Eswcsummerschool2010 ontologies final
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Eswcsummerschool2010 ontologies final Presentation Transcript

  • 1. Vocabulary building (and alignment)Elena Simperlelena.simperl@kit.edu www.kit.edu
  • 2. A LITTLE HISTORY KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)2
  • 3. ontology vocabulary microformat conceptual graph topic map thesaurus schema classification object model semantic network glossary taxonomy KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)3
  • 4. Focus on knowledge representation and reasoning Academic topic Research prototypes of ontology-based * Standardization KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)4
  • 5. Focus on data integration, community-driven initiative on data publishing Community of developers and data and content providers Leveraging maturing semantic technologies, and other trends (open access) KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)5
  • 6. It was never a simple matter What exists? What is? What am I? KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)6 Ontologies and the Semantic Web / Ontologies - A Brief History - 6
  • 7. And we’re back to where it all started Greek etymology (ontos = of being; logia = science, study, theory) Parmenides of Elea, ancient Greek philosopher, early 5th century BC “For never shall this prevail, that things that are not are” Parmenides made the ontological argument against nothingness, essentially denying the possible existence of a void. KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)7 Ontologies and the Semantic Web / Ontologies - A Brief History - 7
  • 8. Closer to our time Jacob Lorhard, German philosopher (1561 - 1609) First occurrence of the word Ontology (lat. Ontologia) and the first published ontology in 1607Translation from: Historical and conceptual foundations of diagrammatical ontology. P. Øhrstøm, S. Uckelman; H. Schärfe KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)8
  • 9. Ontologies (or whatever you call them) in Computer Science An ontology defines Application areas • Concepts Natural language processing • Relationships • Any other distinctions that are relevant to Artificial intelligence capture and model knowledge from a Digital libraries domain of interest Software engineering Ontologies are used to Database design Share a common understanding about a domain among people or machines Enable reuse of domain knowledge This is achieved by Agree on meaning and representation of domain knowledge Make domain assumptions explicit Separate domain knowledge from the operational knowledge KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)9
  • 10. Agree on meaning and representation (define-class Travel (?travel) "A journey from place to place" :axiom-def ( .... ) :iff-def (and (arrivalDate ?travel Date) (departureDate ?travel Date)) :def (and (singleFare ?travel Number) (companyName ?travel String))) KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)10
  • 11. Make domain assumptions explicit KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)11
  • 12. Separate domain and operational knowledge KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)12
  • 13. ONTOLOGIES AND SEMANTIC TECHNOLOGIES KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)13
  • 14. Semantic technologies revisited Data is self-describing Data items are inter-connected Applications can derive new knowledge from existing data Advantages Scalable interoperability Enhanced information management Flexible application engineering (if you have proficient developers) KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)14
  • 15. Semantic technologies at BestBuy Goal: “to provide more visibility to products, services and locations to humans and machines” Search engines identify the data more easily and put it into context (30% increase in search traffic) Improved consumer experience Due to “Increasing product and service visibility through front-end semantic web” by Jan Myers, SemTech 2010 KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)15
  • 16. Semantic technologies at BestBuy Data is marked-up using RDFa and refers to concepts from a pre-defined eCommerce ontology. Markup is entered by BestBuy staff via online forms that produce RDFa. KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)16
  • 17. Semantic technologies in life sciences Medical terminologies reflect a common agreement on the types of things people talk about in medical science, and their properties and relationships. Ontologies provide a specification of these conceptual models using formal languages. Advantages: As a standardized vocabulary: facilitate communication Interoperability: standardization of data exchange formats, automatized integration, interlinking Enhanced information management: biological objects annotated using the ontology; improved navigation and filtering. KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)17
  • 18. Features of an ontology Models knowledge about a specific domain Reflects the shared understanding of a group of stakeholders about that domain Defines A common vocabulary The meaning of terms How terms are interrelated Consists of Conceptualization and implementation Contains Ontological primitives: classes, instances, properties, axioms/constraints… KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)18
