Semantic Web, Metadata, Knowledge Representation, Ontologies

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A general introduction to the semantic web. Major knowledge representation methods: metadata, thesauri, ontologies. Slides for the PhD Course on Semantic Web (http://elite.polito.it/).

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Semantic Web, Metadata, Knowledge Representation, Ontologies

  1. 1. Introduction to the Semantic Web
  2. 2. Semantic Web http://www.w3.org/2001/sw/  Web second generation  Web 3.0  “Conceptual structuring of the Web in an explicit machine-readable way” (Tim Berners-Lee)  In other words… …let the machine do most of the work!!! F. Corno, L. Farinetti - Politecnico di Torino 2
  3. 3. “Official” introduction  The Semantic Web is a web of data. There is lots of data we all use every day, and its not part of the web. I can see my bank statements on the web, and my photographs, and I can see my appointments in a calendar. But can I see my photos in a calendar to see what I was doing when I took them? Can I see bank statement lines in a calendar?  Why not? Because we don‟t have a web of data. Because data is controlled by applications, and each application keeps it to itself F. Corno, L. Farinetti - Politecnico di Torino 3
  4. 4. Example F. Corno, L. Farinetti - Politecnico di Torino 4
  5. 5. “Official” introduction  The Semantic Web is about two things  It is about common formats for integration and combination of data drawn from diverse sources, where on the original Web mainly concentrated on the interchange of documents.  It is also about language for recording how the data relates to real world objects. That allows a person, or a machine, to start off in one database, and then move through an unending set of databases which are connected not by wires but by being about the same thing F. Corno, L. Farinetti - Politecnico di Torino 5
  6. 6. An example …  How can a machine distinguish the meanings … ? “I am a professor of computer science.” “I am a professor of computer science, you may think. Well,…” F. Corno, L. Farinetti - Politecnico di Torino 6
  7. 7. Key principles  The Semantic Web is the Web  Same base technologies, evolutionary  Decentralized (incomplete, inconsistent)  Provide explicit statements regarding web resources  Authors,original information providers  Intermediaries (humans and/or machines)  Information consumers determine consequences of the statements  Distributed „reasoning‟ F. Corno, L. Farinetti - Politecnico di Torino 7
  8. 8. 1989: WWW original proposal F. Corno, L. Farinetti - Politecnico di Torino 8
  9. 9. Technology stack (old: pre-2008) F. Corno, L. Farinetti - Politecnico di Torino 9
  10. 10. Technology stack (current: 2008) F. Corno, L. Farinetti - Politecnico di Torino 10
  11. 11. The real world Not yet...! F. Corno, L. Farinetti - Politecnico di Torino 11
  12. 12. The real world Not Not always yet...! necessary... F. Corno, L. Farinetti - Politecnico di Torino 12
  13. 13. The real world Not Information Not always yet...! retrieval necessary... Statistics F. Corno, L. Farinetti - Politecnico di Torino 13
  14. 14. Current “hot” topics F. Corno, L. Farinetti - Politecnico di Torino 14
  15. 15. Metadata and Metadata Standards
  16. 16. Goal of the semantic Web The Semantic Web will enable machines to COMPREHEND semantic documents and data, NOT human speech and writing  Then, how???  Semantic Web foundation: metadata F. Corno, L. Farinetti - Politecnico di Torino 16
  17. 17. Resource and description  Resource  Content, format, …  Access method dependent on format (I can read it if I “know” its language)  Resource description  Independent of the format (I can read “people‟s comments” about the resource… provided that I know the language in which the comment is written) F. Corno, L. Farinetti - Politecnico di Torino 17
  18. 18. Resource and description this resource is suitable the title of this for PhD students description resource is “Introduction to the Semantic resource Web” this resource the author of was created on this resource April 14th, 2009 is L. Farinetti this resource is related to the quality of computer this resource science, is high, knowledge according to F. representation Corno and metadata F. Corno, L. Farinetti - Politecnico di Torino 18
