1. Ontology and Context Modeling November 20, 2008 Sung-Bae Cho 0

2. Ontology –Introduction –Ontology Components –Ontology Development Process –Ontology Languages Applications Using Ontology for Context Awareness –Contexts Manipulation Using Ontology –AmbieSense Project –Modeling Context Ontology based on Activity Theory Summary & Review Agenda 1

3. Contexts and Ontology Contexts [Dey et. al. 2001] –Context is any information that can be used to characterize the situation of an entity. –Three Fundamental Elements for characterizing the situation Environments - Location, Building, Room, etc. Computational Entity – Smart Sensors, Actuators, etc. User – Profile, Schedule, Activities, etc. Context-Aware System –A system that uses contexts to provide relevant information and services to user Context and ontology –Ontology can define the context as a formal information –Context can be shared as a type of ontology

4. What’s an Ontology? An ontology is an explicit specification of a conceptualization. –Thomas Gruber An ontology is a well-organized system of human knowledge and information made for machines to understand them easily and correctly. An ontology is a common framework that allows data to be shared and reused by human and machines. Other expression –a common vocabulary –a shared understanding 3/44

5. Ontology Structure 4

6. Ontologies Vs. Data Models No strict line in between, but ontologies are –More general –More reusable –Intended for multiple purposes, goals, and users –More easily shareable –Take stand on semantics of concepts (as opposed to mere structure and integrity) 5

7. What Is a Concept? Concepts (among other things) are in general language independent (words 'cat' and 'kissa' denote the same concept) –Are mental or logical representations of reality –Are related to other concepts –Do not need symbols but hold them for means of communication A concept has –Intension or meaning –Extension, i.e. the set of objects that the concept refers to On the difference between intension and extension, consider phrases "Evening star" and "Morning star" that have different meanings (intension) yet both refer to planet Venus (extension) Ontology is mainly concerned with intension 6

8. Ontology in Philosophy Semantics –The meaning of meaning Philosophical discipline, branch of philosophy that deals with the nature and the organization of reality Science of Being (Aristotle, Metaphysics, IV,1) Tries to answer the question –What is being? –What are the features common to all beings? 7

9. Ontology in Computer Science –Tom Gruber –An ontology is a specification of a conceptualization An ontology is a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents –Nicola Guarino –In Artificial Intelligence, an ontology refers to an engineering artifact, constituted by a specific vocabulary used to describe a certain reality, plus a set of explicit assumptions regarding the intended meaning of the vocabulary words 8

10. Ontologies and Controlled Vocabularies Ontology is a Controlled Vocabulary of –Types of subjects, –Types of relations among subjects, –Rules, axioms and constraints. Controlled Vocabulary - a fixed set of (agreed upon) names used within a certain community to refer to subjects in a certain domain.

11. Ontology/CV Examples Glossary –Controlled vocabularies + natural language explanation of the meaning of terms. Meaning is expressed in a human readable form and help human to understand the meaning of terms, often ambiguous. Glossaries were intended to help humans not machines. Thesauri –Controlled vocabularies or glossary + some additional semantics. Synonyms / homonyms / antonyms relationships. Broader / narrower terms. Index –Controlled vocabularies + references to the subject occurrences. Taxonomy and Classification –Controlled vocabulary + hierarchic structure.

12. Why Ontology? Labeling –If I say “car” and you say “voiture” how do we know we mean the same thing? Semantics –If I say “vehicle”, how do you know if this includes buses, powered motorcycles To share common understanding of the structure of descriptive information –Among people –Among software agents –Between people and software To enable reuse of domain knowledge –To avoid “re-inventing the wheel” –To introduce standards to allow interoperability 11

13. 12/36 Why Ontology? (2) 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 manage the combinatorial explosion

14. Ontology –Introduction –Ontology Components –Ontology Development Process –Ontology Languages Applications Using Ontology for Context Awareness –Contexts Manipulation Using Ontology –AmbieSense Project –Modeling Context Ontology based on Activity Theory Summary & Review Agenda 13

15. Knowledge Models Taxonomy of knowledge models –Contains many kinds of information Set of Examples Set of Statements Typologies Component Systems Hybrid Conceptual Systems Series Procedures Parallel Procedures Iterative Procedures Norms and Constraints Laws and Theories Decision Trees Control Rules Processes and Methods Processes Methods Multi-actor workflows Set of traces Procedural Models Prescriptive Models Knowledge Models Factual Models Conceptual Models

