Agents and Knowledge Interoperability in the Semantic Web Era - PowerPoint PPT Presentation

agents and knowledge interoperability in the semantic web era n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Agents and Knowledge Interoperability in the Semantic Web Era PowerPoint Presentation
Download Presentation
Agents and Knowledge Interoperability in the Semantic Web Era

play fullscreen
1 / 143
Agents and Knowledge Interoperability in the Semantic Web Era
124 Views
Download Presentation
zlata
Download Presentation

Agents and Knowledge Interoperability in the Semantic Web Era

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Tutorial Agents and Knowledge Interoperability in the Semantic Web Era Nick Bassiliades Logic Programming & Intelligent Systems Group Dept. of Informatics Aristotle University of Thessaloniki Greece

  2. Topic of the Tutorial • How Semantic Web affects: • knowledge and information interchange • reasoning interoperability among intelligent agents in multi-agent systems • Parts of the tutorial: • Interaction between semantic web rules and ontologies • Agent’s internal knowledge base • Interoperability between reasoning systems for agents • Examples of actual implemented tools for semantic web reasoning in multi-agents systems. Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  3. Tutorial Overview • RULES AND ONTOLOGIES • Homogeneous approach • Entailment-Based OWL Reasoning • The O-DEVICE System • Hybrid approach • The CLIPS-OWL Framework • The DLE Framework • REASONING INTEROPERABILITY • The EMERALD Framework • Reasoning Services - Reasoners • The KC-AGENTS Prototypes • Use Case: A Brokering Scenario Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  4. Tutorial Overview • RULES AND ONTOLOGIES • Homogeneous approach • Entailment-Based OWL Reasoning • The O-DEVICE System • Hybrid approach • The CLIPS-OWL Framework • The DLE Framework • REASONING INTEROPERABILITY • The EMERALD Framework • Reasoning Services - Reasoners • The KC-AGENTS Prototypes • Use Case: A Brokering Scenario Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  5. Rules and Ontologies in the SWIntroduction (1/3) • Semantic Web (SW): vision of a universal medium for data and knowledge exchange. • Current technologies offer data and knowledge interoperability: metadata – RDF, ontologies - OWL 2 • Research shifts to higher layers: logic and proofs • Critical for agents: infer new knowledge and explain actions • Increase trust in the SW • Researchers focusing mainly on integration of rules and ontologies • OWL2 RL: Intersection of Horn logic and description logics • SWRL: Union of Horn logic and description logics • Standardization of rule representation: RuleML, RIF Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  6. Rules and Ontologies in the SWIntroduction (2/3) • RDF and OWL are subsets of predicate logic • Trade-off between expressive power and computational complexity • Horn logic is another subset of predicate logic with efficient proof systems • a.k.a. rule systems, definite logic programs • Description logics and Horn logic are orthogonal • Both needed in expressing different kinds of knowledge in SW • They use different reasoning engines • Part 1 of tutorial discusses how SW rules and ontologies interact • Agent’s internal KB for environment awareness and decision making • Various ways of interaction, interoperation and integration Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  7. Rules and Ontologies in the SWIntroduction (3/3) • Rules are important for the logic layer of SW • Extensions or alternatives to DL based ontology languages • Can be used to develop declarative systems on top of ontologies • There is a lot of debate about the suitability of Logic Programming (LP) in the domain of the SW • However, problems emerged during the development of practical OWL applications • To overcome these, many research efforts focused on: • the mapping of DLs into LP, or • on the combination of DLs and LP Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  8. Rules and Ontologies in the SWUses • Querying: DL reasoning engines have low ABOX reasoning and querying performance. • Combining DLs with the rule paradigm to state expressive instance queriesprovides increased performance • Non-monotonicity: DLs follow the principle of the open world assumption. • Sometimes it is preferable to introduce non-monotonicity in DLs • e.g. negation as failure • DLs’ expressivity: Rules can extend ontology languages. • Integrity constraints: With rules we can define arbitrary integrity constraints over the ABOX. Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  9. Mapping Ontologies into Rules • Since DLs and Horn Logic are orthogonal, this means that not everything in DL can be expressed using rules • In order to map ontologies into rules, a subset of OWL DL must be found, so that OWL constructs can be mapped to LP • DLP (Description Logic Programs) and the OWL2 RL profile of OWL2 define the intersection of LP and DL • Largest syntactic fragment of OWL DL that is implementable using rules. • Simpler than OWL Lite • Enables interaction between description logics and rules Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  10. OWL 2 RL • OWL 2 is based on Description Logic. • A fragment of first-order logic • Inherits the open-world assumption and non-unique-name assumption of Description Logics • OWL 2 RL is an interesting sublanguage of OWL 2 DL • The largest fragment of OWL 2 on which the choice for CWA and UNA does not matter • OWL 2 RL is weak enough so that the differences between the choices don’t show up. • Still large enough to enable useful representation and reasoning tasks. Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  11. Open-World Assumption (OWA) • We cannot conclude some statement x to be false simply because we cannot show x to be true. • The opposite assumption (closed world, CWA) would allow deriving falsity from the inability to derive truth. • OWL is strictly committed to the OWA • In some applications is not the right choice • Example in favor of OWA • Question: “Did it rain in Tokyo yesterday?” • Answer: “I don’t know that it rained, but that’s not enough reason to conclude that it didn’t rain.” • Example in favor of CWA • Question: “Was there a big earthquake disaster in Tokyo yesterday?” • Answer: “I don’t know, but if there had been such a disaster, I’d have heard about it. Therefore I conclude that there wasn’t such a disaster.” Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  12. Unique-Name Assumption (UNA) • When two individuals are known by different names, they are in fact different individuals. • Sometimes works well and sometimes not • In favor: when two products in a catalog are known by different codes, they are different • Against: two people in our social environment initially known with different identifiers (e.g., “Prof. van Harmelen” and “Frank”) are sometimes the same person • OWL does not make the unique-name assumption • It is possible to explicitly assert of a set of identifiers that they are all unique using owl:allDifferent Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  13. OWL 2 RL Advantages • The semantics can be implemented using rule-based technologies • forward or backward chaining rule engines • The reasoning is performed based on a predefined set of entailment rules • known as OWL 2 RL/RDF rules • Benefits • Scalable reasoning on a quite expressive subset of OWL 2 • Implemented by many state of the art large scale reasoners • Even parallel implementations can be used • Enables the definition of rule-based applications on top of ontologies Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  14. Entailment Rules • Simple if-then rules that assert new triples based on existing ones • condition-triples  conclusion-triples • Conditions contain triple patterns with variables • triple patterns match existing triples (facts) • Conclusions derive new triples • based on the variable bindings in the conditions • Safe Rules: a variable in the conclusion should exist in the condition Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  15. OWL 2 RL constructs • Class and property equivalence • Equality- inequality between individuals • Inverse, transitive, symmetric and functional properties • Intersection of classes • Excluded constructors: • Union, existential quantification, and arbitrary cardinality constraints • In general, it cannot infer new individuals not explicitly present in the KB Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  16. RDF constructs • A triple (a, P, b) is expressed as a fact P(a, b) • Instance declaration type(a, C) • a is an instance of class C • expressed as C(a) • C is a subclass of D: C(X) → D(X) • Similarly for subproperty: P(X,Y) → Q(X,Y) • Domain and Range Restrictions • C is the domain of property P: P(X, Y) → C(X) • C’ is the range of property P: P(X, Y) → C’(Y) Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  17. OWL Constructs • equivalentClass(C, D) / pair of rules • C(X) → D(X) / D(X) → C(X) • Similarly for equivalentProperty(P, Q) • P(X,Y) → Q(X,Y) / Q(X,Y) → P(X,Y) • Transitive Properties: P(X, Y), P(Y, Z) → P(X, Z) • Boolean operators. • The intersection of C1 and C2 is a subclass of D • C1(X), C2(X) → D(X) • C is a subclass of the intersection of D1 and D2 • C(X) → D1(X) C(X) → D2(X) • The