  19. 19. Resource and description  Resource  Content, format, …  Access method dependent on format (I can read it if I “know” its language)  Standardization (i.e. common language for applications) ???  Practically impossible …  Huge amount of existing information  Hundreds of human languages  Hundreds of computer languages (other word for formats) F. Corno, L. Farinetti - Politecnico di Torino 19
  20. 20. Resource and description  Resource description  Independent of the format (I can read “people‟s comments” about the resource… provided that I know the language in which the comment is written)  Standardization (i.e. common language for applications) ???  Feasible  Smaller amount of information, possibly new  Solution: define a standard language for writing comments (“metadata” in semantic web terminology) F. Corno, L. Farinetti - Politecnico di Torino 20
  21. 21. Resource and description this resource is suitable the title of this for PhD students resource is “Introduction to the Semantic Web” this resource the author of was created on this resource April 14th, 2009 is L. Farinetti Metadata this resource is related to the quality of computer this resource Field name = field value science, is high, knowledge according to F. representation Corno and metadata F. Corno, L. Farinetti - Politecnico di Torino 21
  22. 22. Resource and description Level = PhD students Title = description “Introduction to the Semantic resource Web” Date = Author = 2009-04-14 L. Farinetti Topic = {computer Quality = high science, knowledge representation, Rated by F. Corno metadata} F. Corno, L. Farinetti - Politecnico di Torino 22
  23. 23. Semantic Web main tasks  Metadata annotation  Description of resources using standard languages  Search  Retrieve relevant information according to user‟s query / interest / intention  Use metadata (and possibly content) in a “smart” way (i.e. “reasoning” about the meaning of annotations) F. Corno, L. Farinetti - Politecnico di Torino 23
  24. 24. Meaningful metadata annotations  Common language for describing resources  Resource description standards  Common language for description field names  Metadata standards  Common language for description field values  Metadata standards + controlled vocabularies  Semantically rich descriptions to support search  Knowledge representation techniques, ontologies F. Corno, L. Farinetti - Politecnico di Torino 24
  25. 25. Common language for describing resources F. Corno, L. Farinetti - Politecnico di Torino 25
  26. 26. Common language for describing resources  Resource Description Framework (RDF)  Resource = URI (retrievable, or not)  RDF is structured in statements  A statement is a triple  Subject – predicate – object  Subject: a resource  Predicate: a verb / property / relationship  Object: a resource, or a literal string F. Corno, L. Farinetti - Politecnico di Torino 26
  27. 27. Common language for describing resources Author = L. Farinetti  Diagram: hasAuthor URI L.Farinetti  Simple RDF assertion (triple): triple (hasAuthor, URI, L.Farinetti) F. Corno, L. Farinetti - Politecnico di Torino 27
  28. 28. Common language for describing resources Author = L. Farinetti  RDF in XML syntax: <RDF xmlns=“http://www.w3.org/TR/ … ” > <Description about=“http://www.polito.it/semweb/intro”> <Author>L.Farinetti</Author> </Description> </RDF> F. Corno, L. Farinetti - Politecnico di Torino 28
  29. 29. Common language for field names  Problem Topic = … Topics, Subject, Subjects, Level = … Argument, Arguments Title = ... Educational level, destination, suitability, … Singular / plural Difficult to clearly define concept in a Date = … Author = … few words Date of creation, date of Creator, Maker, last modification, date of Contributor … revision, … Synonymy Different concepts: need for more details F. Corno, L. Farinetti - Politecnico di Torino 29
