, one of the most prominent media organizations in the world, the content management system (CMS) has been powered by Knowledge Graphs since 2010. The CMS stores attributes common across all types of content, enabling producers to focus on the stories they’re trying to tell using the most appropriate content. Augustine Kwanashie, Software Engineer at BBC, shared some of the engineering behind this.
In Zalando, one of the top retailers in Europe, building and communicating a Knowledge Graph has been a rewarding journey. The Knowledge Graph has had an impact on Zalando’s search – the lifeblood of online retail. Search has improved in terms of precision and recall, resulting in an improved bottomline. Katariina Kari, Research Engineer at Zalando Tech-Hub, shared how they did it. Machine learning included!
AI and Machine Learning can work with, and for, graphs. One of the key challenges for Knowledge Graphs is doing this at scale: building, populating and managing big Knowledge Graphs is hard to do manually, and Machine Learning can help with this. One of the key challenges for Machine Learning and AI on the other hand is adding context and structure, and graphs can help with this.
Facebook has one of the biggest graphs in the world, and is also one of the leaders in Machine Learning and AI. Sebastian Riedel, Researcher at Facebook AI Research, helped explore how Knowledge-based approaches can connect with, and contribute to, Machine Learning for better AI. Riedel participated in an expert panel exploring AI tribes and interconnections, and the role of graphs.
Octavian is one of the pioneers in new approaches to Machine Reasoning and Graph-based Learning. They are working to build machines that can answer useful questions, using neural reasoning and Knowledge Graphs. Andy Jefferson, Neural Networks and Graph AI Researcher at Octavian, shared the state of the art in building AI with Graphs.
The Open Data Institute is one of the leaders in using data in the public sector. Sir Nigel Shadbolt from the ODI has been working on AI for many decades. Now that AI is in the limelight, let’s not forget all the long years of foundational research and the effort that went into accumulating data that facilitates AI. In his keynote, Sir Nigel Shadbolt emphasized the importance of Linked Data as critical infrastructure.
Linked Data and Semantics are key for data integration and governance. This can unlock anything from regulatory compliance to new communication channels. A little semantics may go a long way, and much of the progress and impact of Knowledge Graphs is based on this. Metadata and Data Governance can directly benefit, which is very important for regulatory compliance and beyond.
Electronic Arts, one of the leaders in the gaming industry, is using Linked Data to transform how the company operates. Getting data governance right has boosted EA’s content marketing reach, by enabling it to reach more channels and be more discoverable, translating to more sales. Aaron Bradley, Knowledge Graph Strategist at EA, shared how schema.org has been a foundation for this outcome.
Elsevier, one of the world’s major providers of scientific, technical, and medical information, uses data lineage to enable understanding of data flow activities and to identify and document the legal justification for each type of activity. This has enabled it to define policies for GDPR compliance, and Paraskevi Zerva, Cognition & Knowledge Representation Lead at Elsevier, shared how Linked Data powers this approach.
IBM, one of the leading technology companies in the world, is investing in metadata that can flow with the data in a form that is accessible to tools from many vendors. This open metadata management is coupled with open governance APIs to enable business owners to set policies. Mandy Chessell, Distinguished Engineer at IBM, explained how this could revolutionise the data industry.
Graph Databases are the fastest growing database category for a reason. We called this the Year of the Graph long before the Gartners of the world, and the market seems to stand behind this. Graph databases can work where other databases can’t, especially in scenarios that involve leveraging connections and powering semantics.
A generation of fast, scalable graph databases, is opening up a world of business insight and performance. Some of the use cases Victor Lee, Director of Product Management at TigerGraph, explored were IceKredit, an innovative FinTech transforming the near-prime and sub-prime credit market in United States, and wish.com, delivering real-time personalized recommendations to increase eCommerce revenue.
To make decisions, businesses have to combine their databases with non-proprietary data. But combining diverse data from multiple sources is a complex task. Technology developed by Ontotext, and presented by its CEO Atanas Kiryakov, can help build Big Knowledge Graphs and apply cognitive analytics to them to provide entity awareness. Clients include names such as Astra Zeneca, IET and Raytheon.
Celum is a leading cloud software manufacturer. Their Content & Collaboration Cloud optimizes the complete life cycle of digital content and the interaction of people in teams. With over 800 customers in 35 countries, Celum set out to develop its own architecture to provide the best user experience. Rainer Pichler, Software Architect at Celum, shared how they did it using Graph Databases.
Stay tuned as we explore, and build, the Connections in the years to come.