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

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1. Knowledge Graph

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. 

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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

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

The quality assurance of the knowledge graph is a prerequisite for various

text generation take as input a few RDF triples or

This is a continuation of the discussion that originates on this StackExchange post, about recommender systems using knowledge graphs(KGs). For those who might not prefer reading the original post,...

based models have shown their unique advantages in knowledge graph (KG)

Why does software development velocity slow as a company scales? See how you can improve your company's knowledge graph with Kubernetes.

According to Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

Welcome to this week’s #GraphCast – our series featuring what you might have missed in Neo4j media from the past fortnight.

The coronavirus pandemic is disrupting universities and research institutes across the world. But the same institutions are also working very hard to find out how the disease can be stopped and its effects mitigated.

Intelligent systems designed using machine learning algorithms require a

PubMed is an essential resource for the medical domain, but useful concepts

This blog post by Steve Sarsfield provides an overview of the graph market and how to select the appropriate graph database for your particular use case.

We address the problem of learning a distributed representation of entities

As most of the global workforce goes remote in 2020, businesses and budgets are changing due to a decrease or increase in site traffic. Since this pandemic is new for marketers and organizations, there isn’t exactly a marketing blueprint but one channel is proven to outlast pandemics: Search Engine Optimization (SEO).

In this paper, an overview of the state of the art on knowledge graph