Practical difficulties in the construction of ontologies and different kinds of knowledge representation in the Semantic

1. Practical difficulties in the construction of ontologies and different kinds of knowledge representation in the Semantic Web Mela Bosch melabosch@europe.com

2. What is a knowledge representation in the digital world? • It is a surrogate, a substitute for the thing itself, used to enable an entity to determine consequences by thinking rather than acting • It is a set of ontological commitments, to answer in what terms should we think about a domain • It is a fragmentary theory of intelligent reasoning, expressed in terms of three components: a. the representation's fundamental conception of intelligent reasoning; b. the set of inferences the representation sanctions; c. the set of inferences it recommends • It is a medium of human expression, a language in which we say things about the world • It is a medium for pragmatically efficient computation Ref: Davis, R., Shrobe, H., and Szolovits, P. What is a Knowledge Representation? AI Magazine, 14(1):17-33, 1993. http://groups.csail.mit.edu/medg/ftp/psz/k-rep.html

3. What is a knowledge representation in the Semantic Web ? • Description of content and formal aspects of web resources • This description is expressed by a metadata structure in a markup language: RDF, Resource Description Format • In a way understandable by machines There is not only one structure of knowledge representation. There are different structures and elements that belong to different languages So Semantic Web knowledge representation formalisms consist of different kinds of logics in different kinds of representation formats

5. First level of knowledge representation

6. Methodologies for intelligent systems Procedural  knowledge integrated in the program. Advantages : a great specificity: algorithms for each case. Disadvantages : lack of versatility, difficulty to modify. Declarative  knowledge representation is independent of the computational processes. Advantages: flexible and with hard logical base. Disadvantages: great level of abstraction,difficulty to maintain a consistent logic. Semantic web: declarative knowledge representation using metadata

7. The metadata is expressed in Markup language: <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" </rdf:RDF> Description logic also known as terminological logic • controlled language in which the syntax is independent of system procedures and the terminology is adequate to application domain • well defined semantics, very expressive Markup language: procedimental specifity and declarative abstraction

8. Second level of knowledge representation: well known for library science professionals

9. Second level of knowledge representation: another type of logic The logic of indexation for cataloguing and classifying But it is not the same to index an object and to index something which is the reference to that object

10. Semantic web indexes resources web That is to say, objects, not material objects, but digital objects. Reference items of the digital objects: authoring, date, etc. Objects are described through anontology Similar description to library science classification systems Others aspects of the digital objects such as: attributes, behavior, relationships and cardinality are expressed by another logic

11. has become the mainstream technique in the software industry

12. Third level of knowledge representation: Object oriented modeling paradigm Object-oriented point of view: Computer program: a set of interacting individual units, or objects, that manage their own state and operations Opposed to a traditional view in which a program may be seen as a collection of functions,or a list ofinstructions given to the computer.

13. Third level of knowledge representation: Object oriented logic to: • make domain assumptions explicit • separate domain knowledge from the operational knowledge • using terms for representing concepts that are an abstraction of objects’ main properties AdvantagesIntuitive: direct mapping from the real-world DisadvantagesDifficult to build large coherent and complete representationHand crafting object hierarchy

14. Object oriented logic: Fundamental concepts Class— the unit of definition of data and behavior for some kind-of-thing. Object — an instance of a class, an object Encapsulation — a type of privacy applied to the data, ensures that an object can be changed only through established channels Inheritance — provides a way to define a (sub)class as a specialization or subtype or extension of a more general class Abstraction — the ability to ignore the details of an object's (sub)class and work at a more generic level when appropriate Polymorphism — polymorphism is behavior that varies depending on the class in which the behavior is invoked, that is, two or more classes can react differently to the same message. Ref: wikipedia.org

15. Concept “Jaguar“ Object-Oriented Modeling and Ontology Engineering • There are steps in common: • an iterative process • Defining concepts in the domain (classes) • Arranging the concepts in a hierarchy (subclass-superclass hierarchy) • Defining which attributes and properties classes can have and constraints on their values • Defining individuals and filling in slot values Different: attributes, properties and values

16. Object-Oriented Modeling and Ontology Engineering • The same logic but: • An ontology • reflects the structure of the world • is often about structure of concepts • actual physical representation is not an issue • An Object Oriented class structure • reflects the structure of the data • is usually about behavior • describes the physical representation • of data (long int, char, etc.) • (Ref: http://protege.stanford.edu)

17. Object Oriented logic for construction of ontologiesand Object Oriented software: the same methodological premises as document classification systems . Top-down: define the most general concepts first and then specialize them Bottom-up: define the most specific concepts and then organize them in more general classes Combination: define the more salient concepts first and then generalize and specialize them • Document classification systems • Concepts • parts of the concepts • How they relate to other concepts Ref: http://www.ncess.ac.uk/insight/tutorials/datagrids/data_sh/ontologies/