16. Semantic Networks Knowledge represented as a network or graph –represents semantic relations between the concepts –often used as a form of knowledge representation –a directed or undirected graph consisting of vertices, which represent concepts, and edges A simple type of ontology

17. Semantic Networks Feature By traversing network we can find: –That Nellie has a head (by inheritance) –That certain concepts related in certain ways (e.g., apples and elephants). BUT: Meaning of semantic networks was not always well defined. –Are all Elephants big, or just typical elephants? –Do all Elephants live in the “same” Africa? –Do all animals have the same head? For machine processing these things must be defined.  Formal ontology supports the requirements

18. 17/36 Ontology Components Concepts / Class –Concepts of the domain or tasks, which are usually organized in taxonomies Example: Person, Car, University, … Relations –A type of interaction between concepts of the domain Example: subclass-of, is-a, … Functions –A special case of relations in which the n-the element of the relationship is unique for the n-1 preceding elements Example: Father_of, Sum_of_Price,… Axioms –Model sentences that are always true Example: a+0=0, if x > y, then x+a > y+a, … Instances / Individuals –To represent specific elements Example: Student called Peter, …

19. 18/36 Ontology Components (2) First Order Logic (FOL)Description Logic (DL) Classes Relations Functions Instances Concepts Roles (w/Function, Axiom) Individuals

20. Taxonomy, Ontology, Knowledgebase 19

21. 20/36 Taxonomy Taxonomy := Segmentation, classification and ordering of elements into a classification system according to their relationships between each other

22. 21/36 Thesaurus Terminology for specific domain Graph with primitives, 2 fixed relationships (similar, synonym) Originate from bibliography

23. 22/36 Topic Map A standard for the representation and interchange of knowledge, with an emphasis on the findability of information. The ISO standard is formally known as ISO/IEC 13250:2003

24. 23/36 Ontology Representation Language: Predicate Logic Standards: RDF(S), OWL

25. Knowledge Description & Reasoning Level 24/36 Taxononmy Thesaurus Topic Map Ontology Knowledge Reasoning Level Knowledge search Knowledge Description Level

26. Ontology –Introduction –Ontology Components –Ontology Development Process –Ontology Languages Applications Using Ontology for Context Awareness –Contexts Manipulation Using Ontology –AmbieSense Project –Modeling Context Ontology based on Activity Theory Summary & Review Agenda 25

27. Ontology Development Process Ontology development process consists in seven steps 1. Specification 2. Knowledge acquisition 3. Conceptualization 4. Integration 5. Implementation 6. Evaluation 7. Documentation Ontology development is an iterative process –After evaluation we came back to previous phases and corrected mistakes 26

28. Specification Requirement Analysis What is the goal of the ontology? –What is the usage?, users specifications … What is relevant to fulfill the goal? –E.g., entities, relationships, restrictions What need to be modeled? –E.g., key components of car, types of car What granularity is useful? –What parts should be described, what is unnecessary … 27

29. Knowledge Acquisition Try to get the information based on the available documents in different data sources Put the information in a hierarchy structure with respect to the ontology scope This step occurs in parallel with specification step 28

30. Conceptualization and Integration Concepts in the ontology should be close to objects (physical or logical) and relationships in your domain of interest In order to obtain some uniformity across your ontology with other ontologies, try to get definitions from other ontologies 29

31. Implementation and Evaluation Implementation consists in define all the ontology components through an ontology definition language generally in two stages –Informal stage Ontology is sketched out using either natural language descriptions or some diagram technique –Formal stage Ontology is encoded in a formal knowledge representation language, that is machine computable –Different tools (e.g., Protégé) may help in the implementation Evaluation consists in checking for completeness, consistence and avoiding from redundancy –Different tools (e.g., RACER) may help in the evaluation 30

32. Documentation Produce clear informal and formal documentation Make ontology understandable! An ontology that cannot be understood will not be reused 31

33. Ontology Development Process 32

34. 33/36 Cyclic Definition Cycles are common in many KR systems, though rarely “a good thing” Cycles are disallowed by some tools because they prohibit “code generation”, including RDF/OWL Classes A, B, and C have equivalent sets of instances –By many definitions, A, B, and C are equivalent –Use owl: equivalentClass instaed of creating cycles A C B Is-a

