8. Querying Structured Data in a Natural Way | Learning Microsoft Cognitive Services

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Learning Microsoft Cognitive Services

By : Leif Larsen Henning Larsen

Learning Microsoft Cognitive Services

By: Leif Larsen Henning Larsen

Overview of this book

Take your app development to the next level with Learning Microsoft Cognitive Services. Using Leif's knowledge of each of the powerful APIs, you'll learn how to create smarter apps with more human-like capabilities. ? Discover what each API has to offer and learn how to add it to your app ? Study each AI using theory and practical examples ? Learn current API best practices

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Getting Started with Microsoft Cognitive Services

1. Getting Started with Microsoft Cognitive Services

Cognitive Services in action for fun and life changing purposes

Setting up boilerplate code

Detecting faces with the Face API

Overview of what we are dealing with

Getting feedback on detected faces

Summary

2. Analyzing Images to Recognize a Face

2. Analyzing Images to Recognize a Face

Learning what an image is about using Computer Vision API

Diving deep into the Face API

Adding identification to our Smart-House application

Summary

3. Analyzing Videos

3. Analyzing Videos

Knowing your mood using the Emotion API

Diving into the Video API

Analyzing emotions in videos

Summary

4. Letting Applications Understand Commands

4. Letting Applications Understand Commands

Creating language-understanding models

Training a model

Executing operations based on commands

Summary

5. Speak with Your Application

5. Speak with Your Application

Converting text to audio and vice versa

Knowing who is speaking

Verifying a person through speech

Customizing speech recognition

Summary

6. Understanding Text

6. Understanding Text

Setting up a common core

Correcting spelling errors

Natural Language Processing using the Web Language Model

Extracting information through textual analysis

Exploring text using linguistic analysis

Summary

7. Extending Knowledge Based on Context

7. Extending Knowledge Based on Context

Linking entities based on context

Providing personalized recommendations

Summary

8. Querying Structured Data in a Natural Way

8. Querying Structured Data in a Natural Way

Tapping into academic content using the Academic API

Creating the backend using the Knowledge Exploration Service

Answering FAQs using QnA Maker

Summary

9. Adding Specialized Searches

9. Adding Specialized Searches

Searching the Web from the Smart-House application

Getting the news

Searching for images and videos

Helping the user with auto suggestions

Search commonalities

Summary

10. Connecting the Pieces

10. Connecting the Pieces

Connecting the pieces

Real-life applications using Microsoft Cognitive Services

Where to go from here

Summary

Appendix A. LUIS Entities and Intents

LUIS pre-built intents

LUIS pre-built entities

Appendix B. Additional Information on Linguistic Analysis

Part-of-Speech Tags

Phrase types

Appendix C. License Information

Appendix C. License Information

Video Frame Analyzer

OpenCvSharp3

Newtonsoft.Json

NAudio

Creating the backend using the Knowledge Exploration Service

The Knowledge Exploration Service (KES) is, in some ways, the backend for the Academic API. It allows us to build a compressed index from structured data, authoring grammar to interpret natural language.

To get started with KES, we need to install the service locally.

Note

To download the KES installer, go to https://www.microsoft.com/en-us/download/details.aspx?id=51488 .

With the installation comes some example data, which we will use.

The steps required to have a working service are as follows:

Define a schema.
Generate data.
Build the index.
Author grammar.
Compile grammar.
Host service.

Defining attributes

The schema file defines the attribute structure in our domain. When we previously discussed the Academic API, we saw a list of different entity attributes, which we could retrieve through the queries. This is defined in a schema.

If you open the file, Academic.schema, in the Example folder found where KES is installed, you will see...

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