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Tish Chungoora

Ever wondered how you could capture and represent knowledge to share it with someone else, using the most efficient way possible? Are you interested in learning how knowledge can be pieced together for human interpretation and Artificial Intelligence?

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Ever wondered how you could capture and represent knowledge to share it with someone else, using the most efficient way possible? Are you interested in learning how knowledge can be pieced together for human interpretation and Artificial Intelligence?

Chances are we've probably all at some point been faced with situations where we wished there was a quicker, more effective, way of capturing and representing knowledge so that it makes sense to human beings and computers. Knowledge modelling (or technically speaking, ontology modelling) is about the tools and techniques for capturing and representing knowledge. A knowledge model (a.k.a. ontology) is, basically, a representation that provides a basis for sharing meaning about some subject matter.

There are a great many uses of knowledge modelling from Artificial Intelligence to the Semantic Web, natural language processing, augmented controlled vocabularies & thesauri, reference models used in business analysis, engineering and heaps more. In this course, you'll learn how to go about modelling knowledge from a practical perspective, which means that in addition to getting an appreciation of the context of knowledge modelling, you'll also be expected to get your hands dirty. So, we'll be looking at applying different methods for building knowledge models. These methods include graphical as well as formal computer-aided techniques.

This course is for people who care about knowledge sharing and making knowledge a true asset to support knowledge management, systems interoperability, intelligent information architecture, best practice knowledge capture, and many more.

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What's inside

Learning objectives

  • Become better at approaching the organisation of information and knowledge in such a way that it makes sense to users
  • Apply a methodology for developing seamless knowledge models (ontologies) and use that understanding across any subject matter
  • Gain awareness of the inner workings of knowledge models (ontologies) expressed as visual and machine-interpretable representations
  • Develop semantically-rich ontologies and knowledge graphs, formalized in the web ontology language (owl), using the protégé ontology editor

Syllabus

Introduction

Welcome to the very first lecture in this course! We'll go through introductions and take a look at the high level aims and objectives of the course.

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This lecture should give you a pretty good idea of who the course targets, as well as the various learning objectives you expect to gain by the end of the course.

Well, this is simply going through the structure of the course. This lecture will give you a good idea of the roadmap for the course.

Here, you will find a decision tree diagram that will help you decide whether this course is really what you are after.

This lecture concludes Section 1, summarising the main points discussed.

What is knowledge modelling?

This lecture talks about the different types of knowledge and how knowledge is consumed by us. We'll touch on the generic context of knowledge modelling and see how it falls in the much wider area of knowledge management and information systems.

We very often assume we know what the differences are between data, information and knowledge. This lecture will clarify what we mean by these three concepts in the context of knowledge modelling.

This lecture will give you a taster of what a knowledge model (a.k.a. ontology) is. We'll look, conceptually and at a very high level, what a knowledge model consists of by drawing some useful analogies and without going too technical.

There's a much more profound underpinning when we talk about ontologies. This lecture looks at the philosophical perspective of ontologies and introduces the idea of different levels of abstraction for making sense of the entities in the world around us.

In this lecture, we'll take a look at the most fundamental components of a knowledge model. We'll introduce the concepts of classes, relationships, individuals and axioms that we can use to describe a particular subject matter.

The representation of knowledge models can be tailored for human and machine interpretation. In this lecture, we'll run through the basics of what's needed for being able to represent ontologies.

In addition to the philosophical perspective, ontologies can also be viewed from a logic based perspective. This lecture covers, at a relatively high level, what the logic based perspective is about.

In this lecture, we'll elaborate a little more on the different levels of abstraction in knowledge modelling. We'll take a look at how the stack of abstraction is layered, from the generic to the more specific.

Knowledge modelling has a great many applications, from things like knowledge management to Artificial Intelligence. This lecture summarizes what these applications are.

In this lecture we will see some concrete real-world examples of applied ontology.

This lecture concludes Section 2, summarising the main points discussed.

[Quiz] Knowledge modelling basics
A methodology for knowledge modelling

To conduct knowledge modelling the best way possible, you need a methodology that helps you manage your knowledge modelling expectations. This lecture introduces a methodology that brings out the best of a simple approach but providing sufficient coverage for the full life cycle of knowledge models.

Requirements manage is a concept used in things like project management and business analysis. It can also be used successfully in knowledge modelling. So, in this lecture, we'll take a look at why we need to manage requirements and how we go about doing that.

This lecture explains the goal and scope definition phase of our methodology. This is the phase where you highlight the high level aims, objectives, scope, etc., of your knowledge modelling activity. In this course, we'll be looking at how we go about building a knowledge model of different types of ballpoint pens based on their components and characteristics. We'll be using the ballpoint pen example throughout the course.

Some ontology development methodologies identify “competency questions” as a means of scoping a knowledge model. This lecture elaborates on this topic.