  • 19. Classifications of ontologies Lassila O, McGuiness D. The Role of Frame-Based Representation on the Semantic Web. Technical Report. Knowledge Systems Laboratory. Stanford University. KSL-01-02. 2001. KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)19
  • 20. Classifications of ontologies (2) Issue of the conceptualization Upper-level/Top-level Core Domain Task Application Representation Degree of formality Highly informal: in natural language Semi-informal: in a restricted and structured form of natural language Semi-formal: in an artificial and formally defined language Rigorously formal: in a language with formal semantics, theorems and proofs of such properties as soundness and completeness KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)20
  • 21. Languages for building ontologies Ontologies can be built using various languages with various degrees of formality Natural language UML ER OWL/RDFS WSML FOL ... The formalism and the language have an influence on the kind of knowledge that can be represented, and inferred A conceptual model is not necessarily a formal ontology only because it is written in OWL KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)21
  • 22. Are ontologies just UML? Ontologies vs ER schemas Semantic Web ontologies represented in Web-compatible languages, use Web technologies They represent a shared view over a domain Ontologies vs UML diagrams Formal semantics of ontology languages defined, languages with feasible computational complexity available Ontologies vs thesauri Formal semantics, domain-specific relationships Ontologies vs taxonomies Richer property types, formal semantics of the is-a relationship KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)22
  • 23. Did Linked Data kill ontologies? KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)23
  • 24. Ontologies in the age of Linked Data Publication according to Linked Data principles Trade-off between acceptance/ease-of-use and expressivity/usefulness Human vs machine-oriented consumption (using specific technologies) Stronger commitment to reuse instead of development from scratch Model pre-defined through the (semi-) structure of the data to be published Emphasis on alignment, especially at the instance level KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)24
  • 25. ONTOLOGY ENGINEERING HOW TO BUILD A VOCABULARY KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)25
  • 26. Methodologies CommonKADS [Schreiber et al., 1999] Enterprise Ontology [Uschold & King, 1995] Holsapple&Joshi IDEF5 [Holsapple & Joshi, 2002] [Benjamin et al. 1994] CO4 [Euzenat, 1995] KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)26
  • 27. Methodologies related to Knowledge Management systems The On-To-Knowledge methodology takes a pragmatic approach to ontology Go / Sufficient Meets No Go? requirements requirements Roll-out? Changes? engineering and contains many useful tips to support non-experts to build ? ? an ontology. Common ORSD + Target Evaluated Evolved KADS Semi-formal ontology ontology ontology Worksheets ontology Human description Issues Refine- Evalu- Application Knowledge Feasibility Kickoff & Management study ment ation Evolution Application Software Identify .. 5. Capture 7. Refine semi- 10. Technology- 13. Apply 1. Problems & requirements formal ontology focussed ontology Engineering opportunities specification in description evaluation 14. Manage 2. Focus of KM ORSD 8. Formalize into 11. User- evolution and application 6. Create semi- target ontology focussed maintenance 3. (OTK-) Tools formal ontology 9. Create evaluation description Prototype 12. Ontology- 4. People focussed evaluation Ontology Development KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH) Source: Sure, 2003.27
  • 28. Methodologies related to Software Engineering Ontology Management METHONTOLOGY contains the most comprehensive description of Scheduling, controlling, quality assurance ontology engineering activities. study targeted at ontology engineers. Feasibility It is Problems, opportunities, potential solutions, economic feasibility Knowledge acquisition Knowledge acquisition Domain analysis motivating scenarios, competency questions, existing solutions Ontology reuse Ontology reuse Documentation Documentation Evaluation Evaluation Conceptualization conceptualization of the model, integration and extension of existing solutions Implementation implementation of the formal model in a representation language Maintenance adaptation of the ontology according to new requirements Use ontology based search, integration, negotiation Source: METHONTOLOGY, Gómez-Pérez, A. ,1996. KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)28
  • 29. Collaborative methodologies 2. Local Adaptation 1. Central O1 3. Central Build Analysis 5. Local Update OI O-User 1 Ontology … User Domain Ontology Board Expert Engineer On O-User n Knowledge Engineer 4. Central Revision Source: DILIGENT: Tempich, 2006. KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)29
  • 30. Newer approaches Ontology engineering increasingly becomes an community activity. Employing Wikis in Tagging is a very ontology engineering Usage of games with a successful approach to enables easy purpose to motivate organize all sorts of participation of the humans to undertake content on the Web. community and lowers complex activities in the Tags often describe the barriers of entry for ontology life cycle. meaning of the tagged non-experts. Less suitable for content in one term. So far less suitable for developing anything Approaches to derive developing complex, that is not on a formal ontologies from highly axiomatized mainstream topic tag clouds are ontologies. emerging. KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)30
