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1 Berendt: Advanced databases, 1st semester 2012/2013, 1 Advanced databases – Inference on the Semantic Web.

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Presentation on theme: "1 Berendt: Advanced databases, 1st semester 2012/2013, 1 Advanced databases – Inference on the Semantic Web."— Presentation transcript:

1 1 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 1 Advanced databases – Inference on the Semantic Web Bettina Berendt Katholieke Universiteit Leuven, Department of Computer Science http://people.cs.kuleuven.be/~bettina.berendt/teaching/ Last update: 17 October 2012

2 2 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 2 Agenda Introduction / motivation; kinds of reasoning Properties of Properties (cf. the Pizza Tutorial) Class descriptions, cardinality, & value constraints Does this type of knowledge exist in LOD? Common problems in using OWL reasoning

3 3 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 3 Recall: Task from the previous week Given n your knowledge of logic n and the presence of interconnected data sources such as those shown on p. 63: What inferences would you want to draw (that go beyond data retrieval)? Name 3 examples. Be as concrete or as abstract as you want. (Recall: retrieval is just getting the data – a standard SELECT statement with some selections and projections would be a typical example of retrieval in the SQL world Inference is concluding more from this – it starts with operators such as COUNT or MAX in SELECT statements and then goes as far as logic or other ways of data processing take you)

4 4 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 4 Motivation; deductive and inductive reasoning (ex.s) Approach in this lecture: „Data++“ or: understanding OWL as a conceptual modelling tool à la EER or UML class diagrams plus logic n Deductive reasoning l All swans are white. l Tilly is a swan.  Tilly is white. l Truth-preserving! n Inductive reasoning l Tilly and Edda and Edwin and … are swans. l Tilly and Edda and Edwin and … are white.  All swans are white. l „Bringing new knowledge into the world“

5 5 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 5 Agenda Introduction / motivation; kinds of reasoning Properties of Properties (cf. the Pizza Tutorial) Class descriptions, cardinality, & value constraints Does this type of knowledge exist in LOD? Common problems in using OWL reasoning

6 6 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 6 OWL Properties ( Object and/or Datatype ~) can be … n Functional Inverse functional (or: Inverse of another relation) n Transitive n Symmetric n Asymmetric n Reflexive n Irreflexive … and this allows for inferences on individuals (see Pizza Tutorial)

7 7 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 7 Agenda Introduction / motivation; kinds of reasoning Properties of Properties (cf. the Pizza Tutorial) Class descriptions, cardinality, & value constraints Does this type of knowledge exist in LOD? Common problems in using OWL reasoning

8 8 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 8 Please note The language elements shown on the following slides are not available in all of OWL‘s versions (Lite, DL, Full) and/or are restricted in different ways in these versions. Also, OWL2 is a different matter altogether. We concentrate on OWL DL, i.e. the version that Protégé uses. For all OWL snippets shown on the following slides, we‘ll ask: n What does it mean? (Paraphrase in natural language) n What does it imply? (= What will a reasoner infer?) n Please note that the 2 sets of examples have a slightly different syntax, inspired by OWL XML notation or Protégé

9 9 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 9 Class descriptions n Classes can be described/defined as the l UnionOf l complementOf l IntersectionOf other classes, or as the enumeration of instances (via oneOf )

10 10 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 10 Cardinality constraints n 0/1 n ≥ 0 n MinCardinality n MaxCardinality

11 11 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 11 Value constraints n allValuesFrom n someValuesFrom n hasValue

12 12 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 12 Examples: Class inferences (1) Class(a:bus_driver complete intersectionOf(a:person restriction(a:drives someValuesFrom (a:bus)))) Class(a:driver complete intersectionOf(a:person restriction(a:drives someValuesFrom (a:vehicle)))) Class(a:bus partial a:vehicle) Note: a is the namespace used throughout these examples

13 13 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 13 Examples: Class inferences (2) Class(a:cat_owner complete intersectionOf(a:person restriction(a:has_pet someValuesFrom (a:cat)))) SubPropertyOf(a:has_pet a:likes) Class(a:cat_liker complete intersectionOf(a:person restriction(a:likes someValuesFrom (a:cat))))