  30. 30. Common language for field names  Solution: metadata standards  Many standardization bodies are involved  Standards may be general  e.g. Dublin Core (DC)  or may depend on goal, context, domain, …  e. g. educational resources (IEEE LOM), multimedia resources (MPEG-7), images (VRA), people (FOAF, IEEE PAPI), geospatial resources (GSDGM), bibliographical resources (MARC, OAI), cultural heritage resources (CIDOC CRM) F. Corno, L. Farinetti - Politecnico di Torino 30
  31. 31. Metadata standards examples F. Corno, L. Farinetti - Politecnico di Torino 31
  32. 32. Dublin Core  Dublin Core Metadata Element Set (DCMES)  Building blocks to define metadata for the Semantic Web  15 elements, or categories, general enough to describe most of the published resources  Extra elements and element refinements F. Corno, L. Farinetti - Politecnico di Torino 32
  33. 33. DC metadata element set F. Corno, L. Farinetti - Politecnico di Torino 33
  34. 34. Example of description using Dublin Core (in RDF)  A paper in the “Ariadne” journal F. Corno, L. Farinetti - Politecnico di Torino 34
  35. 35. Common language for field values  Problems  Value type Date = 2009-04-14 type = date Title = “Introduction to the Semantic Author = Web” L. Farinetti type = string type = string “standard” format? Laura Farinetti, Farinetti Laura, Farinetti L., … F. Corno, L. Farinetti - Politecnico di Torino 35
  36. 36. Common language for field values  Problems Level = PhD students  Value type any value?  Value restrictions? list of possible values?  freedom vs shared understanding Topic = Quality = high {computer science, High, medium, low? knowledge 1 to 5? representation, any value? metadata} any value? any number of values? F. Corno, L. Farinetti - Politecnico di Torino 36
  37. 37. Common language for field values  Solution: metadata standards + controlled vocabularies  Metadata standards  Only some, and partially  Controlled vocabularies  Explicit list of possible values F. Corno, L. Farinetti - Politecnico di Torino 37
  38. 38. Examples from IEEE LOM  1484.12.1 - 2002 Learning Object Metadata (LOM) Standard  Developed by the IEEE Learning Technology Standards Committee (LTSC)  Standard to describe the “Learning Objects” in order to guarantee their interoperability F. Corno, L. Farinetti - Politecnico di Torino 38
  39. 39. Examples from IEEE LOM F. Corno, L. Farinetti - Politecnico di Torino 39
  40. 40. Examples from IEEE LOM F. Corno, L. Farinetti - Politecnico di Torino 40
  41. 41. Examples from IEEE LOM F. Corno, L. Farinetti - Politecnico di Torino 41
  42. 42. … + controlled vocabularies  A closed list of named subjects, which can be used for classification Topic =  Metadata field values are {computer science, restricted to a list of terms informatics, (selected by experts) knowledge representation, metadata} F. Corno, L. Farinetti - Politecnico di Torino 42
  43. 43. Semantically rich descriptions to support search F. Corno, L. Farinetti - Politecnico di Torino 43
  44. 44. Semantically rich descriptions to support search http://dictybase.org/db/html/help/GO.html Topic = {metabolism, …} F. Corno, L. Farinetti - Politecnico di Torino 44
  45. 45. Knowledge Representation
  46. 46. Need for knowledge representation  Semantically rich descriptions need “understanding” the meaning of a resource and the domain related to the resource  Disambiguation of terms  Shared agreement on meanings  Description of the domain, with concepts and relations among concepts F. Corno, L. Farinetti - Politecnico di Torino 46
  47. 47. Example: Dublin Core metadata Metadata of a single paper F. Corno, L. Farinetti - Politecnico di Torino 47
  48. 48. Problems  Title usually offers good clues, but  it does not necessarily mention all names of all subjects the user is interested in  it may presuppose knowledge the user does not actually possess  Subject is meant to convey precisely what the document is about, but  much depends on how extensive the set of keywords is, whether all related subjects are mentioned, and whether too many subjects are listed  Metadata does not say much about “how related” a resource is to a given subject F. Corno, L. Farinetti - Politecnico di Torino 48
