FA-0494Software DevelopmentDevOps, Cloud & InfrastructureData & Analytics

Implementing Knowledge Graph Technology

for Question Answering System

Introduction

Why this course

This 1 + 3 + 1 day course provides an introduction to semantic technology and its application in building a Q & A app with Python. Participants will learn about natural language processing, knowledge graphs, and the Semantic Web. They will also gain hands-on experience in using Python libraries for semantic technology, creating a knowledge graph, and integrating it with a FastAPI app to create a Q & A app.

Learning Outcome

By the end of the course, participants will be able to:

Understand the basics of semantic technology and its role in Q & A applications
Use Python libraries for natural language processing and semantic technology
Create a knowledge graph using RDF and OWL
Build SPARQL queries to retrieve information from a knowledge graph
Implement a Flask app to serve as the front-end of a Q & A app
Integrate the knowledge graph and natural language processing into a Q & A app
Deploy a Q & A app using a cloud service provider
Evaluate and improve a Q & A app based on semantic technology

Duration : 4 Days

Prerequisites

Basic programming knowledge, including familiarity with Python
Understanding of web development concepts, such as HTML and CSS
Basic understanding of database concepts, such as SQL
Familiarity with Linux command line interface and basic shell scripting
Completion of the Course “Knowledge Graph Technology for Question Answering System.”

Additionally, some prior exposure to natural language processing and web ontology languages such as RDF and OWL would be helpful, but not required.

Training outline

5 modules

01Day 1:4 topics

Prerequisites overview
Python recap
Python app deployment Recap
Project Overview

02Day 2:3 topics

Installing and setting up prerequisites and required libraries
Exploring natural language processing using Python
Creating a simple Q & A app without semantic technology

03Day 3: 3 topics

Building a simple knowledge graph using RDF and OWL
Creating SPARQL queries to retrieve information from the knowledge graph
Integrating the knowledge graph into the Q & A app

04Day 4:3 topics

Building a FastAPI app to serve as the front-end of the Q & A app
Integrating the knowledge graph and natural language processing into the app
Deploying the Q & A app using a cloud service provider

Conclusion:

Recap of the concepts covered in the course
Discussion of possible extensions or improvements to the Q & A app
Recommendations for further learning in semantic technology and Python

05Day 5:1 topics

Assessment

Create a Simple Knowledge Graph: Provide students with a dataset and ask them to create a simple knowledge graph using RDF and SPARQL. The knowledge graph should include a hierarchy of concepts, relationships between entities, and properties for each entity.
Querying a Knowledge Graph: Provide students with the knowledge graph from exercise 1 and ask them to write SPARQL queries to retrieve specific information from the graph. The queries should demonstrate understanding of the RDF data model and basic SPARQL syntax.
Using Knowledge Graphs for Search and Recommendation: Ask students to research a company or organization that uses knowledge graphs for search or recommendation and write a short report on how they use the technology. The report should demonstrate an understanding of the benefits and challenges of using knowledge graphs for these applications.
Knowledge Graphs in Action: Provide students with a real-world use case of a knowledge graph and ask them to analyze it using SPARQL queries. The use case should demonstrate how knowledge graphs can be used to solve real-world problems and how to query and analyze the data in the graph.

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