14 14 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 14 Examples: Class inferences (3) Class(a:driver complete intersectionOf(a:person restriction(a:drives someValuesFrom (a:vehicle)))) Class(a:driver partial a:adult) Class(a:grownup complete intersectionOf(a:adult a:person))

15 15 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 15 Examples: Class inferences (4) Class(a:sheep partial restriction(a:eats allValuesFrom (a:grass)) a:animal) Class(a:grass partial a:plant) DisjointClasses(unionOf(restriction(a:part_of someValuesFrom (a:animal)) a:animal) unionOf(a:plant restriction(a:part_of someValuesFrom (a:plant)))) Class(a:vegetarian complete intersectionOf( restriction(a:eats allValuesFrom (complementOf(restriction(a:part_of someValuesFrom (a:animal))))) restriction(a:eats allValuesFrom (complementOf(a:animal))) a:animal))

16 16 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 16 Examples: Class inferences (5) Class(a:giraffe partial a:animal restriction(a:eats allValuesFrom (a:leaf))) Class(a:leaf partial restriction(a:part_of someValuesFrom (a:tree))) Class(a:tree partial a:plant) DisjointClasses(unionOf(restriction(a:part_of someValuesFrom (a:animal)) a:animal) unionOf(a:plant restriction(a:part_of someValuesFrom (a:plant)))) Class(a:vegetarian complete intersectionOf( restriction(a:eats allValuesFrom (complementOf(restriction(a:part_of someValuesFrom (a:animal))))) restriction(a:eats allValuesFrom (complementOf(a:animal))) a:animal))

17 17 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 17 Examples: Class inferences (6) Class(a:old_lady complete intersectionOf(a:person a:female a:elderly)) Class(a:old_lady partial intersectionOf( restriction(a:has_pet allValuesFrom (a:cat)) restriction(a:has_pet someValuesFrom (a:animal)))) Class(a:cat_owner complete intersectionOf(a:person restriction(a:has_pet someValuesFrom (a:cat))))

18 18 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 18 Examples: Class inferences (7) Class(a:cow partial a:vegetarian) DisjointClasses(unionOf(restriction(a:part_of someValuesFrom (a:animal)) a:animal) unionOf(a:plant restriction(a:part_of someValuesFrom (a:plant)))) Class(a:vegetarian complete intersectionOf( restriction(a:eats allValuesFrom (complementOf(restriction(a:part_of someValuesFrom (a:animal))))) restriction(a:eats allValuesFrom (complementOf(a:animal))) a:animal)) Class(a:mad_cow complete intersectionOf(a:cow restriction(a:eats someValuesFrom (intersectionOf(restriction(a:part_of someValuesFrom (a:sheep)) a:brain))))) Class(a:sheep partial a:animal restriction(a:eats allValuesFrom (a:grass)))

19 19 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 19 Examples: Instance inferences (1) Individual(a:Daily_Mirror type(owl:Thing)) Individual(a:Nick type(a:male) value(a:drives a:Q123_ABC) value(a:reads a:Daily_Mirror)) Individual(a:Q123_ABC type(a:van) type(a:white_thing)) Class(a:white_van_man complete intersectionOf(a:man restriction(a:drives someValuesFrom (intersectionOf(a:van a:white_thing))))) Class(a:white_van_man partial restriction(a:reads allValuesFrom (a:tabloid)))

20 20 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 20 Examples: Instance inferences (2) Individual(a:Spike type(owl:Thing) value(a:is_pet_of a:Pete)) Individual(a:Pete type(owl:Thing)) ObjectProperty(a:has_pet domain(a:person) range(a:animal)) ObjectProperty(a:is_pet_of inverseOf(a:has_pet))

21 21 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 21 Examples: Instance inferences (3) Individual(a:Walt type(a:person) value(a:has_pet a:Huey) value(a:has_pet a:Louie) value(a:has_pet a:Dewey)) Individual(a:Huey type(a:duck)) Individual(a:Dewey type(a:duck)) Individual(a:Louie type(a:duck)) DifferentIndividuals(a:Huey a:Dewey a:Louie) Class(a:animal_lover complete intersectionOf(a:person restriction(a:has_pet minCardinality(3))))

