GraphNews

Collaborative #knowledgegraphs such as @Wikidata rely on the crowd. Graph populated by non-standard, noisy, long or awkward titles, which is a challenge. #research: context-aware attentive #neuralnetwork approach for entity linking h/t @aaranged #AI #data

All knowledge panels may be contextual knowledge panels based upon the words in a query that triggers those panels to appear in search results

Introducing how we built the Knowledge Graph at Airbnb, which helped us categorize our inventories and deliver contexts to people.

#Semantic interoperability is the key enabler for #digitaltransformation. But how do we achieve it? By having content understandable & available in a machine processable form. #Ontologies will play a key role in providing infrastructure to support this

I have seen several tools for converting spreadsheets to RDF over the years. They typically try to cover so many different cases that learning how to use them has taken more effort than just writing a short perl script that uses the split() command, so that’s what I usually ended up doing. (Several years ago I did come up with another way that was more of a cute trick with Turtle syntax.)

Any JSON at all.

So that we can use tools designed around those vocabularies.

In this work, we present a new dataset for conversational recommendation over

The index to the multiple installments in the 'Cooking with Python and KBpedia' (CWPK) series.

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19 tracking project data available to the public.  This data is paired with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. This dataset is updated hourly at 45 minutes past the hour.

We’re proud to announce that we have now released the first public release of OpenCorporates’ new corporate events system. This has been a really substantial project that’s been more complex and more difficult than we expected, and threw up many issues and questions along the way. But what we have now is a big step forward for OpenCorporates, and, we believe, ultimately the world’s understanding of companies. First, the headlines: We’ve generated over 420 million corporate events for 170m companies. Around 50 thousand new events added each day on average. Currently features key lifecycle ev...

This post will showcase the use of cosine similarity algorithm in Neo4J and also provide examples in addition to the available…

Luis Bosquez, Program Manager for Azure Cosmos DB shares an overview of the Graph/Gremlin API, and best practices app developers can use when building apps using the graph data model and Apache Tinkerpop Gremlin language. http://www.azurecosmosdb.com Graph/Gremlin API - Documentation: https://docs.microsoft.com/en-us/azure/cosmos-db/graph-introduction About Azure Cosmos DB: Azure Cosmos DB is a fully-managed NoSQL database service offering unlimited and elastic scalability of throughput and storage, and guaranteed speed and performance anywhere in the world.

We're currently building a Data Governance Platform product that enables UK Financial Services organisations to discover and manage the life-cycle, usage, risk and compliance requirements of data asse

The unprecedented in-depth analysis enabled by graph analytics can produce risk warnings faster and better than was possible ten years ago.

Most #machinelearning #algorithms work well w/ either text or structured #data. #Semantic data models can close the gap. Relationships between business/data objects can be made available for further analysis #knowledgegraph #AI #datascience #EmergingTech

life connections will make the technology particularly useful for investment management in 2020 and the years to com

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

To find the right nail for your AI or blockchain hammer, think distributed.

Network Medicine offers a series of powerful tools to identify new drugs and diagnostics. In this exceptional moment of need, we decided...

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19. This editor can help you provide them with that information. Note that most cases for using this markup is restricted to official government sites and there are strict guidelines for the accuracy of the information. Please read this Bing Post with details on what you are allowed to do, and need to do. Bing may punish sites that break the guidelines. If you do successfully provide information to Bing or Google, I'd love to hear about it. Email me or start a conversation on Twitter or the Facebook group (see to left). Same goes for any issues you have. Let's make sure this works. Also contact me if you have issues understanding the specifications and documentation. Bing, Google and schema.org are also very keen to get this working. To get started, use the following editor to enter your Special Announcem

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We are a diverse team of scientists, developers and data people from academia and industry.

One of the "defects" of RDF for data management is that it does not support business rules. That's a generality, so let me explain a bit. ...

Building a knowledge graph the semantic data modeling way in 10 steps with the help of Ontotext's knowledge graph technology experts.

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Read this blog to learn more about the benefits of using a native graph database to build a real-time recommendation engine with personalization.