How might we model graphs beyond the most common use cases, and take a more creative approach to graph modeling?

based Technology Enables Companies in the Financial Sector to Quickly and Efficiently Integrate and Analyze the Explosion of Disparate Data from RegulatorsOver the past decade, the advance of digital innovation combined with the growing number of regulatory guidelines in all jurisdictions has sped up the rise of Regulatory Technology, or RegTech for short. Originally, its main application was to enhance regulatory processes in the Financial sector, but nowadays it is expanding more and more in to any regulated business. A good sign that this technology is steadily going mainstream is also the fact that more and more regulatory bodies are supporting RegTech development and implementation.According to a September 2019 report from Juniper Research, the value of global spending on RegTech is expected to jump to more than US $127 billion by 2024, up from US$25 billion spent in 2019. Automation in Know Your Customer (KYC) processes and background checks are set to be so

Read about why and how knowledge graph technology helps build networks of interwoven digital objects in the world of cultural heritage.

time.Therefore, we have recognised that building a “Talent Knowledge Graph” will allow us to effectively operate in our domain as well as to fully unlock the predictive power of artificial intelligence and provide unique insights from our heterogeneous data sources.After Google’s reveal of its Knowledge Graph platform in 2012, the term has been rapidly growing in popularit

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BLINK is an Entity Linking python library that uses Wikipedia as the target knowledge base.

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AWS Data Migration Service (DMS) now supports migrating graph data from relational sources to Amazon Neptune. The AWS Database Migration Service (AWS DMS) enables you to migrate data from one data source to another. Using relational databases as source and Neptune as destination allows customers to copy their connected data into Neptune for graph queries.   Customers using both relational and graph databases today manually load their data into Neptune. DMS minimizes the manual effort to carry out the workflow. Using DMS, you can configure a destination endpoint to existing Neptune databases. DMS will carry out a full copy of data from relational databases to Neptune. The DMS workflow allows you to target either a RDF model or a property graph model by specifying the appropriate mapping file for each data model. DMS version 3.3.2 supports Amazon Neptune as the destination endpoint. You can configure DMS using the AWS Management Console, AWS SDK or CLI. You will be ch

1. Knowledge Graph

19 data lake. A large amount of data on coronavirus exists in research publications. One of the datasets in the data lake is a massive corpus of these publications, which the Allen Institute for AI aggregates and updates. The problem lies in how to find and extract the information you need. This post walks you through solving this problem using knowledge graphs. Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. T

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The Centre for Digital Built Britain’s National Digital Twin programme has launched an open consultation seeking feedback on the proposed approach to the development of an Information Management Framework for the built environment. 

Dr Nicolas FigayDigital Enterprises Organisation and Collaboration around Manufacturing and Product Data2w · EditedEmerging Landscape of #graphs related technologies: required move from da facto standards to #ISO open standard?

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Neo4j’s Alicia Frame explains how life science researchers can exploit graph databases to get truly granular insight into big data to make major leaps forward in medical research.Complex data sets hold the key to advancing medical breakthroughs. These data sets tend to be voluminous and heterogeneous by nature, presenting an insurmountable challenge for traditional data analysis methods as they struggle to link patterns and outcomes. The unfortunate consequence is a slowdown in the progress of research.Anyone who works in life sciences is aware that they are working with highly connected information; the challenge is making sense of these connections. Unfortunately, many scientists are still using relational databases and spreadsheets which makes mapping important patterns and connections unintuitive and difficult, if not impossible.Graph technologyGraph technology is emerging as an enabler for researchers to trawl gargantuan amounts of unstructured data, turning it into valuab

tier_architectureHowever, knowledge graphs propose a paradigm shift to this design blurring the barrier between logic and data. By bringing some of the knowledge of the domain into a graph through rules a knowledge graph captures more than just the data in the system. As a result, rules can make the queries and requests much simpler to write and manage which in turns allows applications to be more flexible, less error prone and faster.This article will introduce a simple example to showcase the impact of rules on query design. The example will be ill

The team continue their blogs on the Stanford University course on Knowledge Graphs. This week “What is the role of knowledge graphs in machine learning?”

based search systems do not reflect the semantics of individual input words of search queries. For example, a query for the word “house” would not return records for the words “building” or “real estate”. How can such relationships be represented in a technical system? One approach is to include synonyms. Search engines like Elasticsearch provide methods to integrate synonym lists. However, a list of synonyms itself is required for configuration.

With the huge volume of research now available, and the ability to manage and analyze enormous amounts of data, you could say there has never been a better time to be a researcher. The opportunities to build on each other’s work are greater than ever.However, all that information brings challenges, with researchers indicating that just navigating all that information takes more time than ever.So how about a system of information that could give researchers precise answers to their questions immediately, rather than having to browse a list of webpages that might hold the answer deep in the text?Researchers at Elsevier's DiscoveryLab – part of the Innovation Center for Artificial Intelligence (ICAI) in Amsterdam – are building a system to deliver just that. Dr. Frank van Harmelen, Professor of Computer Science at the Vrije Universiteit (VU) Amsterdam and Academic Director of Elsevier's DiscoveryLab, explained:What we’re developing is an integrated knowledge graph for resea

For autonomous vehicles (AV), the ability to share information about their surroundings is crucial. With Level 4 and 5 autonomy in sight, solving the challenge of organization and efficient storing of data, coming from these connected platforms, becomes paramount. Research done up to now has been mostly focused on communication and network layers of V2X (Vehicle-to-Everything) data sharing. However, there is a gap when it comes to the data layer. Limited attention has been paid to the ontology development in the automotive domain. More specifically, the way to integrate sensor data and geos...

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To embrace the future of work, companies need technology that mimics how their organizations are structured and how their workers relate to one another. Today's work relationships, for example, are complex and fluid. Using highly structured relational databases, which organize data hierarchically in rows and columns to represent these relationships, no longer makes sense. Instead, we need adaptable technology that models data based on contextual relationships. Enter graph databases, a revolutionary technology that Gartner predicts will grow 100% per year over the next couple of years. Early Applications Of Graph Databases Graph databases may now be leveraged by giants like Google and Amazon, but they weren't always popular. In fact, their popularity grew with the rise of social media. Consider the complex relationships between people, places and things stored in social networks like Facebook or LinkedIn. Organizing these relationships based on hierarchies is problematic, as it

ExaMode is a EU-funded research project empowering medical professionals to provide more efficient patient care through data-driven diagnoses.

based relevance, without any contextual awareness. Identifying "golden” as a colour would, in fact, elevate the user experience as a whole.To understand our customer's intention, we needed to understand each que

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