22 22 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 22 Examples: Instance inferences (4) Individual(a:Minnie type(a:female) type(a:elderly) value(a:has_pet a:Tom)) Individual(a:Tom type(owl:Thing)) ObjectProperty(a:has_pet domain(a:person) range(a:animal)) Class(a:old_lady complete intersectionOf(a:person a:female a:elderly)) Class(a:old_lady partial intersectionOf( restriction(a:has_pet allValuesFrom (a:cat)) restriction(a:has_pet someValuesFrom (a:animal))))

23 23 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 23 Agenda Introduction / motivation; kinds of reasoning Properties of Properties (cf. the Pizza Tutorial) Class descriptions, cardinality, & value constraints Does this type of knowledge exist in LOD? Common problems in using OWL reasoning

24 24 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 24 A recent empirical investigation of the occurrence of ontology concepts in LOD (Glimm, Hogan, Krötzsch, Polleres, Proc. LDOW 2012) Note: the y axis is scaled logarithmically

25 25 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 25 (table is longer in the paper)

26 26 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 26 Agenda Introduction / motivation; kinds of reasoning Properties of Properties (cf. the Pizza Tutorial) Class descriptions, cardinality, & value constraints Does this type of knowledge exist in LOD? Common problems in using OWL reasoning

27 27 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 27 Properties and existential restrictions

28 28 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 28 Primitive and defined classes

29 29 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 29 Open world reasoning (1)

30 30 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 30 Open world reasoning (2): Is this a Margherita Pizza?

31 31 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 31 Open world reasoning (3): closure restrictions/axioms

32 32 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 32 Domain and range restrictions are axioms

33 33 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 33 Does „only“ ( allValuesFrom ) imply „some“ ( someValuesFrom )?

34 34 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 34 „and“ and „or“ in logics vs. in natural language

35 35 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 35 „some not …“ vs. „not some …“

36 36 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 36 Task for the next week n Find 3 ontological statements on the Semantic Web, for example using Sindice. n Paraphrase what they mean. n Find 1 statement involving owl:EquivalentClass. What problems are likely to arise when statements made about this class (with its two names) come from different knowledge sources?

37 37 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 37 Outlook Introduction / motivation; kinds of reasoning Properties of Properties (cf. the Pizza Tutorial) Class descriptions, cardinality, & value constraints Does this type of knowledge exist in LOD? Common problems in using OWL reasoning Combining SW data: Schema/ontology matching

38 38 Berendt: Advanced databases, 1st semester 2012/2013, http://www.cs.kuleuven.be/~berendt/teaching/ 38 Used sources p. 8: n OWL specification at l http://www.w3.org/TR/owl-ref/ resp. http://www.w3.org/TR/owl-ref/ l http://www.w3.org/TR/owl2-overview/ http://www.w3.org/TR/owl2-overview/ n Difference between OWL Lite, DL, and Full by Ritesh Agrawal (2007) at http://ragrawal.wordpress.com/2007/02/20/difference-between-owl-lite-dl-and-full/ http://ragrawal.wordpress.com/2007/02/20/difference-between-owl-lite-dl-and-full/ pp. 12-21: Bechhofer, S. (2003). OWL Reasoning Examples. http://owl.man.ac.uk/2003/why/20031203 http://owl.man.ac.uk/2003/why/20031203 pp. 24f: Glimm, B., Hogan, A., Krötzsch, M., & Polleres, A. (2012). OWL: Yet to arrive on the Web of Data? In the Proceedings of the Linked Data on the Web WWW2012 Workshop (LDOW 2012). Available at http://arxiv.org/abs/1202.0984http://arxiv.org/abs/1202.0984 pp. 27-35: Alan L. Rector, Nick Drummond, Matthew Horridge, Jeremy Rogers, Holger Knublauch, Robert Stevens, Hai Wang, Chris Wroe: OWL Pizzas: Practical Experience of Teaching OWL-DL: Common Errors & Common Patterns. EKAW 2004: 63-81. http://www.co-ode.org/resources/papers/ekaw2004.pdf Picture credits: see PPT „comments“ field

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