  49. 49. Search results for “topic maps” F. Corno, L. Farinetti - Politecnico di Torino 49
  50. 50. Problems  Authors were free to define their own subject keywords  Results are not “about” topic maps, but “related to” topic maps  If an author forgets to list “topic maps”, his paper will never be found F. Corno, L. Farinetti - Politecnico di Torino 50
  51. 51. Subject-based classification  Any form of content classification that groups objects by their subjects  e.g the use of keywords to classify papers  Metadata fields describe what the objects are about by listing discrete subjects inside a subject-based classification  Important: difference between describing the objects being classified and describing the subjects used to classify them  Metadata describe objects  Subject-based classification is the approach to describe subject F. Corno, L. Farinetti - Politecnico di Torino 51
  52. 52. Subject-based classification ... “On those remote pages it is written that animals are divided into: a. those that belong to the Emperor b. embalmed ones c. those that are trained d. suckling pigs From The Celestial Emporium of Benevolent Knowledge, Borges e. mermaids f. fabulous ones g. stray dogs h. those that are included in this classification i. those that tremble as if they were mad j. innumerable ones k. those drawn with a very fine camel's hair brush l. others m. those that have just broken a flower vase n. those that resemble flies from a distance" F. Corno, L. Farinetti - Politecnico di Torino 52
  53. 53. Subject-based classification techniques  Controlled vocabularies  Taxonomies  Thesauri  Faceted classification  Ontologies  Folksonomies  Others  … Most come from library science F. Corno, L. Farinetti - Politecnico di Torino 53
  54. 54. Controlled vocabulary  A closed list of named subjects, which can be used for classification  Composed of terms: particular name for a particular concept  similar to keywords  Terms are not concepts A single term may be the name of one or more concepts  A single concept may have multiple names  Ambiguity avoided by forbidding duplicate terms F. Corno, L. Farinetti - Politecnico di Torino 54
  55. 55. Topic = Controlled vocabulary {computer science, knowledge representation, mtadata, RDF, topic navigation maps}  Goal topic maps  Prevent authors from defining terms that are meaningless, too broad or too narrow  Prevent authors from misspelling  Prevent different authors from choosing slightly different forms of the same term  The simplest form of controlled vocabulary is a list of terms (or “pick list”) F. Corno, L. Farinetti - Politecnico di Torino 55
  56. 56. Controlled vocabulary  Reduce ambiguity inherent in normal human languages  Solve the problems of homographs, homonyms, synonyms and polysemes by ensuring  That each concept is described using only one authorized term  That each authorized term in the controlled vocabulary describes only one concept F. Corno, L. Farinetti - Politecnico di Torino 56
  57. 57. Problems solved Synonym different words with identical or very similar meanings F. Corno, L. Farinetti - Politecnico di Torino 57
  58. 58. Problems solved “Will you please close that door!” close “The tiger was now so close that I could smell it...” student pupil opening in the iris of the eye ('æk.səz) plural of axe axes ('æk.siz) plural of axis Synonym different words with identical or very similar meanings F. Corno, L. Farinetti - Politecnico di Torino 58
  59. 59. Problems solved take (I'll get the drinks) to get become (she got scared) understand (I get it) a piece of a tree wood a geographical area with many trees Synonym different words with identical or very similar meanings student and pupil (noun) buy and purchase (verb) sick and ill (adjective) F. Corno, L. Farinetti - Politecnico di Torino 59
  60. 60. Controlled vocabulary examples Circuit theory Blood Electronic circuits Cord blood Microwave technology Erythrocyte Electron tubes Leukocyte Semiconductor materials and devices Basophil Dielectric materials and devices Eosynophil Magnetic materials and devices Lymphoblast Superconducting materials and devices Lymphocyte … Monocyte Neutrophil …  Practically no “real” examples  With very little extra effort: taxonomies and thesauri! F. Corno, L. Farinetti - Politecnico di Torino 60
