Semantic Web - Ontology 101
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The document is a comprehensive guide on creating OWL ontologies, detailing the fundamental concepts of OWL2, including axioms, entities, and classes. It outlines the ontology development process, emphasizing the importance of defining terms, creating classes and properties, and setting constraints. Additionally, it provides practical advice on ontology engineering, tools for development, and addresses common modeling challenges.
Semantic Web - Ontology 101
- 1. Ontology 101 GETTING STARTED… A guide and a process for creating OWL ontologies
- 2. Recap: OWL2 • OWL 2 is a knowledge representation language, designed to formulate, exchange and reason with knowledge about a domain of interest • Basic notions – Axioms: the basic statements that an OWL ontology expresses – Entities: elements used to refer to real-world objects – Expressions: combinations of entities to form complex descriptions from basic ones • The results of the modeling processes are called ontologies • Knowledge consists of elementary pieces that are often referred to as statements or propositions • Statements that are made in an ontology are called axioms in OWL 2 09/02/2017 Ontology 101 2
- 3. Recap: OWL2 • When humans think, they draw consequences from their knowledge • A statement is a consequence of other statements essentially means that this statement is true whenever the other statements are • In OWL terms: “a set of statements A entails a statement a if in any state of affairs wherein all statements from A are true, also a is true” • A set of statements may be – Consistent, if there is a possible state of affairs in which all the statements in the set are jointly true – Inconsistent, if there is no such state of affairs • The formal semantics of OWL specifies, in essence, for which possible “states of affairs” a particular set of OWL statements is true 09/02/2017 Ontology 101 3
- 4. Recap: What is an ontology? • An ontology is an explicit description of a domain – concepts – properties and attributes of concepts – constraints on properties and attributes – individuals (often, but not always) • An ontology defines – a common vocabulary – a shared understanding 09/02/2017 Ontology 101 4
- 5. Recap: What is in a OWL2 ontology? • Classes • Instances • Properties – Object Properties – DataType Properties • Restrictions • Annotations 09/02/2017 Ontology 101 5
- 6. Recap: Tools • Editors (http://semanticweb.org/wiki/Editors) – Most common editor: Protégé 5 – Other tools: TopBraid Composer ($), NeOn toolkit – Special purpose apps, esp. for light-weight ontologies (e.g., FOAF editors) • Reasoners (http://semanticweb.org/wiki/Reasoners) – OWL DL: Pellet 2.0*, HermiT, FaCT++, RacerPro ($) – OWL EL: CEL, SHER, snorocket ($), ELLY – OWL RL: OWLIM, Jena, Oracle OWL Reasoner ($) – OWL QL: Owlgres, QuOnto, Quill * The next-gen reasoner (version 3) is part of Stardog, a closed source RDF database 09/02/2017 Ontology 101 6
- 7. Ontology Engineering • The process of building and maintaining an ontology • To define terms in a domain and relations among such terms – defining concepts (classes) in the domain – arranging the concepts in a hierarchy (subclass- superclass hierarchy) – defining which properties classes can have and constraints on their values (restrictions) – defining individuals (instances) and filling in properties values 09/02/2017 Ontology 101 7
- 8. Ontology Engineering vs. OOP Ontology Engineering An ontology • reflects the structure of the world • is often about structure of concepts • actual physical representation is not an issue Object-Oriented Modeling A class • reflects the structure of the data and code • is usually about behavior (methods) • describes the physical representation of data (int, string, etc.) 09/02/2017 Ontology 101 8
- 9. Why develop an ontology? A. You hate your life! B. You need to fill several days and weeks with "something" C. Other 09/02/2017 Ontology 101 9
- 10. Why develop an ontology? • To share common understanding of the structure of information – among people – among software artifacts • To enable reuse of domain knowledge – to avoid “re-inventing the wheel” – to introduce standards to allow interoperability 09/02/2017 Ontology 101 10
- 11. Why develop an ontology? • To make domain assumptions explicit – easier to change domain assumptions (consider a genetics knowledge base) – easier to understand and update legacy data • To separate domain knowledge from the operational knowledge – re-use domain and operational knowledge separately (e.g., configuration based on constraints) • To analyze domain knowledge 09/02/2017 Ontology 101 11