  • 31. Condensed version Documentation Test (Evaluation) Knowledge acquisition Requirements analysis motivating scenarios, use cases, existing solutions, effort estimation, competency questions, application requirements Glossary creation (Conceptualization) conceptualization of the model, integration and extension of existing solutions Modeling (Implementation) implementation of the formal model in a representation language KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)31
  • 32. Issues to be considered What is the ontology going to be used for? Who will use the ontology? How it will be maintained and by whom? What kind of data items will refer to it? And how will these references be created and maintained? Are there any information sources available that could be reused? To answer these questions, talk to domain experts, users, and software designers. Domain experts don‘t need to be technical, they need to know about the domain, and help you understand its subtleties Users teach you about the terminology that is actually used and the information needs they have. Software designers tell you tell you about the type of use cases you need to handle, including the data to be described via the ontology KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)32
  • 33. „Design for a world where Google is your Example: BBC homepage, Wikipedia is your CMS, and humans, software developers and machines are your users“ Various micro-sites built and maintained manually No integration across sites in terms of content and metadata Use cases Find and explore content on specific (and related) topics Maintain and re-organize sites Leverage external resources Ontology: One page per thing, reusing DBpedia and MusicBrainz IDs, different labels…http://www.slideshare.net/reduxd/beyond-the-polar-bear KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)33
  • 34. Please stop building new ontologies… REUSING EXISTING KNOWLEDGE KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)34
  • 35. Where to find ontologies Swoogle: over 10 000 documents, across domains http://swoogle.umbc.edu/ Protégé Ontologies: several hundreds of ontologies, across domains http://protegewiki.stanford.edu/index.php/Protege_Ontology_Library#OWL_ontologies Open Ontology Repository: work in progress, life sciences, but also other domains http://ontolog.cim3.net/cgi-bin/wiki.pl?OpenOntologyRepository Tones: 218 ontologies, life sciences and core ontologies. http://owl.cs.manchester.ac.uk/repository/browser Watson: several tens of thousands of documents, across domains http://watson.kmi.open.ac.uk/Overview.html Talis repository http://schemacache.test.talis.com/Schemas/ Ontology Yellow Pages: around 100 ontologies, across domains http://wg.sti2.org/semtech-onto/index.php/The_Ontology_Yellow_Pages OBO Foundation Ontologies http://www.obofoundry.org/ AIM@SHAPE http://dsw.aimatshape.net/tutorials/ont-intro.jsp VoCamps http://vocamp.org/wiki/Main_Page KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)35
  • 36. Swoogle functionality KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)36
  • 37. Swoogle coverage KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)37
  • 38. Protégé ontology library KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)38
  • 39. Open ontology repository Presentation: http://ontolog.cim3.net/file/work/OOR/OOR_presentations_publications/OO R-SemTech_Jun2010.pdf Demo: http://oor-01.cim3.net/search KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)39
  • 40. OBO Foundation ontologies KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)40
  • 41. More resources http://vocamp.org/wiki/Where_to_find_vocabularies KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)41
  • 42. How to select the right ontology What will the ontology be used for? Does it need a natural language interface and if yes in which language? Do you have any knowledge representation constraints (language, reasoning)? What level of expressivity is required? What level of granularity is required? What will you reuse from it? Vocabulary++ How will you reuse it? Imports: transitive dependency between ontologies Changes in imported ontologies can result in inconsistencies and changes of meanings and interpretations, as well as computational aspects. KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)42
  • 43. How to select the right ontology (2) The FOAF level: Use the simple ones, especially if they are used by others as well FOAF, DC, Freebase schemas… The upper-level: Use upper-level ontologies, they are typically the result of extensive discussions and considerations and allow you to ground your more specific ontologies Other knowledge structures: Use taxonomies, vocabularies and folksonomies as a baseline, but encode using Semantic Web languages (Make your results available to the community) Ontology learning: Apply existing tools to create a baseline structure, then revise and enrich KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)43