18. Object Oriented logic and document classification • concepts • parts of the concepts • How they relate to other concepts • properties (attributes): contain primitive values (strings, numbers) • complex properties: contain (or point to) other objects Hybrid knowledge representation : supports a rich knowledge model with different kinds of representation at the same time Ref: http://www.ncess.ac.uk/insight/tutorials/datagrids/data_sh/ontologies/

19. Hierarchy: French wines Examples from: Protégé: a graphical ontology- development tool, open-source and freely available: (http://protege.stanford.edu) . Class structure usually constitute a taxonomic hierarchy: Class Subclass : Hierarchy: Pizza

20. Terminology Class=concept Instance= object Slot=property Facet=values Slot cardinality = the number of values a slot has (common facet) Slot value type = the type of values a slot has (common facet): string, num, boolean Minimum and maximum value = a range of values for a numeric slot (common facet) Default value = the value a slot has unless explicitly specified otherwise(common facet) Slots in a class definition describe attributes of instances of the class and relations to other instances: Each wine will have color, sugar content, producer, etc. • Types of properties • “intrinsic” properties: flavor and color of wine • “extrinsic” properties: name and price of wine • parts: ingredients in a dish • relations to other objects: producer of wine (winery)

21. Examples from: http://protege.stanford.edu/publications/ontology_development/ontology101.html Class instance creation Superclass Wine A subclass inherits all the slots from the superclass, but with a list of own allowed values Subclass French wine Ref: http://www.ncess.ac.uk/insight/tutorials/datagrids/data_sh/ontologies/

22. Common problems: Is a Margherita Pizza a Vegetarian Pizza? Errors in understanding common logical constructs A class can have more than one superclass A subclass inherits slots and facet restrictions from all the parents Different systems resolve conflicts differently • Ref: http://www.co-ode.org

23. Common problems: Is a Margherita Pizza a Vegetarian Pizza? Different subclasses Closure Axiom: only Correct hierarchy • Ref: http://www.co-ode.org

24. Semantic Web knowledge representation formalisms consist of different kinds of logics in different kinds of representation formats • there are many aspects involved such as description logic, classification systems, object oriented data structure and markup languages Conclusion “Every ontology is a treaty – a social agreement – among people with some common motive in sharing” Grubersays: Ref: http://www.sigsemis.org/newsletter/october2004/tom_gruber_interview_sigsemis The answer Collaborative approach to construction of ontologies • Communication between collaborators from different disciplines is difficult • Ontology Building, maintenance and reuse: time consuming activities, cost analysis is complex The problems

26. Much remains yet to be done Conclusion Trends: • Methodologies for collaboratively creating and managing shared information: Modeling semantically heterogeneous data sources and services, Representing and reasoning with ontologies and mappings between ontologies • Semantic community support systems and collaboration applications: Groupware tools for supporting collaborative ontology design, Semantic Wikis, semantic blogging, • Case studies and experience reports on semantics-aware collaborative applications • Cost Estimation Models for Ontology Engineering

27. Examples of ontologies: ProtegeOntologiesLibrary http://protege.cim3.net/cgi-bin/wiki.pl?ProtegeOntologiesLibrary References • Hodgson, Ralph; Keller, Paul. Collaborative Ontology-Based Systems. Innovator Perspectives and Demonstrations of New Open Standards and Technologies in Support of Ontology Engineered Solutions. TopQuadrant and NASA Ames. Collaborative Expedition Workshop #38. February 22, 2005 at NSF Semantic Conflict, Mapping, and Enablement: Making Commitments Together. http://www.topquadrant.com/documents/talks/TQ%20Ontology-Based%20Collaborative%20Environments%20(v4).pdfBased%20Collaborative%20Environments%20(v4).pdf • Díaz, Alicia, Baldo, G.CO-Protégé: A Groupware Tool for Supporting Collaborative Ontology Design with Divergence.Lifia, Fac. Informática- UNLP, La Plata, Argentina- Loria, Campus Scientifique, Vandœuvre-lès-Nancy cedex, France.http://protege.stanford.edu/conference/2005/slides/6.2_A.Diaz_Co-Protege_slices_and_flyer.pdf; http://protege.stanford.edu/conference/2005/submissions/abstracts/accepted-abstract-diaz.pdf • Gamper, Johann, Nejdl, Wolfgang; Wolpers, Martin.Combining Ontologies and Terminologies in Information Systems.European Academy Bolzano/Bozen,Scientific Area ``Language and Law''Bozen, Italy - Institut für Rechnergestützte Wissensverarbeitung University of Hannover, Germany. http://www.kbs.uni-hannover.de/Arbeiten/Publikationen/1999/tke99/ • W3C Working Draft 07 March 2002, Requirements for a Web Ontology Language. Latest version:http://www.w3.org/TR/webont-req/ • Institut für Informatik,  Freie Universität Berlin. OntologyEngineering Cost Estimation with ONTOCOM.http://ontocom.ag-nbi.de//index.html Contact me: melabosch@europe.com