- 12. Create an ontology: the process 1. Determine the scope 2. Consider reuse 3. Enumerate terms 4. Define classes 5. Define properties 6. Define constraints 7. Create instances 09/02/2017 Ontology 101 12 Determine the scope Consider reuse Enumerate terms Define classes Define properties Define constraints Create instances TODAY!closely intertwined
- 13. Create an ontology: the process However, in the real world… 09/02/2017 Ontology 101 13 Determine the scope Consider reuse Enumerate terms Define classes Define properties Define constraints Create instances Consider reuse Enumerate terms Define classes Define classes Define properties Define classes Create instances Consider reuse Define constraints Create instances Define properties …
- 14. Disclaimer • There is no one “correct” way or methodology for developing ontologies • It is a long, hard, and precise activity • Fundamental rules: 1. There is no one correct way to model a domain— there are always viable alternatives. The best solution depends on the application that you have in mind and the extensions that you anticipate. 2. Ontology development is necessarily an iterative process. 3. Concepts in the ontology should be close to objects (physical or logical) and relationships in your domain of interest. These are most likely to be nouns (objects) or verbs (relationships) in sentences that describe your domain. 09/02/2017 Ontology 101 14
- 15. 1. Determine domain and scope • What is the domain that the ontology will cover? • For what we are going to use the ontology? • For what types of questions the information in the ontology should provide answers (competency questions)? • Who will use and maintain the ontology? Answers to these questions may change during the lifecycle of an ontology 09/02/2017 Ontology 101 15
- 16. Competency Questions • One way to determine the scope of the ontology • Write down a list of questions that a knowledge base built upon the ontology should be able to answer • These questions may serve also later, for a preliminary evaluation and for end the process • Questions do not need to be exhaustive, just a "sketch" 09/02/2017 Ontology 101 16
- 17. Learning by Example • We will apply the process presented here up to the first iteration • The chosen domain is: University – From which perspective? – What are our competency questions? 09/02/2017 Ontology 101 17
- 18. 2. Consider reuse • In some cases, it is a good idea to reuse existing ontologies • Why? – to save the effort – to interact with the tools used by other ontologies – to employ ontologies that have been already validated through use in applications • What to reuse? 09/02/2017 Ontology 101 18
- 19. What to reuse? • Upper/general ontologies – Cyc, http://www.cyc.com – DOLCE (Descriptive Ontology for Linguistic and Cognitive Engineering), http://www.loa.istc.cnr.it/old/DOLCE.html – WordNet, http://www.cogsci.princeton.edu/~wn/ • Domain-specific ontologies – GO (Gene Ontology), http://ww.geneontology.org – DogOnt, http://elite.polito.it/ontologies/dogont – MUO, http://idi.fundacionctic.org/muo/ 09/02/2017 Ontology 101 19
- 20. 3. Enumerate important terms • Write down a list of all terms we would like to – make a statement about – explain to a user • Questions – What are the terms we need to talk about? – What are the properties of these terms? – What would we like to say about the terms? • Initially, it is important to get a comprehensive list of terms – without worrying about overlap between concepts, relations, properties, hierarchy, etc. 09/02/2017 Ontology 101 20
- 21. 4. Define classes and their hierarchy • A class is – a concept in the domain, not the word that denote the concept – a collection of elements with similar properties • Instances of classes – specific (named) elements that pertain to that collection • Classes usually constitute a taxonomic hierarchy – a subclass-superclass hierarchy • A class hierarchy is usually an IS-A hierarchy – an instance of a subclass is an instance of a superclass 09/02/2017 Ontology 101 21
- 22. Modes of development 1. top-down – define the most general concepts first and then specialize them 2. bottom-up – define the most specific concepts and then organize them in more general classes 3. combination – define the more salient concepts first and then generalize and specialize them None of them is inherently better than the others 09/02/2017 Ontology 101 22