union of C1 and C2 is a subclass of D (not opposite) • C1(X) → D(X) / C2(X) → D(X) Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  18. OWL Restrictions • allValuesFrom(P, D) • the anonymous class of all x such that y must be an instance of D whenever P(x, y) • C subClassOfallValuesFrom(P, D) (not opposite) • C(X), P(X, Y) → D(Y) • someValuesFrom(P, D) • the anonymous class of all x for which there exists at least one y instance of D, such that P(x, y). • someValuesFrom(P, D) subClassOf C(not opposite) • P(X, Y), D(Y) → C(X) Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  19. Mapping Ontologies into RulesDisadvantages • The mapping approaches result in languages with restricted expressiveness (semantics) • Combination of DL and LP solve expressivity problem • Homogeneous or hybrid approach Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  20. Tutorial Overview • RULES AND ONTOLOGIES • Homogeneous approach • Entailment-Based OWL Reasoning • The O-DEVICE System • Hybrid approach • The CLIPS-OWL Framework • The DLE Framework • REASONING INTEROPERABILITY • The EMERALD Framework • Reasoning Services - Reasoners • The KC-AGENTS Prototypes • Use Case: A Brokering Scenario Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  21. Mapping Ontologies into RulesHomogeneous approach • Rule and ontology predicates are treated homogeneously, as a new single logic language. • Rules can use: • unary and binary predicates from the ontology (classes and properties) • predicates that occur only in rules (rules predicates) • To maintain decidability, the safety conditionis needed • Restricts variables occurring in the head of a rule to those that occur in at least one positive rule predicate in the body of the rule. Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  22. Mapping Ontologies into RulesHomogeneous approach • The homogeneous approaches can be used either for building rule programs on top of ontologies or ontologies on top of rules. • OWL semantics are mapped into a rule-based formalism that coexist in the KB with rule predicates • A new reasoner is needed, able to handle the new homogeneous language • The mapping approaches can be considered as the first step for building a homogeneous system Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  23. Semantic Web Rule Language (SWRL) • W3C member submission rule language • Extends OWL/OWL 2 DL with first-order rules to overcome the limited expressivity of ontologies • Function-free Horn logic, written in DatalogRuleML • Allows class and property predicates to occur in the head and body of a rule • unrestricted combination of ontologies and rules • follows the OWA and non-UNA • … but it is undecidable • DL-safe rules • apply only on known individuals • have limited expressivity but regain decidability Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  24. Rules in SWRL • B1, …,Bn → A1, …, Am • commas denote conjunction on both sides of the arrow • A1, …, Am, B1, …, Bn can be • C(x), P(x, y), sameAs(x, y), differentFrom(x, y) • C is an OWL class description, P is an OWL property, • x, y are Datalog variables, OWL individuals, or OWL data values. • If the head of a rule has more than one atom the rule can be transformed to an equivalent set of rules with one atom in the head • conjunction of atoms without shared variables • A(X,Y)→ B(X), C(Y) • A(X,Y)→ B(X) , A(X,Y)→ C(Y) Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  25. Complexity of SWRL • Arbitrary OWL expressions (e.g. restrictions), can appear in the head or body of a rule. • This adds significant expressive power to OWL, but at the high price of undecidability • There can be no inference engine that draws exactly the same conclusions as the SWRL semantics. • SWRL vs. OWL 2 RL • OWL 2 RL tries to combine the advantages of both languages in their common sublanguage • SWRL takes a more maximalist approach and unites their respective expressivities. Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  26. Example SWRL Rules • Reclassification • Man(?m) → Person(?m) /subclassOf relation in OWL • Person(?m)  hasSex(?m, male) → Man(?m) • Possible in OWL – hasValue (sufficient) restriction • Not all such reclassifications are possible in OWL • Property Value Assignment • hasParent(?x, ?y) hasBrother(?y, ?z) → hasUncle(?x, ?z) • Property chaining • Possible in OWL 2 - Not possible in OWL 1.0 • Person(?p) hasSibling(?p,?s)  Man(?s) → hasBrother(?p,?s) • Not possible in OWL Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  27. Example SWRL Rules: Built-ins • Built-ins dramatically increase expressivity • most rules are not expressible in OWL 1 • Somebuilt-inscanbeexpressed