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Ontology modelling and the semantic web

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Presentation from Digital Documents lecture at HiOA 2012-10-23

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Ontology modelling and the semantic web

  1. 1. Ontology modelling and the semantic web Asgeir Rekkavik Deichmanske bibliotek
  2. 2. What does the word semantic mean? • Semantics: The branch of linguistics concerned with meaning. (Shorter Oxford English dictionary) • Semantics is the study of meaning. (Wikipedia 2013-10-16)
  3. 3. I love you I♥ U Different syntax, same semantics
  4. 4. What does ontology mean? • Ontology: The science or study of being. (Shorter Oxford English dictionary) • In computer science and information science, an ontology formally represents knowledge as a set of concepts within a domain, and the relationships between those concepts. (Wikipedia 2013-10-16)
  5. 5. What does ontology mean? • The world can be described in many different ways: e.g. language, art etc. • An ontology describes the world in a way that is formal, structured and unambiguous. • Why? Because we want to describe it to computers.
  6. 6. Ann and Becky are sisters Ann and Becky are mothers
  7. 7. Taxonomies
  8. 8. Taxonomies • Hierarchical classification • Characteristics • • • • Generic relations (’is-a’ relations) Directed graph Nodes represent categories Arrows represent broader/narrower relations • Especially known from biology. Developed by Carl von Linné.
  9. 9. Taxonomies
  10. 10. Transitive relations • If A is related to B and B is related to C, then A is related to C • Examples: • If Ann is younger than Bob and Bob is younger than Carl, then Ann is younger than Carl • If a wolf is a mammal and a mammal is an animal, then a wolf is an animal.
  11. 11. Transitive relations • Other transitive relations can exist between concepts, e.g. ’part-of’ relations
  12. 12. Different types of relations • Generic (’is-a’, e.g. Cat - Animal) • Partitive (’part-of’, e.g. Oslo - Norway) • Instance (e.g. Socrates - Philospher) • Equivalence (e.g. Dove – Pigeon) • Associative (’the rest’)
  13. 13. Thesaurus • Concepts are represented by terms • Certain types of relations between concepts are formalized: • Generic, partitive and instance relations are all formalized as ’broader / narrower’ • Equivalence relations are formalized as ’use% / use for ’ • Some associative relations are formalized as ’see also:’
  14. 14. Thesaurus hierarchy
  15. 15. Thesaurus • Solar systems NT: Planets • Planets BT: Solar systems NT: Gas giants • Gas giants BT: Planets NT: Jupiter • Jupiter BT: Gas giants
  16. 16. Protégé • Free, open source ontology editor • Developed by Stanford University and the University of Manchester • Available from: http://protege.stanford.edu
  17. 17. Ontology – key concepts • Classes • Instances (individuals) • Properties
  18. 18. Classes • Represent categories, sets of individual instances • Are related to eachother through parentchild relationships (superclass-subclass) • Only generic ’is a’-relations are allowed • Unlike in a taxonomy, multiple inheritence is allowed.
  19. 19. Generic class hierarchy
  20. 20. Generic class hierarchy
  21. 21. Properties of classes • Classes can be: • Disjoint (if n is a member of A, n is not a member of B) (e.g. if Robin is a girl, then Robin is not a boy) • Equivalent (if n is a member of A, n is also a member of B and if n is a member of B, n is also a member of A) (e.g. Firstgraders ≡ Pupils born in 2007)
  22. 22. Exercise Create a taxonomy with these classes: • • • • • • • • • • Bicycle Boat Bulldog Car Cat Colour Dog Dolphin Flower Man • • • • • • • • • • Oak Person Pet Pinetree Plant Puppy Rose Whale Woman Zebra
  23. 23. Instances • Individual entities that can populate any number of classes. • An instance that is a member of a class, is necessarily also a member of all its superclasses.
  24. 24. Exercise Create these instances:
  25. 25. The semantic triple • A semantic triple is a statement consisting of three parts: • an instance (subject) • a property that refers to that instance (predicate) • a value for that property (object) George likes chocolate s p o
  26. 26. Properties • The instances are described through properties. • There are two different types of properties: • Object property: • Takes another instance as value • e.g. Alice knows Fred • Datatype property • Takes a distinct datatype value, like a number, a string etc. • e.g. King Harald has year of birth 1937 • The property is the ”predicate” in the semantic triple.
  27. 27. Domain and Range • The domain and range of a property determine what kind of instances it can be used for and what kind of values it can have. • Domain • The class, whose instances can have the property • If domain is not set, domain=Thing • Range • The class, whose instances can be value for an object property • The type of data that is allowed as value for a datatype property
  28. 28. Properties of properties • Properties can be: • symmetric (Martin has cousin Thomas) ⇔ (Thomas has cousin Martin) • asymmetric (Martin is father of Rosie) ⇒ (Rosie can not be father of Martin) • inverse (Martin is parent of Rosie) ⇔ (Rosie is child of Martin) • transitive (Rosie descends from Martin) and (Martin descends from Emma) ⇒ (Rosie descends from Emma) • functional (can have only one value) • inverse functional (value can be held by only one instance) • reflexive (instance takes itself as a value)
  29. 29. Exercise Object properties • Create the following object properties • owns • ownedBy • hasNeighbour • Set domain and range • Connect instances, so that: • Mr. Taylor owns Duchess • Mrs. Robertson owns Lassie • Mr. Taylor and Mrs. Robertson are neighbours
  30. 30. Restrictions • Classes can be populated according to rules called restrictions. • This is done by expressing that a class is equivalent to a certain set of instances. • The set can be defined by • combining other classes with and/or/not operators • using criteria based on desired properties for the instances
  31. 31. Restrictions • Add new class LivingThing • Use class expression editor to express equivalence relation: LivingThing ≡ Animal or Plant or Person
  32. 32. • • • • Add the class Gender Add the individuals Male and Female Add the property hasGender, domain: LivingThing Express that: • • • • • • Lassie is female Duchess is female Moby Dick is male Mr. Taylor is male Mrs. Robertson is female Thomas O’Malley is male
  33. 33. • Add classes FemaleBeing and MaleBeing • Use class expression editor to express equivalence relations: FemaleBeing ≡ MaleBeing ≡ Pet ≡ hasGender value Female hasGender value Male Animal and ownedBy some Person
  34. 34. What about this? WildAnimal ≡ Animal and not (ownedBy some Person)
  35. 35. Open world assumption • The truth-value of an assumption does not depend on whether it is known or not • The absence of a statement therefore does not count as a negation of that statement
  36. 36. • Statements: • Mary is a woman • George is a man • Mary is an American citizen • Question: • Is George an American citizen? • Answers • Closed world assumption: • Open world assumption: "No" "Unknown"
  37. 37. Example ontologies • Dublin Core metadata terms http://purl.org/dc/terms/ • Bibo (Bibliographic ontology) http://purl.org/ontology/bibo/ • Core FRBR http://purl.org/spar/frbr/ • FOAF (Friend of a friend) http://xmlns.com/foaf/spec/

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