- 23. 5. Define properties • Attributes of instances of the class and relations to other instances • Types of properties – “intrinsic”, e.g., color of an object – “extrinsic”, e.g., price of an object – relations to other instances, e.g., producer of an object • Simple and complex properties – simple properties (data properties): contain primitive values like strings and numbers – complex properties (object properties): contain or point to other objects, e.g., a manufacturer instance 09/02/2017 Ontology 101 23
- 24. 6. Define constraints • Property describe or limit the set of possible values for a slot – The name of an object is a string – Politecnico di Torino is an instance of University – A university has exactly one location (main campus) • We mainly refer to properties restrictions – cardinality, domain, range, etc. – see the previous set of slides for further details 09/02/2017 Ontology 101 24
- 25. Properties and Classes Inheritance • A subclass inherits all the properties from the superclass • If a class has multiple superclasses, it inherits properties from all of them • A subclass can override the restrictions to “narrow” the list of allowed values – make the cardinality range smaller – replace a class in the range with a subclass 09/02/2017 Ontology 101 25
- 26. 7. Create instances • The last step (hopefully!) • Defining an individual require to – choose the desired class – create the desired instance of the class – fill in the properties values • Some ontologies may not have instances • In most cases, classes and instances are in two separate OWL files 09/02/2017 Ontology 101 26
- 27. Create an ontology: the process 1. Determine the scope 2. Consider reuse 3. Enumerate terms 4. Define classes 5. Define properties 6. Define constraints 7. Create instances 09/02/2017 Ontology 101 27 Determine the scope Consider reuse Enumerate terms Define classes Define properties Define constraints Create instances
- 28. COMMON PROBLEMS… … and their solutions 09/02/2017 Ontology 101 28
- 29. Multiple inheritance • A class can have more than one superclass • A subclass inherits properties and restrictions from all the parents 09/02/2017 Ontology 101 29
- 30. Disjoint classes • Classes are disjoint if they cannot have common instances • Disjoint classes cannot have any common subclasses either 09/02/2017 Ontology 101 30
- 31. Avoid class cycles • Danger of multiple inheritance: cycles in the class hierarchy • If class A has a subclass B and, at the same time, B is a superclass of A, the classes are equivalent! 09/02/2017 Ontology 101 31
- 32. Siblings in the hierarchy • All the siblings in the class hierarchy must be at the same level of generality – Think about section and subsections in a book 09/02/2017 Ontology 101 32
- 33. How many class is too many? How few classes are too few? • Rule of thumb • If a class has only one direct subclass, there may be a modeling problem – or the ontology is not complete • If there are more than a dozen subclasses for a given class, then additional intermediate may be necessary 09/02/2017 Ontology 101 33
- 34. Naming, domain and range • Single and plural class names – Singular or plural – It is up to you but you need to choose • Domain and range – When defining a domain or range, find the most general class or classes 09/02/2017 Ontology 101 34
- 35. Limiting the scope • An ontology should not contain all the possible information about the domain • No need to specialize or generalize more than the application requires • No need to include all possible properties of a class – Only the most salient properties – Only the properties that the applications require 09/02/2017 Ontology 101 35
- 36. References • Natalya F. Noy and Deborah L. McGuinness, "Ontology Development 101: A Guide to Creating Your First Ontology", Knowledge Systems Laboratory, Stanford University, March 2001 • Asunción Gómez-Pérez, Mariano Fernandez- Lopez and Oscar Corcho, "Ontological Engineering", Springer-Verlag London, 2004 09/02/2017 Ontology 101 36
- 37. Questions? 01RRDIU SEMANTIC WEB Luigi De Russis luigi.derussis@polito.it
- 38. License • This work is licensed under the Creative Commons “Attribution- NonCommercial-ShareAlike Unported (CC BY-NC-SA 3,0)” License. • You are free: – to Share - to copy, distribute and transmit the work – to Remix - to adapt the work • Under the following conditions: – Attribution - You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work). – Noncommercial - You may not use this work for commercial purposes. – Share Alike - If you alter, transform, or build upon this work, you may distribute the resulting work only under the same or similar license to this one. • To view a copy of this license, visit http://creativecommons.org/license/by-nc-sa/3.0/ 09/02/2017 Ontology 101 38