Read:

• Reasoning on the Web
• OWL vs. RDF, DL, DL rules, DLP
• Rules on the Semantic Web

Reasoning on the Web - summary:

• queries
  • SPARQL (see RDF lab)
• DL reasoning
  • more powerful than querying, DL reasoning tasks
  • example reasoner: http://clarkparsia.com/pellet/, demo: http://pellet.owldl.com/owlsight/
• rules over ontologies
  • RuleML, RIF - rule interchange formats
  • SWRL Semantic Web Rule Language based on OWL
• OWL 2 RL - new version of OWL, additional constructs for expressing rules in the ontology

• Analyze the reasoning examples in OWL DL
• Use the Manchester Pizza Finder to see the results of implementing your ontology from the previous lab
• Browse sample ontologies with OwlSight (using Pellet as a DL reasoner)
  • Find all inconsistencies of the example ontologies.
  • Read the explanations of the inconsistencies - how could one repair the ontologies?

In this exercise we will use Protege editor with the SWRLTab plugin.

Depending on the configurations, Pellet/Jess may be used to execute SWRL rules.

1. Read about SWRLTab in Protege
2. Download family ontology with rules.
3. Open it in Protege (run protege3), in SWRLTab analyze the rules. Click on J button to enable Jess rule engine. Run the rules iteratively. Check the inferred axioms.

1. Open Protege editor and enable SWRL Tab (Project→Configure in Protege 3.4.4)
2. Implement an example expert system - an expert system for recognizing animals:
   1. define Classes used in the system (look for isa predicate): Mammal, Bird, Carnivore, etc.
   2. define characteristics of the animals (There Is More Than One Way To Do It!)
      • as Properties, e.g.: has
      • as Classes, e.g. Swims
   3. Make assertions about individuals, e.g. has(dennis,hair). parent(dennis,diana). Swims(denzel).
   4. Define rules of the system
   5. Express the initial data as appropriate assertions
   6. Run Pellet/Jess to execute the rules. Observe the inferred facts. Execute the rule engine iteratively, till no more information can be concluded.
   7. extra task: add more rules to the system, use built-in SWRL predicates.

rule id1: 
  [1: has(X,hair)]
  ==>
  [assert(isa(X,mammal)),
   retract(all)].

has(?x,„hair”) → Mammal(?x)

rule id4: 
  [1: flies(X),
   2: lays_eggs(X)]
  ==>
  [assert(isa(X,bird)),
   retract(all)].

Flies(?x) ∧ Lays_eggs(?x) → Bird(?x)

initial_data([
	has(dennis,hair),
	has(dennis,hoofs),
	has(dennis,black_stripes),
	parent(dennis,diana)
	]).

Read: OWL 2 and Rules slides

Control questions:

• How to express rules using owl:subClass relationships?
• what is a property chain axiom?

1. Run Protege, version 4: protege4
2. Create a new ontology in Protege
3. Model the family ontology with rules using OWL 2 constructs (See OWL2 tutorials)
4. extra task: if there is enough time, model the animals expert system in OWL 2 RL

Further reading:

• Logics for the Semantic Web - FOL, DL, LP & F-logic

Test cases for OWL2 (incl.OWL2RL):

• http://www.w3.org/2007/OWL/wiki/Test_Suite_Status

Z braku lepszego miejsca tutaj studenci wpisują komentarze natury ogólnej do tego lab.

Za dużo materiału do przerobienia. Gdy wybiła godzina końca zajęć, ja dotarłem do Family Rules (podpunkt 3), czytając wcześniej jedynie Reasoning Examples in OWL DL (co zajęło ponad 45 minut).