Zara  Bryant

Zara Bryant


Building A Personal Assistant Bot with Microsoft Graph

Building A Personal Assistant Bot with Microsoft Graph


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Building A Personal Assistant Bot with Microsoft Graph

Top Microsoft big data solutions Companies | Best Microsoft big data Developers

An extensively researched list of top Microsoft big data analytics and solution with ratings & reviews to help find the best Microsoft big data solutions development companies around the world.
An exclusive list of Microsoft Big Data consulting and solution providers, after examining various factors of expert big data analytics firms and found the equivalent matches that boast the ace qualities with proven fineness in data analytics. For business growth and enterprise acceleration getting inputs from the whole data of the organization have become necessary, thus we bring to you the most trustworthy Microsoft Big Data consultants and solutions providers for your assistance.
Let’s take a look at the List of Best Microsoft big data solutions Companies.

#microsoft big data solutions development companies #microsoft big data analytics and solution #microsoft big data consultants #microsoft big data developers #microsoft big data #microsoft big data solution providers

Sival Alethea

Sival Alethea


Create A Twitter Bot With Python

Create a Twitter bot with Python that tweets images or status updates at a set interval. The Python script also scrapes the web for data.

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#python #a twitter bot #a twitter bot with python #bot #bot with python #create a twitter bot with python

Zara  Bryant

Zara Bryant


Build Great Discovery & Collaboration Apps for Microsoft 365 with New Microsoft Graph

Microsoft Graph is a powerful way to bring your solution’s data into Microsoft’s enterprise-scale apps and experiences. In this session we’ll show you how Microsoft Graph Connectors have evolved to provide even richer access for your data to enterprise search, eDiscovery and more. We’ll go a step farther and demonstrate how you can use that same connector-fed data to create powerful cross-application workflows using our latest version of Adaptive Cards.

Microsoft Graph Dev Center –
Microsoft 365 Dev Center –
Explore Deeper Content and Training –

Microsoft Build 2021

#microsoft #developer #graph

How to create and configure your bot to work in Microsoft Teams

This article is a step-by-step guide on how to create a Bot from scratch using Microsoft Bot Framework and how to configure it to work with Microsoft Teams.


  1. Office 365 Tenant
  2. Azure Subscription with Azure Bot Service, App Service
  3. Visual Studio

Prepare the Azure resources

Navigate and log in to Azure Portal. Create a new resource group then add a new Web App Bot (You can type “bot” in the search bar to filter your results).

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After you click on the Create button, you will be redirected to the configuration page of your resource.

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Since you added the resource directly from the resource group, some properties will be automatically set to the resource group values (like Resource group, Location, and Subscription).

Let’s fill up the remaining properties as follow:

  • Bot handle: Unique identifier for your bot.
  • Pricing tier: F0 (Fee up to 10k messages without premium channels) or S1 ($0.50 per 1,000 messages and the possibility to create premium channels)
  • App name: This will form the bot’s Endpoint Url.
  • Bot template: Currently it is possible to use the SDK for C## and Node.JS to implement two different templates: Echo Bots (a simple bot that echoes back the user’s message) and Basic Bot (bot template that contains Language Understanding and Bot Analytics services).

To complete the configuration and create the resource click Create and wait a few seconds to allow Azure to complete the task in the background. From the Channels tab under Bot Management, click on the Microsoft Teams icon to add the MS Teams channel to the bot.

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Create an empty bot

Open Visual Studio and create a new empty .NET Core web application project. It’s possible to start from a Skill Template but it comes with many pre-added features (like CosmosDb, Monitoring, Multilanguage, and many more) which might confuse you and for the purpose of this demo I prefer to start with a barebone bot.

Add dependencies

Open the Package Manager Console and execute the following instruction to add the required dependencies:

Install-Package Microsoft.AspNetCore.Mvc.NewtonsoftJson
Install-Package Microsoft.Bot.Builder
Install-Package Microsoft.Bot.Builder.Integration.AspNet.Core

Configure the application

Now it’s time to add services to our collection and make them available in our application through dependency injection and configure the middleware pipeline:

using Microsoft.AspNetCore.Builder;
using Microsoft.AspNetCore.Hosting;
using Microsoft.Bot.Builder;
using Microsoft.Bot.Builder.Integration.AspNet.Core;
using Microsoft.Extensions.Configuration;
using Microsoft.Extensions.DependencyInjection;
using Microsoft.Extensions.Hosting;
using Pegasus.Bots;
namespace Pegasus
    public class Startup
        public IConfiguration Configuration { get; }
        public Startup(IConfiguration configuration)
            Configuration = configuration;
        public void ConfigureServices(IServiceCollection services)
            services.AddSingleton<IBotFrameworkHttpAdapter, AdapterWithErrorHandler>();
            services.AddTransient<IBot, PegasusBot>();
        public void Configure(IApplicationBuilder app, IWebHostEnvironment env)
            if (env.IsDevelopment())
                .UseEndpoints(endpoints =>

#microsoft-teams #bots #microsoft #coding #tutorial

Luna  Mosciski

Luna Mosciski


Graph Therapy: The Year of the Graph Newsletter, June/May 2020

Parts of the world are still in lockdown, while others are returning to some semblance of normalcy. Either way, while the last few months have given some things pause, they have boosted others. It seems like developments in the world of Graphs are among those that have been boosted.

