Using R to extract user data from the Yelp API and create network graphs

Learn how to visualize your intellectual property protection holistically to discover the gaps and evaluate their effects on dependent products and revenues.

The Semantic Web emerged as an extension to the traditional Web, adding meaning (semantics) to a distributed Web of structured and linked information. At its core, the concept of ontology provides the means to semantically describe and structure information, and expose it to software and human agents in a machine and human-readable form. For software agents to be realized, it is crucial to develop powerful artificial intelligence and machine-learning techniques, able to extract knowledge from information sources, and represent it in the underlying ontology. This survey aims to provide insight into key aspects of ontology-based knowledge extraction from various sources such as text, databases, and human expertise, realized in the realm of feature selection. First, common classification and feature selection algorithms are presented. Then, selected approaches, which utilize ontologies to represent features and perform feature selection and classification, are described. The selective and representative approaches span diverse application domains, such as document classification, opinion mining, manufacturing, recommendation systems, urban management, information security systems, and demonstrate the feasibility and applicability of such methods. This survey, in addition to the criteria-based presentation of related works, contributes a number of open issues and challenges related to this still active research topic.

Graph Neural Networks for Natural Language Processing: A Survey Paper https://lnkd.in/gefRmQi Deep learning has become the dominant approach in coping...

It's always a nice feeling when you speculate about something Google is doing (in this case, on-camera yesterday afternoon with Barry Schwartz at SMX -...

Over the past few years, growth in the uptake of RDF has picked up steadily. In some domains, such as asset management and systems engineering, this growth is quite significant and driven by national and European standards.

Knowledge graphs have been increasingly seen as a way to understand relationships among data. They are used for biological networks, drug information - and chemical reactions.

Our latest benchmark report, Trillion Edge Knowledge Graph, is the first demonstration of a massive knowledge graph that consists of materialized data and Virtual Graphs spanning hybrid multicloud data sources. We prove it is possible to have a 1 trillion-edge knowledge graph and deliver sub-second query times while achieving a 98% cost savings.

Google introduces the #OpenSource Insights Project Exploratory #visualization provides an interactive view of #OSS projects dependencies A full dependency graph is built/published, incorporating metadata, so you can see how it may affect your #software

In this contributed article, editorial consultant Jelani Harper discusses The how increasing reliance on knowledge graphs parallels that of Artificial [...]

A collection of important graph embedding, classification and representation learning papers with implementations. - benedekrozemberczki/awesome-graph-classification

[vc_row type=”in_container” full_screen_row_position=”middle” scene_position=”center” text_color=”dark” text_align=”left” overlay_strength=”0.3″ shape_divider_position=”bottom”][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” width=”1/1″ tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][vc_column_text]What Is Ethical AI? Ethical, or responsible, artificial intelligence (AI), “is the...

A community reveals the features and connections of its members that are different from those in other communities in a network. Detecting communities is of great significance in network analysis....

This blog discusses SHACL capabilities and advantages of modeling in SHACL. It also debunks some myths about SHACL's shortcomings.

In the world of asset management, data exchange is a big thing. Modelling the underlying information is a central tenet in Building Information Modelling (BIM).

In recent years, knowledge graphs have been widely applied to organize data in a uniform way and enhance many tasks that require knowledge, for example, online shopping which has greatly...

Last year, we announced the general availability of the Azure Digital Twins platform. The associated open modeling language, Digital Twins Definition Language (DTDL), is a blank canvas which can model any entity. It is therefore important to provide common domain-specific ontologies to bootstrap sol...

As part of work to convert plain JSON records to proper RDF in JSON-LD I often want to convert a string value to a URI that identifies a thing (real world concrete thing or a concept). Simple string to URI mapping Given a fragment of a schedule in JSON {"day": "Tuesday"} As well as converting … Continue reading Strings to things in context →

Play detective on Reddit: Discover political disinformation campaigns, secret influencers and morehttps://t.co/J6kYL6TM2w#api #social #data #socialMedia #database #reddit #politics #etl #analytics #neo4j #graph #analysis #socialNetwork #network #graphDatabase #etl #graphdb pic.twitter.com/KjXAQMJbHI— Luc Michalski (@lucmichalski) May 8, 2021

Taxonomies are crucial for businesses and institutions to handle bigger amounts of data. Manually organizing thousands of concepts into a knowledge tree has so far been the only way to do this. Aside

Posted by Siamak Shakeri, Staff Software Engineer and Oshin Agarwal, Research Intern, Google Research Large pre-trained natural language...

Causaly, the London-based company that allows researchers and specialists to intuitively map and navigate the intricate landscape of biomedical research, has raised $17 million from investors to grow its team and expand into new markets.

Graph Theory in Machine Learning, and How it’s Changed the Game

We present Arianna+, a framework to design networks of ontologies for representing knowledge enabling smart homes to perform human activity recognition online. In the network, nodes are ontologies...

This is what happens when you bulk add keywords without paying attention to the details. If you’re searching for a specific result in a database or on... 43 comments on LinkedIn

Graph clustering aiming to obtain a partition of data using the graph information, has received considerable attention in recent years. However, noisy edges and nodes in the graph may make the...

This is a guest post by Russell Waterson, Knowledge Graph Engineer at Data Lens Ltd. Customers use knowledge graphs to consolidate and integrate information assets and make them more readily available. Building knowledge graphs by getting data from disparate existing data sources can be expensive, time-consuming, and complex. Project planning, project management, engineering, maintenance and […]

A new benchmarking study released at this week’s The Knowledge Graph Conference illustrates how large corporates are starting to embrace…