  61. 61. Taxonomy  Subject-based classification that arranges the terms in the controlled vocabulary into a hierarchy  Dates back to Carl Linnæus‟s work on zoological and botanical classification (18th century) F. Corno, L. Farinetti - Politecnico di Torino 61
  62. 62. Taxonomy  Allow related terms to be grouped together  It is clear that “topic maps” and “XTM” are related  Easier to classify documents  Easier to choose search keywords F. Corno, L. Farinetti - Politecnico di Torino 62
  63. 63. Taxonomies and metadata  Metadata are stored as usual with the resource  The “subject” will contain only controlled terms  Controlled terms belong to a hierarchy, shared by all papers F. Corno, L. Farinetti - Politecnico di Torino 63
  64. 64. Taxonomy example: INSPEC http://www.theiet.org/publishing/inspec/index.cfm F. Corno, L. Farinetti - Politecnico di Torino 64
  65. 65. Taxonomy example: INSPEC F. Corno, L. Farinetti - Politecnico di Torino 65
  66. 66. INSPEC journal article database F. Corno, L. Farinetti - Politecnico di Torino 66
  67. 67. Taxonomy example: anatomy terms http://www.cbil.upenn.edu/anatomy.php3 F. Corno, L. Farinetti - Politecnico di Torino 67
  68. 68. Taxonomy example F. Corno, L. Farinetti - Politecnico di Torino 68
  69. 69. Taxonomy example http://www.acm.org/class/1998/ccs98.html F. Corno, L. Farinetti - Politecnico di Torino 69
  70. 70. Taxonomy limits  Only two kinds of relationships between terms  Parent = broader term  Child = narrower term no more in use synonym topic navigation maps difference? synonym XML topic map difference? F. Corno, L. Farinetti - Politecnico di Torino 70
  71. 71. Thesaurus  Extends taxonomies  subjects are arranged in a hierarchy  Other statements can be made about the subjects  Two ISO standards  ISO2788 for monolingual thesauri  ISO5964 for multilingual thesauri F. Corno, L. Farinetti - Politecnico di Torino 71
  72. 72. Thesaurus relationships  BT – broader term  Refers to a term with wider or less specific meaning  Some systems allow multiple BTs for one term, while others do not  Inverse property: NT - narrower term  A taxonomy only uses BT and NT  SN – scope note  Stringexplaining its meaning within the thesaurus  Useful when the precise meaning of the term is not obvious from context F. Corno, L. Farinetti - Politecnico di Torino 72
  73. 73. Thesaurus relationships  USE  Another term that is to be preferred instead of this term  Implies that the terms are synonymous  Inverse property: UF  TT – top term  The topmost ancestor of this term  The BT of the BT of the BT...  RT – related term A term that is related to this term, without being a synonym of it or a broader/narrower term F. Corno, L. Farinetti - Politecnico di Torino 73
  74. 74. Thesaurus example http://www.ukat.org.uk/thesaurus/ F. Corno, L. Farinetti - Politecnico di Torino 74
  75. 75. Thesaurus example http://www.swinburne.edu.au/corporate/registrar/rms/keywords.htm F. Corno, L. Farinetti - Politecnico di Torino 75
  76. 76. Thesaurus example  Library of Congress Subject Heading http://www.loc.gov/cds/lcsh.html F. Corno, L. Farinetti - Politecnico di Torino 76
  77. 77. W3C standard: SKOS F. Corno, L. Farinetti - Politecnico di Torino 77
  78. 78. Faceted classification  Proposed by S.R. Ranganathan in the „30s  Facets are the different axes along which documents can be classified  Each facet contains a number of terms  Usually with a thesaurus organization  Usually a term belongs to one facet only  A document is classified by selecting one term from each facet F. Corno, L. Farinetti - Politecnico di Torino 78
  79. 79. Faceted classification example http://flamenco.berkeley.edu/ F. Corno, L. Farinetti - Politecnico di Torino 79
  80. 80. Advantages  Multi- dimensionality  Persistence  Scalability  Flexibility http://freeable.polito.it/ F. Corno, L. Farinetti - Politecnico di Torino 80
