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Building the world’s largest open source of company data

Chris_taggart

Chris Taggart will share his experience of building from scratch the world’s largest open database of companies data (134m companies as of Sep 2017). In his talk Chris will present how the technical engineering challenges and data quality issues faced and how OC worked to overcome these leveraging connected data standards, best practices and technologies […]

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Building the world’s largest open data of companies data

Chris Taggart will share his experience of building from scratch the world’s largest open database of companies data (134m companies as of Sep 2017). In his talk Chris will present how the technical engineering challenges and data quality issues faced and how OC worked to overcome these leveraging connected data standards, best practices and technologies […]

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Schema centric vs Schemaless approaches – when, where & how to use

Panel discussion at Connected Data London 2016

In this panel schemaless approaches such as Label Property Graphs and other ‘NoSQL’ technologies will be contrasted alongside schema centric approaches such as triplestores, quadstores and traditional relational databases. Is it possible to be truly schemaless? Are different technologies suited to different requirements? Do different technologies perhaps suit different levels of data maturity? Can semantics […]

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Getting Connected Data off the ground in your organisation

path_green

Starting to leverage Connected Data tools, techniques and capabilities in your organisation can seem daunting, yet every leader has had to start somewhere. In this panel discussion leaders and newcomers to the connected data space share some practical tips in getting initiatives off the ground and common pitfalls to avoid. Whether its a Enterprise Knowledge […]

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Tackling climate change through agricultural supply chain transparency

Javier_Godar

Trase.earth is a powerful new sustainability platform by the Stockholm Environment Institute (SEI) that enables governments, companies, investors and others to better understand and address the environmental and social impacts linked to their supply chains. Its pioneering approach draws on vast sets of production, trade and customs data, for the first time laying bare the […]

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Large Scale Graph visualisation techniques

Fabio_Sikansi

Fabio will give attendees a practical overview of the history and future developments in large scale graph visualisation techniques. Providing attendees with a whistlestop tour of key concepts such as edge-bundling Fabio will demonstrate how leaders are using these techniques to draw meaning from large scale connected datasets.     About the Speaker   Fabio […]

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Data Model vs Ontology Development – a FIBO perspective

Mike_Bennett

Mike Bennett will draw upon the experience of creating the Financial industry Business Ontology (FIBO), to set out how ontologies and data models compare, what it takes to create a business ontology as a common language, and how this is realized in practical applications that use inferencing capabilities.   About the speaker   Mike Bennett […]

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Implementing explicit Semantics in a schemaless Graph Database

Jesús_Barrasa

In this thought provoking and humorous lightening talk Jesus talks about his motivations and challenges in developing a semantic plugin for Neo4j. He also debunks certain myths perpetuated by vendors in this space and tackles common concerns around integration, data quality and implenting first order logic principles.   About the speaker   Jesus Barrasa P.h.D. […]

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Does Connected Data need AI or AI need Connected Data?

robot_orange

Connected Data encompasses data acquisition and data management requirements from a range of areas including the Semantic Web, Linked Data, Knowledge Management, Knowledge Representation and many others. Yet for the true value of many of these visions to be realised both within the public domain and within organisations requires the assembly of often huge datasets. […]

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Delivering Technology for People through Explainable AI (XAI)

Freddy_Lecue

Machine learning and its models have been largely studied to derive efficient solutions for problems ranging from regression, classification to clustering. However, explaining such models and their underlying predictions remains an open problem, mainly due to the model complexity and interpretability. This work presents our journey towards Explainable AI (i.e., how systematically decoding and enriching […]

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