An abundance of educational material on all things graph has been prepared and delivered online, and is now freely accessible, with more on the way.

Graph databases have been making progress and announcements, repositioning themselves by a combination of releasing new features, securing additional funds, and entering strategic partnerships.

A key graph database technology, RDF*, which enables compatibility between RDF and property graph databases, is gaining momentum and tool support.

And more cutting edge research combining graph AI and knowledge graphs is seeing the light, too. Buckle up and enjoy some graph therapy.

Stanford’s series of online seminars featured some of the world’s leading experts on all things graph. If you missed it, or if you’d like to have an overview of what was said, you can find summaries for each lecture in this series of posts by Bob Kasenchak and Ahren Lehnert. Videos from the lectures are available here.

Stanford Knowledge Graph Course Not-Quite-Live-Blog

Stanford University’s computer science department is offering a free class on Knowledge Graphs available to the public. Stanford is also making recordings of the class available via the class website.

Another opportunity to get up to speed with educational material: The entire program of the course “Information Service Engineering” at KIT - Karlsruhe Institute of Technology, is delivered online and made freely available on YouTube. It includes topics such as ontology design, knowledge graph programming, basic graph theory, and more.

Information Service Engineering at KIT

Knowledge representation as a prerequisite for knowledge graphs. Learn about knowledge representation, ontologies, RDF(S), OWL, SPARQL, etc.

Ontology may sound like a formal term, while knowledge graph is a more approachable one. But the 2 are related, and so is ontology and AI. Without a consistent, thoughtful approach to developing, applying, evolving an ontology, AI systems lack underpinning that would allow them to be smart enough to make an impact.

The ontology is an investment that will continue to pay off, argue Seth Earley and Josh Bernoff in Harvard Business Review, making the case for how businesses may benefit from a knowldge-centric approach

Is Your Data Infrastructure Ready for AI?

Even after multiple generations of investments and billions of dollars of digital transformations, organizations struggle to use data to improve customer service, reduce costs, and speed the core processes that provide competitive advantage. AI was supposed to help with that.

Besides AI, knowledge graphs have a part to play in the Cloud, too. State is good, and lack of support for Stateful Cloud-native applications is a roadblock for many enterprise use-cases, writes Dave Duggal.

Graph knowledge bases are an old idea now being revisited to model complex, distributed domains. Combining high-level abstraction with Cloud-native design principles offers efficient “Context-as-a-Service” for hydrating stateless services. Graph knowledge-based systems can enable composition of Cloud-native services into event-driven dataflow processes.

Kubernetes also touches upon Organizational Knowledge, and that may be modeled as a Knowledge Graph.

Graph Knowledge Base for Stateful Cloud-Native Applications

Extending graph knowledge bases to model distributed systems creates a new kind of information system, one intentionally designed for today’s IT challenges.

The Enterprise Knowledge Graph Foundation was recently established to define best practices and mature the marketplace for EKG adoption, with a launch webinar on June the 23rd.

The Foundation defines its mission as including adopting semantic standards, developing best practices for accelerated EKG deployment, curating a repository of reusable models and resources, building a mechanism for engagement and shared knowledge, and advancing the business cases for EKG adoption.

Enterprise Knowledge Graph Maturity Model

The Enterprise Knowledge Graph Maturity Model (EKG/MM) is the industry-standard definition of the capabilities required for an enterprise knowledge graph. It establishes standard criteria for measuring progress and sets out the practical questions that all involved stakeholders ask to ensure trust, confidence and usage flexibility of data. Each capability area provides a business summary denoting its importance, a definition of the added value from semantic standards and scoring criteria based on five levels of defined maturity.

Enterprise Knowledge Graphs is what the Semantic Web Company (SWC) and Ontotext have been about for a long time, too. Two of the vendors in this space that have been around for the longer time just announced a strategic partnership: Ontotext, a graph database and platform provider, meets SWC, a management and added value layer that sits on top.

SWC and Ontotext CEOs emphasize how their portfolios are complementary, while the press release states that the companies have implemented a seamless integration of the PoolParty Semantic Suite™ v.8 with the GraphDB™ and Ontotext Platform, which offers benefits for many use cases.

#database #artificial intelligence #graph databases #rdf #graph analytics #knowledge graph #graph technology