  81. 81. Ontology  Model for describing the world that consists of a set of types, properties, and relationships  Extends the other subject-based classification approaches  Has open vocabularies  Has open relationship types (not just BT/NT, RT and USE/UF) F. Corno, L. Farinetti - Politecnico di Torino 81
  82. 82. Ontology structure  Concepts  Relationships Is-a Other  Instances F. Corno, L. Farinetti - Politecnico di Torino 82
  83. 83. Folksonomy  Internet-mediated social environments  Tags compiled through social tagging  Social tagging  Decentralized practice where individuals and groups create, manage and share tags to annotate digital resources in an online social environment  Generally characterized by non-standard tagging F. Corno, L. Farinetti - Politecnico di Torino 83
  84. 84. Example (flickr - del.icio.us) Knowledge Management - Intro 84
  85. 85. Other subject-based techniques  Synonym rings  Connect together a set of terms as being equivalent for search purpose  Similar to UF/USE relationship of thesauri, but no preferred term F. Corno, L. Farinetti - Politecnico di Torino 85
  86. 86. Other subject-based techniques  Authority file  Similar to a synonym ring, but consists of UF/USE relationships instead of synonym relationships  One term in each synonym ring is indicated as the preferred term for that subject  e.g. Library of Congress Name Authority File F. Corno, L. Farinetti - Politecnico di Torino 86
  87. 87. Subject-based classification summary Terminology is rarely used in a consistent way Controlled vocabularies are thesauri, thesauri are ontologies, … http://www.iesr.ac.uk/profile/vocabs/index.html/#CtrldVocabsList F. Corno, L. Farinetti - Politecnico di Torino 87
  88. 88. Subject-based classification summary F. Corno, L. Farinetti - Politecnico di Torino 88
  89. 89. Ontologies
  90. 90. Semantically rich descriptions to support search http://dictybase.org/db/html/help/GO.html Topic = {metabolism, …} F. Corno, L. Farinetti - Politecnico di Torino 90
  91. 91. Ontologies  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 F. Corno, L. Farinetti - Politecnico di Torino 91
  92. 92. “Ontology engineering”  Defining terms in the domain and relations among them  defining concepts in the domain (classes)  arranging the concepts in a hierarchy (subclass-superclass hierarchy)  defining which attributes and properties (slots) classes can have and constraints on their values  defining individuals and filling in slot values F. Corno, L. Farinetti - Politecnico di Torino 92
  93. 93. Why develop an ontology?  To share common understanding of the structure of information  among people  among software agents  To enable reuse of domain knowledge  to avoid “re-inventing the wheel”  to introduce standards to allow interoperability F. Corno, L. Farinetti - Politecnico di Torino 93
  94. 94. An ontology takes Certificate 1 year Is_equivalent_to Is_a takes Is_a HNC Award Is_a takes Is_a HND 2 years Diploma takes F. Corno, L. Farinetti - Politecnico di Torino 94
  95. 95. A more complex ontology [base.Entity] Person Worker Faculty Professor AssistantProfessor AssociateProfessor FullProfessor VisitingProfessor Lecturer PostDoc Assistant ResearchAssistant TeachingAssistant AdministrativeStaff Director Chair {Professor} Dean {Professor} ClericalStaff SystemsStaff Student UndergraduateStudent GraduateStudent F. Corno, L. Farinetti - Politecnico di Torino 95
  96. 96. A more complex ontology Organization Department School University Program ResearchGroup Institute Publication Article TechnicalReport JournalArticle ConferencePaper UnofficialPublication Book Software Manual Specification Work Course Research Schedule F. Corno, L. Farinetti - Politecnico di Torino 96
  97. 97. A more complex ontology Relation Argument 1 Argument 2 ====================================================== publicationAuthor Publication Person publicationDate Publication .DATE publicationResearch Publication Research softwareVersion Software .STRING softwareDocumentation Software Publication teacherOf Faculty Course teachingAssistantOf TeachingAssistant Course takesCourse Student Course age Person .NUMBER emailAddress Person .STRING head Organization Person undergraduateDegreeFrom Person University mastersDegreeFrom Person University doctoralDegreeFrom Person University advisor Student Professor subOrganization Organization Organization ……….. F. Corno, L. Farinetti - Politecnico di Torino 97