  • 44. WordNet http://www.w3.org/TR/wordnet-rdf/ KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)44
  • 45. Freebase KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)45
  • 46. Freebase (ii) Schemas: concepts/types, properties and instances, similar to ontologies. KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)46
  • 47. DBpedia Extract structured information from Wikipedia and to make this information available on the Web 2.9 million things, 282,000 persons, 339,000 places (including 241,000 populated places), 88,000 music albums, 44,000 films, 15,000 video games, 119,000 organizations (including 20,000 companies and 29,000 educational institutions), 130,000 species, 4400 diseases Ontology backbone 259 classes arranged in a subsumption hierarchy with altogether 1200 properties Overview of all classes at http://mappings.dbpedia.org/server/ontology/classes Infobox-to-ontology and the table-to-ontology mappings KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)47
  • 48. GoodRelations KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)48
  • 49. Other approaches Create RDF data from existing resources http://simile.mit.edu/wiki/RDFizers http://esw.w3.org/ConverterToRdf Schema mappings have to be configured manually. Some issues to be considered Open vs closed world assumption Semantics of the is-a relationship Expressivity: n-ary relatioships, attributes of relatotionships… Enrich folksonomies: ambiguities, spelling variants and errors, abbreviations, multilinguality… KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)49
  • 50. Ontology engineering today Various domains and application scenarios: life sciences, eCommerce, Linked Open Data Engineering by reuse for most domains based on existing data and vocabularies Alignment of data sets Data curation Human-aided computation (e.g., games, crowdsourcing) Most of them much simpler and easier to understand than the often cited examples from the 90s However, still difficult to use (e.g., for mark-up) KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)50
  • 51. Ontology engineering today (2) Back to the BBC example KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)51
  • 52. Ontology engineering today (3) Management Development oriented Support Pre-development Knowledge acquisition Scheduling Environment study Feasibility study Development Evaluation Integration Specification Conceptualization Control Documentation Merging Formalization Implementation Post-development Quality assurance Configuration Alignment Maintenance Use management KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)52
  • 53. Open topics Meanwhile we have a better understanding of the scenarios which benefit from the usage of semantics and the technologies they typically deploy. Guidelines and how-to‘s Design principles and patterns Schema-level alignment (data-driven) Vocabulary evolution Assessment and evaluation Large-scale approaches to knowledge elicitation based on combinations of human and computational intelligence. KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)53
  • 54. Modeling hands-on www.kit.edu
  • 55. Design principles Abstraction Ignoring certain aspects in order to simplify the handling of something or to better understand other aspects The modeler decides what it is important or not and then chooses a representation that is more tractable than the original A representation of something cannot be greater than that something Models should be divisible Model modules should be highly cohesive and have low coupling Use informative labels 55 KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)55
  • 56. The very basics constraint relationship Some important thing Other important thing The node is a non-trivial thing, easy to find in the domain, with a technological equivalent, with high cohesion and low coupling Candidates for nodes:  things or entities in ER models, knowledge bases  classes in OO models  types states in state machine diagrams etc Relationships/associations/relations/properties/attributes hold between instances of the entities. Constraints/axioms/restrictions/rules further specify the nature of relationships. KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)56
  • 57. Classes A class represents a set of instances A class should be highly cohesive, precisely nameable, relevant A class should have a strong identity Crime Suspect KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)57
  • 58. How to find classes Typical candidates: NOUNS Actors of use cases do not necessarily correspond to classes Other terms as well Verbs An association which starts to take on attributes and associations of its own turns into an entity: „Officer arrests suspect“ Events: „Being ill“  „Illness episode“ Passive form: re-formulate in active form No pronouns 58 KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)58
  • 59. Cohesion and identity A class should represent one thing, all of that thing and nothing but that thing You can prove cohesion by Giving the class a representative name Noun (+ adjective, sometimes however also captured as attribute value) Blackmail victim, robbery victim Blue car, red car Cars is not cohesive Avoid ambiguous terms Manager, handler, processor, list, information, item, data… Identity ~ individuality: classes change values, but are still the same entity Child/Adult: age 59 KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)59