35. 34/36 Siblings in the Class Hierarchy All siblings should be specified at roughly the same level of generality Compare to section and subsections in a book

36. 35/36 Class Specification If a class has only one child, there may be a modeling problem – often a sign that a definition is incomplete If the only Red Burgundy we have is Cotes d’Or, why introduce the subclass? Subclass of a class usually have –Additional properties –Additional slot restrictions –Participate in different relationships Compare to bullets in a bulleted list

37. 36/36 Creating Levels and Subclasses If a class has a large number of subclasses, it may be useful to define intermediate levels For example, in the domain of wines, there are natural groupings around wine color However, if no natural classification exists, the long list may be appropriate

38. 37/36 Inheritance, Naming, Synonyms A “wine” is not a subclass of “wines” A particular vintage should be classified as an instance of the class Wines Class names should be either –All singular –All plural Synonym names for the same concept are not different classes Many systems, metadata standards support synonymous terms as part of a class definition OWL allows defining necessary and sufficiency condition definitions thereby allowing synonym definitions to be “first class” terms Instance MariettaOldVinesRed Instance-of

39. 38/36 Class vs. Property Value Do concepts with different slot values becomes restrictions for different slots? How important is the distinction for the domain? Class definitions for most domains should be fairly stable – i.e., they should not change frequently once the definitions are established and individuals created OR Wine color: Red, White, Rose

40. 39/36 Class vs. Individual Individual instances are the most specific objects in an ontology If concepts form a natural hierarchy, represent them as classes If they will have instances below them, represent them as classes

41. Ontology –Introduction –Ontology Components –Ontology Development Process –Ontology Languages Applications Using Ontology for Context Awareness –Contexts Manipulation Using Ontology –AmbieSense Project –Modeling Context Ontology based on Activity Theory Summary & Review Agenda 40

42. Ontology Languages ExpressivityKIF/S CL OKB C F-LogicLOO M RDF/ S OW L UM L Class ●●●●●●● Slots/Attributes ●●●●●●● Metaclasses ━━━━━━● Number Restrictions ●●●●●● Complex Class Extensions ●●●●●━ Subsumption Hierarchies ●●●●━●● Value Restrictions ●●●●●━ Add New Facets ●●●━ Behaviors, Procedures, Methods ●●●● Relations / Functions ●━●●●●● Slots/Attributes ●━●●━━━ Subsumption Hierarchies ●━━●●●━ N-ary Relations/Functions ●━━●━━━ Built-in Functions, Equations, Formulate ●●● Instances / Individuals / Facts ●●●●●●● Axioms ●━●●●━ Production Rules ●●●━ 41/36

43. OWL Web Ontology Language Official W3C Standard since Feb 2004 Based on predecessors (DAML+OIL) A Web Language: Based on RDF(S) An Ontology Language: Based on logic

44. OWL Ontologies What’s inside an OWL ontology –Classes + class-hierarchy –Properties (Slots) / values –Relations between classes (inheritance, disjoints, equivalents) –Restrictions on properties (type, cardinality) –Characteristics of properties (transitive, …) –Annotations –Individuals Reasoning tasks: classification, consistency checking

45. Example Ontology (Protégé)

46. Resources FaCT++ system (open source) –http://owl.man.ac.uk/factplusplus/http://owl.man.ac.uk/factplusplus/ Protégé –http://protege.stanford.edu/plugins/owl/http://protege.stanford.edu/plugins/owl/ W3C Web-Ontology (WebOnt) working group (OWL) –http://www.w3.org/2001/sw/WebOnt/http://www.w3.org/2001/sw/WebOnt/ DL Handbook, Cambridge University Press –http://books.cambridge.org/0521781760.htmhttp://books.cambridge.org/0521781760.htm

47. Ontology –Introduction –Ontology Components –Ontology Development Process –Ontology Languages Applications Using Ontology for Context Awareness –Contexts Manipulation Using Ontology –AmbieSense Project –Modeling Context Ontology based on Activity Theory Summary & Review Agenda 46