When modelling knowledge, you very often start off gathering unstructured or semi-structured information that you then later analyse. This lecture talks about the methods for gathering and eliciting information, as well as their advantages and potential drawbacks when it comes to collecting information for knowledge modelling.

In this lecture, we'll focus on explaining a couple of really useful methods, namely affinity diagrams and mind maps, to help us quickly spot early patterns in the organisation of ideas and concepts.

This lecture concludes Section 3, summarising the main points discussed.

[Quiz] Knowledge modelling methodology
Initial structuring

In this lecture, we'll introduce the initial structuring phase of our knowledge modelling methodology. Initial structuring is about transforming the unstructured or semi-structured information you collected in the previous phase into visually-represented knowledge models.

How can go from unstructured or semi-structured information to structured models? The starting point is about being able to analyse all sorts of content. This lecture looks at how we go about listing and analysing statements to start spotting ontology structures.

The analysis of statements leads to the compilation of a term pool, which you need to administer during the course of ontology modelling. We'll explore an approach, based on a spreadsheet to help you with easily tracking and monitoring the progress of your terms, as you make decisions as to what needs to go into your knowledge models and what you leave behind.

This lecture introduces the implications of choosing graphical languages for modelling the building blocks of knowledge models.

Unified Modelling Language (UML) is just one graphical method for representing subject matter. In this lecture, we'll see what UML has to offer to help us model ontology structures.

This is a continuation from the previous lecture. This time, we'll be looking at actually using a software application for representing knowledge models expressed in UML. If you do not have a suitable software installed for modelling in UML - no worries, you can simply use pen and paper!

In this lecture, we'll be looking at a second graphical language for representing ontologies, called IDEF5. We'll first introduce the basic shapes we need for modelling and then, we'll dive into doing some modelling work using Microsoft Visio. If you don't have Visio installed, you can still crack it out with pen and paper!

In this lecture, we'll be going through additional discussions on the topic of capturing and representing knowledge using graphical methods.

This lecture concludes Section 4, summarising the main points discussed.

[Quiz] Initial structuring
Formalization

The formalization phase of our methodology is about transforming our visual knowledge models into models that can be interpreted by computers. In other words, we'll learn how to code our models but without needing to do any coding at all, as a user interface application will handle that for us! We'll be going through an in-depth explanation of the Web Ontology Ontology (OWL) and see how to use Protégé ontology editor. In this section, we'll be showing how OWL works by going through various hands-on tutorials based around our ballpoint pen ontology.

In this lecture, we'll download Protégé ontology editor and also run through the steps for setting up an ontology in Protégé.

In this activity, you will download and run Protégé ontology editor, as well as provide an IRI for your ontology.

This lecture covers the basics of creating classes in OWL using Protégé.

This is a continuation from the previous lecture, where we'll look at further features of OWL classes.

In this activity, you will create a hierarchy of classes and declare disjoint classes.

This lecture covers the basics of creating individuals in OWL using Protégé.

In this activity, you will create individuals of classes.

This lecture covers the basics of creating properties in OWL using Protégé.

The domain and range allow you to specify the classes or sets of classes that your relationships are expected to connect to. In this lecture, we'll focus on how to specify the domain and range.

This lecture explains how to create inverse properties in OWL using Protégé.

Property characteristics help us build more meaningful relations in our knowledge models. This lecture explains all the property characteristics supported in OWL.

In this activity, you will create a hierarchy of object properties, define the domain & range of properties, define inverse properties and specify property characteristics.

By describing and defining classes, it becomes possible to build more meaningful class structures. In this lecture, we'll cover the basics of class description and definition in OWL.

Existential restrictions are one kind of description we can add to classes to capture their semantics. This lecture explains and exemplifies how to use existential restrictions in OWL.

In this activity, you will create existential restrictions.

'hasValue' restrictions are another kind of description we can add to classes to capture their semantics. This lecture explains and exemplifies how to use 'hasValue' restrictions in OWL.

In this activity, you will create hasValue restrictions.

This lecture explores the essence of primitive and defined classes in OWL.

In this activity, you will convert primitive classes to defined classes.

Ontology tools for OWL offer reasoning facilities for making inferences based on the structures and descriptions captured in our formal knowledge models. In this lecture, we'll see how to use a reasoner in Protégé to perform auto-classification of concepts for us.

In this activity, you will work with the inferred class hierarchy view and invoke a reasoner to reclassify the classes.

Universal restrictions are another kind of description we can add to classes to capture their semantics. This lecture explains and exemplifies how to use universal restrictions in OWL.

In this activity, you will create universal restrictions.

Cardinality restrictions are another kind of description we can add to classes to capture their semantics. This lecture explains and exemplifies how to use cardinality restrictions in OWL.

In this activity, you will create cardinality restrictions.