  • 60. Relevance Goint out too far vs. going down too far Investigate homonyms and synonims Can medicine and drug be considered synonims? Do they have the same properties/characteristics/attributes/relationships? Do they have a critical mass of commonalities? 60 KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)60
  • 61. Characterizing classes Two types of principal characteristics Measurable properties: attributes Inter-entity connections: relationships, associations Arrest details as attribute of the suspect vs. Arrest as a class vs Arrest as a relationship Do we measure degrees of arrestedness or do we want to be able to distinguish between arrests? Color of an image as attribute vs. class A „pointing finger“ rather than a „ruler“ indicates identity 61 KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)61
  • 62. Attributes An attribute is a measurable property of a class Scalar values: choice from a range of possibilities An attribute is NOT a data structure. It is not complicated to measure Value of attributes: integer, real numbers, enumerations, text… Witness Attributes do NOT exhibit identity name:text age: integer eyesight: Attributes should have precise representative names enum{…} 62 KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)62
  • 63. How to find attributes Nouns in „-ness“ Velocity-ness, job-ness, arrested-ness… „How much, how many“ test. If you evaluate this, then it is probably an attribute If you enumerate these, it is probably a class Range of attributes Age abstracted as an integer Latitude and longitude: real numbers/NSEW Names abstracted as text KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)63
  • 64. Relationships Crime Suspect 1 copycat Some instances Crime of a class hold a * relationship with some instances 0..1 0..* of another class. Person Vehicle * * Crime Officer investigates 64 KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)64
  • 65. How to find relationships Verbs, verbal phrases and things that could have been verbs. „The butler murdered the duchess“ Properties reflexivity, cardinality, functional, inverse-functional, discountinuous multiplicity, many-to-many, all values from, some values of, transitivity, symmetry etc. Roles Nouns, adjectives. Verbs: indication of time‘s passing. Short-term, one-to-one associations should be named with present participles. Longer-term, one-to-many associations should be named with past participles, or with the simple present third-person singular. 65 KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)65
  • 66. Examples * * Crime Officer investigated * * Crime Officer investigating is investigated Crime * * Officer investigating KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)66
  • 67. Is-a hierarchy Top-down, bottom-up, middle-out Are all instances of entity A also instances of entity B? Are all A‘s also B‘s? Roles Difference between classifications, associations, and aggregations KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)67
  • 68. Examples Bill MealOrder Dish Menu Bed Mattress Diary Appointment Crime CrimeScene KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)68
  • 69. Overloading subsumption Instantiation Thing vs model Composition Is-a vs part-of Constitution Thing vs what matter is it made ofExamples due to Chris Welty, IBM Research KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)69
  • 70. Assignment: Modeling “San Francisco Opera is the second largest opera company in North America. Gaetano Merola and Kurt Herbert Adler were the Company’s first two general directors. Merola led the Company from its founding in 1923 until his death in 1953; Adler was in charge from 1953 through 1981. Legendary for both their conducting and managerial skills, the two leaders established a formidable institution that is internationally recognized as one of the top opera companies in the world—heralded for its first-rate productions and roster of international opera stars. Following Adler’s tenure, the Company was headed by three visionary leaders: Terence A. McEwen (1982–1988), Lotfi Mansouri (1988–2001), and Pamela Rosenberg (2001–2005). Originally presented over two weeks, the Company’s season now contains approximately seventy-five performances of ten operas between September and July. San Francisco Opera celebrated the 75th anniversary of its performing home, the War Memorial Opera House, in 2007 . The venerable beaux arts building was inaugurated on October 15, 1932 and holds the distinction of being the first American opera house that was not built by and for a small group of wealthy patrons; the funding came thanks to a group of private citizens who encouraged thousands of San Franciscans to subscribe. The War Memorial currently welcomes some 500,000 patrons annually.” KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)70
  • 71. Assignment: Encoding in OWL From http://www.jfsowa.com/ontology/ KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)71
  • 72. Assignment: Alignment The aim is to reach a ‚shared conceptualization‘ of all participants at the ESWC2011 summer school on the ontology developed in the previous assigment. Assumption: every group is committed to their conceptualization. Procedure: each group selects a representative, representatives agree on an editor, and on the actual steps to be followed. KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)72