in OWL 2 • Person(?p) hasAge(?p, ?age) swrlb:greaterThan(?age, 17) → Adult(?p) • Person(?p) hasSalaryInPounds(?p, ?pounds)  swrlb:multiply(?dollars, ?pounds, 2.0) → hasSalaryInDollars(?p, ?dollars) Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  28. Tutorial Overview • RULES AND ONTOLOGIES • Homogeneous approach • Entailment-Based OWL Reasoning • The O-DEVICE System • Hybrid approach • The CLIPS-OWL Framework • The DLE Framework • REASONING INTEROPERABILITY • The EMERALD Framework • Reasoning Services - Reasoners • The KC-AGENTS Prototypes • Use Case: A Brokering Scenario Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  29. Entailment-Based OWL Reasoning • The EBOR paradigm enables the materialization of OWL semantics into the KB of a rule engine using OWL entailment (inference) rules. • The EBOR paradigm is considered as a first step in realizing a homogeneous combination of OWL and rules • Allows to build rule programs on top of ontologies, as well as ontologies on top of rule programs • The rule program coexists with the inference rules • Rule execution is interleaved with the inference procedure Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  30. Architecture EBOR paradigm • The asserted knowledge (ontology), is mapped into an internal rule engine representation format • Inference rules (OWL entailments), expressed in the language of the rule engine, are applied to • deduce new knowledge, or • check consistency of the ontology • Entailment rules have relatively low computational complexity • consistency is in P • entailment is NP-complete • if there are not blank nodes in P Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  31. Entailment Rules • The semantics of the ontology can be (partially) captured using entailment rules • For RDF and RDFS there is a complete set of entailments • http://www.w3.org/TR/rdf-mt • Example: rdfs9 entailment rule (N-Triple notation) • Defines the subsumption characteristic of the rdfs:subClassOf property if <c> <rdfs:subClassOf> <d> . ∧ <x> <rdf:type> <c> . then <x> <rdf:type> <d> . Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  32. pD* Semantics • There is no such a complete set of entailment rules for OWL • The pD* semantics is a weakened variant of OWL Full • Realized by 23 entailment rules and 2 inconsistency rules. • Inspired the OWL 2 RL profile • Example: rdfp4 entailment • Handles the values of transitive properties defined using the owl:TransitiveProperty OWL construct if <p> <rdfs:type> <owl:TransitiveProperty> . ∧ <s> <p> <x> . ∧ <x> <p> <z> . then <s> <p> <z>. P(S, X), P(X, Z) → P(S, Z) Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  33. EBOR System Development Issues • Ontology mapping • Inferencing process • Query support Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  34. EBOR System Development IssuesOntology mapping • An EBOR system should define a mapping procedure of the ontological knowledge into the KB of the rule engine • This mapping is performed over the ontology triples. • The purpose is to generate an internal, rule engine-specific representation of the ontological information where the entailment rules will be applied on. Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  35. EBOR System Development IssuesInferencing process • An EBOR system should implement the desirable number of entailment rules expressed in the engine’s rule language. • This defines the reasoning completeness of the EBOR system • There are implementations of different expressiveness according to the number of implemented entailments Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  36. EBOR System Development IssuesQuery support • An EBOR system should be able to answer queries about the derivations of its KB. • The query infrastructure is implemented with query rules. • Query rules follow either the rule language of the rule engine, or they have a standard-based syntax. Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  37. Approaches for developing EBOR systems • Extended approach (E-EBOR) • An E-EBOR system is built on top of an existing, general purpose rule engine • Augments it with the ability of manipulating ontological information • Needs: • To transform ontology into facts • To populate rule base with the appropriate inference rules • Native approach (N-EBOR) • A N-EBOR system is built from scratch • Draws conclusions directly on the OWL data model • E.g. Jena2, Bossam, OWLIM, BaseVISor Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  38. Advantages and Disadvantages • Reasoning Performance • A N-EBOR system is built directly on the OWL data model and it has increased reasoning performance (speed) • An