  98. 98. Example of ontology engineering chair F. Corno, L. Farinetti - Politecnico di Torino 98
  99. 99. Example of ontology engineering 1.A piece of furniture consisting of a seat, legs, back, and often arms, designed to accommodate one person. 2.A seat of office, authority, or dignity, such as that of a bishop. a.An office or position of authority, such as a professorship. b.A person who holds an office or a position of authority, such as one who presides over a meeting or administers a department of instruction at a college; a chairperson. 3.The position of a player in an orchestra. 4.Slang. The electric chair. 5.A seat carried about on poles; a sedan chair. 6.Any of several devices that serve to support or secure, such as a metal block that supports and holds railroad track in position. chair F. Corno, L. Farinetti - Politecnico di Torino 99
  100. 100. Example of ontology engineering A piece of furniture consisting of a seat, legs, back, and often arms, designed to accommodate one person. chair F. Corno, L. Farinetti - Politecnico di Torino 100
  101. 101. Example of ontology engineering chair seat stool bench F. Corno, L. Farinetti - Politecnico di Torino 101
  102. 102. Example of ontology engineering Something I can sit on ??? chair seat stool bench F. Corno, L. Farinetti - Politecnico di Torino 102
  103. 103. Example of ontology engineering Something I can sit on “sittable” chair seat stool bench F. Corno, L. Farinetti - Politecnico di Torino 103
  104. 104. Example of ontology engineering Something I can sit on “sittable” table chair seat stool bench F. Corno, L. Farinetti - Politecnico di Torino 104
  105. 105. Example of ontology engineering Something I can sit on “sittable” Something designed for sitting “for_sitting” table chair seat stool bench F. Corno, L. Farinetti - Politecnico di Torino 105
  106. 106. Ontology structure “sittable” “for_sitting” table chair seat stool bench F. Corno, L. Farinetti - Politecnico di Torino 106
  107. 107. Concepts Synthetic title Furniture to sit on “sittable” Definition Shorthand name Some piece of furniture that can be used to sit on, either by design or by its shape. F. Corno, L. Farinetti - Politecnico di Torino 107
  108. 108. Internationalization Synthetic title Furniture to sit on Furniture to sit on Furniture to sit on Furniture to sit on Furniture to sit on Furniture to sit on Furniture to sit on “sittable” Definition Shorthand name Some piece of furniture that can Some piece of furniture that can Some piece of furniture that can beSome topieceoffurniture that can used pieceon, furniture sit of beSome topieceofeither by that can used piece on, furniture that can beSome tosit on, either by that can used tosit on,either by beSome its of furniture design usedtosit shape. beused by its on,either by designor by tosit shape. beused tosit on,either by designor by its shape. be used sit on,either by designor by its shape. either by designor by its shape. or by its shape. design or by its shape. design or F. Corno, L. Farinetti - Politecnico di Torino 108
  109. 109. Relationships material room is_a is_a is_a “sittable” wood classroom is_a dining room “for_sitting” is_a is_a table is_a is_a is_a chair seat stool bench F. Corno, L. Farinetti - Politecnico di Torino 109
  110. 110. Relationships made_of material room is_a is_a is_a “sittable” wood classroom is_a made_of dining room “for_sitting” is_a is_a table is_a is_a is_a chair seat stool bench F. Corno, L. Farinetti - Politecnico di Torino 110
  111. 111. Ontology building blocks  Ontologies generally describe:  Individuals  the basic or “ground level” objects  Classes  sets, collections, or types of objects  Attributes  properties, features, characteristics, or parameters that objects can have and share  Relationships  ways that objects can be related to one another F. Corno, L. Farinetti - Politecnico di Torino 111