In OWL, datatype properties are relationships that link something to a data value. This lecture exemplifies how to work with datatype properties.

In this activity, you will create and work with datatype properties.

Class description and definition do not stop there. There are other ways in which we can capture the description of our classes. In this lecture, we'll be swinging by these extras.

In this activity, you will create a class as a complement of another.

Protégé comes with various plugins and tools for helping us do a range of things like visualizing our knowledge models, while we create them in the application. In this lecture, we'll run through the other useful features you need to be aware of.

In this activity, you will explore other useful features in ontology formalization.

This lecture concludes Section 5, summarising the main points discussed.

[Quiz] Formalization
Deployment

The deployment phase of the methodology is for us to exploit our knowledge models in their intended application or settings. This lecture introduces the idea of deployment, highlighting the considerations to bear in mind when rolling out our ontologies.

This lecture looks at a few tools and methods to generate ontology documentation from OWL files.

In this lecture, we'll be focusing on additional ways to share our knowledge models using visual and graphical methods.

This is a continuation from the previous lecture, where we'll look at the use of radial diagrams for composing ontology visuals.

This lecture explores, at a conceptual level, what the building blocks of ontology driven systems are. We'll discuss the basic architecture for being able to 'plug' our formal knowledge models into actual information systems for people to start using.

This lecture concludes Section 6, summarising the main points discussed.

Evaluation

The evaluation phase is the last one in our methodology. This phase looks at all the previous stages and how well we accomplished them. In this phase, you also try to understand how to continuously improve the whole process of ontology development and deployment, in light of future knowledge modelling cycles.

Lessons learnt is a knowledge management concept that we can apply during the evaluation phase of the knowledge modelling methodology. Here, we'll touch on the basics of lessons learnt and its importance.

This lecture deals with the commonly made mistakes during ontology development that could affect the quality of your work.

This lecture concludes Section 7, summarising the main points discussed.

Course wrap-up

This lecture provides an overview of the 'softer' skills to target when learning to become a great knowledge architect.

This is the concluding lecture in this series. The lecture briefly wraps up everything we've covered in the course.

Download course slides.

Attributions, special thanks and disclaimer.

Bonus lecture

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops foundational skills in knowledge modelling, which are core skills for knowledge sharing and management
Teaches practical applications of knowledge modelling for Artificial Intelligence
Develops fundamental knowledge of knowledge models, which are standard in ontology modelling
Provides in-depth understanding of formal knowledge models
Taught by Tish Chungoora, who are recognized for their work in ontology modelling
Requires no prior knowledge of ontology modelling

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Practical Knowledge Modelling: Ontology Development 101 with these activities:
Review ontology fundamentals
Reviewing basic ontology concepts will provide a solid foundation for understanding the course material.
Browse courses on Ontology
Show steps
  • Read introductory articles on ontology.
  • Review your notes or study materials on ontology.
  • Do practice questions or exercises on ontology.
Volunteer for an ontology-related project
Volunteering on ontology-related projects can provide valuable hands-on experience and contribute to the field.
Browse courses on Ontology
Show steps
  • Identify organizations or projects that are involved in ontology development or research.
  • Contact the organizations and inquire about volunteer opportunities.
  • Contribute your skills and knowledge to the project, such as ontology modeling, data annotation, or user testing.
  • Get feedback from experienced professionals and learn from their expertise.
Contribute to an ontology-related open-source project
Contributing to open-source ontology projects can enhance your understanding of ontology practices and connect you with a community of developers.
Browse courses on Ontology
Show steps
  • Identify ontology-related open-source projects that align with your interests.
  • Review the project's documentation and familiarize yourself with its goals and objectives.
  • Identify areas where you can contribute, such as bug reporting, code refactoring, or feature implementation.
  • Submit your contributions to the project and engage with the community for feedback.
Show all three activities