E-EBOR system does not apply any optimization in handling ontological information • Ontology Utilization • An E-EBOR system utilizes the ontology via rule-based applications • User-defined rules can operate over the inferred knowledge • An N-EBOR system throws away established and efficient rule engines. • User-defined rules are usually hard to be encoded Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  39. Tutorial Overview • RULES AND ONTOLOGIES • Homogeneous approach • Entailment-Based OWL Reasoning • The O-DEVICE System • Hybrid approach • The CLIPS-OWL Framework • The DLE Framework • REASONING INTEROPERABILITY • The EMERALD Framework • Reasoning Services - Reasoners • The KC-AGENTS Prototypes • Use Case: A Brokering Scenario Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  40. The O-DEVICE System • O-DEVICE is an EBOR system • Built on top of the CLIPS production rule engine • It transforms the ontology into the object-oriented model of the COOL language of CLIPS • It applies entailment rules on the generated object-oriented schema • pd* (in OWL 1), OWL 2 RL (in OWL 2) • Entailment rules are implemented as production rules • Conditions match objects, instead of RDF triples • Actions insert or modify objects, instead of deriving axioms Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  41. CLIPS Production Rule Engine(… a few words …) • A RETE-based production rule engine • Utilizes 3 programming paradigms • rule programming, functional programming and object oriented programming • Utilizes 2 data/knowledge modeling paradigms • Fact-based • ordered and non-ordered (deftemplates) facts • rules match facts • Object-Oriented • the KB consists of classes with slots (attributes) and objects • rules match objects Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  42. CLIPS Object-Oriented Language (COOL) • Integrates the production rule paradigm with OO data/knowledge modeling • Knowledge is represented in terms of • Classes (defclass) • slots: single-field or multi-field (multislot) • facets/constraints: describe various features of slots • single/multiple inheritance • Objects (make-instance or definstances) Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  43. Classes and slots: Basic syntax (defclass <name> (is-a <superclass-name>+) (<slot> <constraint>*)* (<multislot> <constraint>*)*) • The name of the class • A lists of its superclasses • Definition of the slots of the class (slot, multislot) • type, allowed-values, allowed-classes, …. Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  44. Class Examples (defclass Human (is-a USER) (slot age (type INTEGER) (range 1 100)) (slot married (type STRING) (allowed-values yes no)) (multislothasParent (type INSTANCE-NAME) (allowed-classes Human))) (defclass Man (is-a Human)) Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  45. Instances: Basic Syntax (make-instance [<name>] of <class> [(<slot-name> <values>)]*) • Every object has a name (inside square brackets) • A single class as the direct class type • Based on the subclass hierarchy, objects inherit also the class types of the superclasses • The objects can use the slots inherited from the superclasses • Values in slots may also be defined Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  46. Instance Example (make-instance [george] of Man (age 28) (married no) (hasParent [Peter] [Mary])) Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  47. Matching Objects in Rules • CLIPS OO rules are defined by: • A left hand side (LHS) that matches object patterns • A right hand side (RHS) that contains actions to execute (possibly on objects) (defrule rule-name (object-pattern-1) (object-pattern-2) ...=> (action-1) (action-2) ...) Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  48. Object Patterns: Basic Syntax <object-pattern> ::= (object <attribute-constraint>*) <attribute-constraint> ::= (is-a <constraint>) | (name <constraint>) | (<slot-name> <constraint>*) • The is-a constraint is used for specifying class constraints • The name constraint is used for specifying a specific object on which to pattern-match • Constraints are also used in slots/multislots in order to restrict certain type of values. • A single-value variable is denoted as ?x • A multivalue variable is denoted as $?x Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  49. Rule Example • “mark checked all adult men“ (defrule test-rule (object (is-a Man) (name ?x) (age ?age) (checked no)) (test (> ?age 17))=> (modify-instance ?x (checked yes))) Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era

  50. O-DEVICE Architecture Nick Bassiliades, Agents and Knowledge Interoperability in the Semantic Web Era