  112. 112. Individuals  Also known as “instances”  can be concrete objects  animals  molecules  trees  or abstract objects  numbers  words F. Corno, L. Farinetti - Politecnico di Torino 112
  113. 113. Concepts  Also known as “Classes”  abstract groups, sets, or collections of objects  They may contain  individuals  otherclasses  a combination of both  Examples  Person: the class of all people  Vehicle: the class of all vehicles F. Corno, L. Farinetti - Politecnico di Torino 113
  114. 114. Concepts  Can be defined extensionally …  By defining every object that falls under the definition of the concept  A class C is extensionally defined if and only if for every class C', if C' has exactly the same members of C, C and C' are identical  E.g.: DayOfWeek = {Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday}  … or intensionally  By defining the necessary and sufficient conditions for belonging to the concept  E.g.: “bachelor” is an “unmarried man” F. Corno, L. Farinetti - Politecnico di Torino 114
  115. 115. Concepts  Defined by  Name: any identifier, usually carefully chosen  Definition: describes the well agreed meaning of the concept, in a human readable form  Terms (Lexicon): list of terms (synonyms, etc.) usually adopted to identify the concept F. Corno, L. Farinetti - Politecnico di Torino 115
  116. 116. Subsumption  A concept (class) can subsume / be subsumed by any other class  Subsumption is used to establish class hierarchies F. Corno, L. Farinetti - Politecnico di Torino 116
  117. 117. Class partition  A set of related classes and associated rules that allow objects to be placed into the appropriate class GEOMETRIC FIGURE GEOMETRIC TWO POINT DIMENSIONAL FIGURE ONE DIMENSIONAL FIGURE F. Corno, L. Farinetti - Politecnico di Torino 117
  118. 118. Class partition  Disjoint partition A disjoint partition rule guarantees that a single instance of a class cannot be in more than one sub-classes VEHICLE  E.g. one specific truck cannot be in both 4-axle and TRUCK CAR 6-axle classes 6-AXLE 4-AXLE F. Corno, L. Farinetti - Politecnico di Torino 118
  119. 119. Class partition  Exhaustive partition  every concrete object in the super-class is an instance of at least one of the partition classes F. Corno, L. Farinetti - Politecnico di Torino 119
  120. 120. Attributes  Describe specific features  Can be complex (e.g.: list of values)  Defined for a class/concept (e.g. car)  Examples:  number-of-doors: 4  number-of-wheels: 4  engine: {3.0L,4.0L} F. Corno, L. Farinetti - Politecnico di Torino 120
  121. 121. Relationships  Attributes that relate two or more concepts  two concepts → binary relationship  three concepts → ternary relationship  Domain  the concept(s) from which the relationship departs  Range  the concept(s) to which the relationship applies F. Corno, L. Farinetti - Politecnico di Torino 121
  122. 122. Relationships  Examples  Car(MiniMinor) → individual definition  Car(Mini) → individual definition  Successor(Mini,MiniMinor) → relationship domain range F. Corno, L. Farinetti - Politecnico di Torino 122
  123. 123. Commonly used relationships  Subsumption  the most important  is-superclass-of  usually denoted by its inverse is-a (is-subclass-of)  Meronymy  is-part-of  describes how object are combined together to form composite objects F. Corno, L. Farinetti - Politecnico di Torino 123
  124. 124. Example http://www.yeastgenome.org/help/GO.html F. Corno, L. Farinetti - Politecnico di Torino 124
  125. 125. Ontology alignment http://www.webology.ir/2006/v3n3/a28.html F. Corno, L. Farinetti - Politecnico di Torino 125
  126. 126. License  This work is licensed under the Creative Commons Attribution-Noncommercial- Share Alike 3.0 Unported License.  To view a copy of this license, visit http://creativecommons.org/licenses/by- nc-sa/3.0/ or send a letter to Creative Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA. F. Corno, L. Farinetti - Politecnico di Torino 126

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