48. Contexts Manipulation Using Ontology An Approach for Configuring Ontology-based Application Context Model –Chung-Seong Hong, Hyun Kim, Hyoung-Sun Kim –Electronics and Telecommunication Research Institute, Republic of Korea Backgrounds –Previous researches mainly focus on the collecting and analyzing context information from the computational devices. –Contexts are managed and interpreted inside of the infrastructure with their own context model. –Applications are created and executed based on the unified context model that is managed in the context-aware infrastructure. Problems –With the unified context model, Is it possible to support all kinds of ubiquitous applications? –What about contexts outside of the context-aware system? Information System - Scheduling Sys., Weather Forecasting Sys., etc. Web Services 47

49. Three Phases of Contexts Manipulation

50. Goals We propose a conceptual modeling approach – focusing on how to configure application context model using ontology through expanding context-aware systems ’ context model – for intelligent services in ubiquitous computing environments. – A new context modeling approach is designed to overcome shortcomings such as –context inference through OWL –context knowledge reuse through context modularization –context knowledge expansion through ontology merging

51. We simplify the application context model as four-layered space based on the abstraction level of contexts. Layered Application Context Model

52. Modeling Common and Domain Ontology

53. Prototype Smart Meeting Room Application

54. Integrated Application Context Ontology

55. Ontology –Introduction –Ontology Components –Ontology Development Process –Ontology Languages Applications Using Ontology for Context Awareness –Contexts Manipulation Using Ontology –AmbieSense Project –Modeling Context Ontology based on Activity Theory Summary & Review Agenda 54

56. AmbieSense Project Case-Based Situation Assessment in a Mobile Context-Aware System –Anders Kofod-Petersen and Agnar Aamodt –Artificial Intelligence in Mobile System, 2003 AmbieSense –A small and wireless context tag –Inside furniture, beside artworks, in a meeting room, in a shop window, or in an open area 55

57. The Developed Domain Context Model Generic concepts –Task, Goal, Action, Physical Object Concepts of the domain in a multi-relational semantic network –Airport Hall, Gate, Restaurant, Newsstand

58. A Hungry User Example Spatio-temporal context –1:15 PM, Oslo airport Personal context –A hang to Italian food –More than five hours since last used his credit card at a restaurant Environmental context –Context Tag nearby

59. Ontology –Introduction –Ontology Components –Ontology Development Process –Ontology Languages Applications Using Ontology for Context Awareness –Contexts Manipulation Using Ontology –AmbieSense Project –Modeling Context Ontology based on Activity Theory Summary & Review Agenda 58

60. Modeling Context based on Activity Theory Using Activity Theory to Model Context Awareness –Anders Kofod-Petersen and Jorg Cassens –Lecture Notes on Artificial Intelligence, vol. 3946, pp. 1-17, 2006. A major shortfall of the research into context-aware systems –Lack of a common understanding of a context model –Lack of an agreed definition of context Most of the research today –Focusing on the technical issues associated with context –The syntactic relationships between different concepts Main reason for this approach –Using socio-technical theories to design context-aware systems –Using Activity Theory to model context and to describe situations

61. Activity Theory Activity theory –A descriptive tool to help understand the unity of consciousness and activity Activity –Subject, object and a mediating artifact or tool –Subject A person or a group engaged in an activity –Object Object that subject wants to achieve

62. Activity Theory and Context Awareness CHAT (Cultural Historical Activity Theory) –An expanded model of Activity Theory A social and cultural context Example : software development –The members of the team → all subjects in the development process –Subjects and the client and other stake-holders → a community –Working → division of labor –Coding standards or working culture → a set of rules –Methods for analysis and programming tools → mediating tools

63. The Proposed Context Model Context (defined by Dey) –The set of suitable environmental states concerning a user Context taxonomy –Environmental context → things, services, people –Personal context → mood, expertise and disabilities –Social context → different roles a user can assume –Task context → what the user is doing, user’s goal, task –Spatio-temporal context → time, location, the community

64. Ontology –Introduction –Ontology Components –Ontology Development Process –Ontology Languages Applications Using Ontology for Context Awareness –Contexts Manipulation Using Ontology –AmbieSense Project –Modeling Context Ontology based on Activity Theory Summary & Review Agenda 63

65. Summary What is ontology? –Introduction of Ontology –Why use ontology? –Components of Ontology –Ontology Development Process How to use ontology? –Applications Using Ontology for Context Awareness –Ontology based contexts manipulation –Ontology based context-aware service –Ontology based context modeling Summary & Review