Career center

Learners who complete Practical Knowledge Modelling: Ontology Development 101 will develop knowledge and skills that may be useful to these careers:
Knowledge Architect
A Knowledge Architect is a professional who designs, builds, and manages knowledge bases, ensuring that organizations can access and use their knowledge effectively. Those looking to start or advance in the field of Knowledge Architecture may benefit from the course, as it covers the practical skills and theoretical foundations needed to succeed. The course's focus on ontology development will be especially useful, as ontologies are a core component of knowledge management systems.
Ontology Engineer
Ontology Engineers specialize in developing and maintaining ontologies, ensuring that they are consistent, accurate, and useful for their intended applications. This course provides a comprehensive introduction to ontology development, covering the different methods and techniques used by professionals in the field. The course's hands-on exercises and real-world examples will be especially valuable for those looking to gain practical experience in ontology engineering.
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting data to extract insights and inform decision-making. This course provides a foundation in knowledge modeling and ontology development, which can be valuable for Data Scientists who need to organize and structure data effectively. The course's focus on semantic technologies will be especially useful for those working with complex data sets.
Information Architect
Information Architects design and organize information systems to make them easy to find, understand, and use. This course provides a foundation in knowledge modeling and ontology development, which can be beneficial for Information Architects who need to create structured and meaningful information architectures. The course's emphasis on user experience and usability will be especially valuable for those working on public-facing websites and applications.
Knowledge Manager
Knowledge Managers are responsible for managing and sharing an organization's knowledge assets, ensuring that they are accessible and useful to employees. This course provides a comprehensive introduction to knowledge management, covering the different methods and techniques used by professionals in the field. The course's focus on ontology development will be especially valuable for those working with complex knowledge domains.
Business Analyst
Business Analysts are responsible for understanding and documenting business requirements, and then working with IT teams to implement solutions that meet those requirements. The course provides a foundation in knowledge modeling and ontology development, which Business Analysts can use to create clear and concise requirements specifications. The course's emphasis on communication and collaboration will be especially valuable for those working on complex business projects.
Software Engineer
Software Engineers design, develop, and maintain software applications. This course provides a foundation in knowledge modeling and ontology development, which can be valuable for Software Engineers who need to create complex and structured software systems. The course's hands-on exercises and real-world examples will be especially valuable for those looking to gain practical experience in software engineering.
User Experience Designer
User Experience Designers are responsible for designing and evaluating user interfaces to ensure that they are easy to use and enjoyable. This course provides a foundation in knowledge modeling and ontology development, which can be valuable for User Experience Designers who need to create user interfaces that are both functional and intuitive. The course's emphasis on human-computer interaction and usability will be especially valuable for those working on user-facing applications.
Instructional Designer
Instructional Designers are responsible for designing and developing learning materials and experiences. This course provides a foundation in knowledge modeling and ontology development, which can be beneficial for Instructional Designers who need to create structured and engaging learning experiences. The course's emphasis on pedagogy and learning theory will be especially valuable for those working on educational materials.
Technical Writer
Technical Writers are responsible for creating and maintaining documentation for technical products and systems. This course provides a foundation in knowledge modeling and ontology development, which can be valuable for Technical Writers who need to create clear and concise technical documentation. The course's emphasis on writing and communication will be especially valuable for those working on complex technical products.
Librarian
Librarians are responsible for organizing and managing information resources, and providing access to information to users. This course provides a foundation in knowledge modeling and ontology development, which can be beneficial for Librarians who need to create and maintain structured and accessible information collections. The course's emphasis on information organization and retrieval will be especially valuable for those working in libraries and information centers.
Archivist
Archivists are responsible for preserving and managing historical records and artifacts. This course may be useful for Archivists who need to organize and manage complex collections of historical materials. The course's focus on knowledge modeling and ontology development can help Archivists to create structured and meaningful representations of their collections, making them easier to access and use.
Museum curator
Museum Curators are responsible for managing and interpreting museum collections, and for developing and delivering educational programs. This course may be useful for Museum Curators who need to organize and manage complex collections of artifacts and specimens. The course's focus on knowledge modeling and ontology development can help Museum Curators to create structured and meaningful representations of their collections, making them more accessible and educational.
Historian
Historians are responsible for researching, analyzing, and interpreting the past. This course may be useful for Historians who need to organize and manage complex collections of historical documents and artifacts. The course's focus on knowledge modeling and ontology development can help Historians to create structured and meaningful representations of their research, making it easier to access and use.
Anthropologist
Anthropologists are responsible for studying human societies and cultures. This course may be useful for Anthropologists who need to organize and manage complex collections of ethnographic data. The course's focus on knowledge modeling and ontology development can help Anthropologists to create structured and meaningful representations of their research, making it easier to access and use.

Reading list

We've selected seven books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Practical Knowledge Modelling: Ontology Development 101.
Provides a comprehensive overview of data science, including topics such as data mining, data visualization, and machine learning. It practical guide for business professionals who want to learn how to use data to make better decisions.
Provides a comprehensive overview of machine learning, including topics such as supervised learning, unsupervised learning, and reinforcement learning. It practical guide for beginners who want to learn how to use machine learning to solve real-world problems.
Provides a comprehensive overview of deep learning, including topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It classic textbook in the field of deep learning and is used by many universities worldwide.
Provides a comprehensive overview of reinforcement learning, including topics such as Markov decision processes, value functions, and policy optimization. It classic textbook in the field of reinforcement learning and is used by many universities worldwide.
Provides a comprehensive overview of natural language processing, including topics such as syntax, semantics, and pragmatics. It classic textbook in the field of natural language processing and is used by many universities worldwide.
Provides a comprehensive overview of computer vision, including topics such as image processing, object recognition, and video analysis. It classic textbook in the field of computer vision and is used by many universities worldwide.

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