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A knowledge graph connects all data without moving or copying it. A knowledge graph seamlessly layers atop the existing data infrastructure, and reveals the relationships within the data, regardless of its source or format. These graphs are highly scalable, and retain each analysis, which acts as a reusable asset.

This paper attempts to compare and combine different approaches for

Alethio brings you more than 18,000,000,000 facts about Ethereum

What is more important than data consolidation? Making sense of that data and communicating it to others. With data volumes growing and time for analysis decreasing, this doesn't seem like an easy task. Data visualisation, however, may be the most reliable way to get on with this daunting process and make it seamless. Data visualisation and more specifically graph visualisation helps you organize all your data and make it readable and insightful.

With the constant growth of Internet of Things (IoT) ecosystems, allowing them to interact transparently has become a major issue for both the research and the software development communities. In this paper we propose a novel approach that builds semantically interoperable ecosystems of IoT devices. The approach provides a SPARQL query-based mechanism to transparently discover and access IoT devices that publish heterogeneous data. The approach was evaluated in order to prove that it provides complete and correct answers without affecting the response time and that it scales linearly in la...

Graph machine learning (graphML) is a subset of deep learning with much higher accuracy because big data records are linked together by their relationships.

Artificial Intelligence company Expert System today announced the availability in its Cogito® AI platform of the Creative Works Ontology developed by MovieLabs, the non-profit R&D joint venture started by the major Hollywood studios. MovieLabs’s Creative Works Ontology, published late last year helps fill the need of media companies to adapt their infrastructures to an increasingly … Continue reading "Expert System supports media organizations with the MovieLabs Creative Works Ontology"

Recent #research proved #deeplearning can be used to learn RDFS inference rules. A deficiency: lack of derivations for inferred triples, or explainability in #AI terms. @IBMResearch @jahendler provide inferred graph, explain how triples were inferred

Incorporating knowledge graphs into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich and complementary information to user-item interactions. Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user’s interest. However, existing efforts have not fully explored this connectivity to infer user preferences, especially in terms of modeling the sequential dependencies within and ...

And of course, querying it with SPARQL.

SciBite CTO James Malone gives his views on the most useful ways to visualise an ontology.

A series on how the decentralised XRP ledger can be used to explore relationships between wallets using a graph database. This first part focuses on graph databases versus “normal database” and my process of establishing the database.

[Submitted on 17 Apr 2020] Download PDF Abstract: ``Classical'' word embeddings, such as Word2Vec, have been shown to capture

distanceFeature engineering

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.

Facebook appears to be testing the addition of Wikipedia knowledge panels in search results, according to reports from multiple users.Based on the screenshots shared on Twitter, this feature is reminiscent of Google’s integration with Wikipedia.Here’s an example that was spotted a few days ago:New? Facebook shows Wikipedia snippits in search resultsh/t @jc_zijl pic.twitter.com/zcbQJmauhE— Matt Navarra | 🚨 #StayAtHome (@MattNavarra) June 9, 2020Just like in Google’s search results, the Wikipedia knowledge box in Facebook search shows key details about the entity being searched for.You’ll also notice there’s a lone Instagram link, which is a stark contrast to Google’s search results containing links to all popular social media profiles.Unlike Google’s knowledge panels, which link to a number of domains where people can learn more about a entity, Facebook is trying to keep people within the Facebook ecosystem as much as possible.Here’s another example that looks

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Facebook’s recent $5.7 billion investment in Indian telecommunications behemoth Jio Platforms goes beyond the typical tech deal and will help the American social media titan finally unlock the world's biggest democracy.

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

Stardog 6.2 just shipped with scalable virtual graph caching, better Kubernetes integration, support for Amazon Redshift, and many new optimizations . Read on for the details.

user experience was also far from great and administrative features like access control were totally missing. At the same time, the mainstream web application architecture was relying on techn

Dan Barber, the James Beard award-winning chef who pioneered the farm-to-table movement, has a single, elegant term for the complexity of molecules, processes, and sequences that underpins great food: flavor. Like great chefs, computer scientists grapple with complexity, seeking elegant ways to model and analyze increasingly complex–or perhaps, flavorful–phenomena. Across many business verticals, we are […]

László Barabási never bothered to learn English. “My worst grades were always in English because I thought, Why study it? You can never leave this country,” explains Barabási, director of the Center for Complex Network Research (CCNR) at Northeastern University in Boston. “It wasn’t until I got to the University of Bucharest and became interested in research that I understood the importance of being able to read academic papers in English.” Barabási emigrated from Romania to Budapest with his father in the summer of 1989, a few months before Ceaușescu was overthrown, and completed a master’s degree in physics at Eötvös Loránd University two years later. But it wasn’t until after he’d earned a Ph.D. in physics at Boston University in 1994, while working as a postdoc at IBM’s legendary Thomas J. Watson Research Center, that Barabási became inter

Solutions Review highlights the most common graph database use cases you need to know about so you can select the best software.

A big new #LOAD resource: https://t.co/J44j8EKsGo All 500M+ owl:sameAs statements in the #LOD cloud, their closure into 50M equivalence classes, and an estimated error degree for all of these. All for download at https://t.co/sKeetxXzgY— Frank van Harmelen (@FrankVanHarmele) April 28, 2019

Combines graph and document technologies, ontology design, and AI data science expertise

Grab your free copy of the brand new O'Reilly book, "Graph Algorithms: Practical Examples in Apache Spark & Neo4j" – a practical guide to graph analytics.