Using RDF in Agent-Mediated Knowledge Architectures K. Hui, S. Chalmers, P.M.D. Gray & A.D. Preece University of Aberdeen U.K Part of AKT(Advanced Knowledge Technology) Consortium supported by EPSRC

Outline 4 RDFS - Schema for Semantic Web 4 - Metaschema extended to hold FOL Constraints 4 Use of RDF in AM Knowledge Architectures –KRAFT: information integration & fusion –CONOISE: virtual organisations with Autonomy subject to Constraints 4 Conclusions

What we believe 4 Representing knowledge make sense only if it is used in reasoning by machines 4 More direct use of RDF in knowledge architectures –RDFS makes RDF usable within a semantic data model as in Edutella (Risch et al) –extra semantic layers can be built above RDF using built-in extensibility of RDFS –Agent langs should use RDF(S) for content

Pros & Cons of RDF 4 Pros –Tree -> DAG –XML Serialis’n –Extensible by RDFS –stable –cross-platform –good Java support (Jena parser, FrodoViz) –uniform representation (data & meta-data) 4 Cons –Simple –lack of DL expressiveness –wordy (for humans)

ApplicationsRDFtriples RDF(S) Triple Representation 4 RDF triples: –subject-predicate-object –Jena tool creates as Java objects –We can map triples to Prolog terms –almost canonical form –easy to add on extra triples (easier than graph arcs) Prolog terms Java objects

Case Study 1 - Capturing Knowledge in KRAFT 4 Fuses mobile constraints for Configuration problem 4 CSP solving by Sicstus/Eclipse solver 4 Knowledge to capture: –domain model (schema) –data instances choices (solution space & results) –quantified constraints (CoLan/CIF) requirement, restrictions

Capturing Data Instances & Domain Model 4 Domain Model –map P/FDM schema into RDFS web-enabling the schema losing some knowledge –e.g. cardinality, key –can be added to metadata layers ( cif:entmet ) 4 Data Instances –make use of domain model defined in RDFS

Domain Model Example <rdfs:subClassOf rdf:resource= " Resource"/> <rdfs:subClassOf rdf:resource= " Resource"/> declare os ->> entity... declare pc ->> entity declare memory(pc) -> integer declare has_os(pc) -> os...

Constraint Examples in CoLan constrain each p in pc to have size(has_os(p)) =< size(has_disk(p)) constrain each p in pc some s in slots(pc) has sltype(s)=“USB” constrain each t in tutor such that astatus(t) = “research” each st in advisees(t) has grade(st) > 60

Constraints in RDF 4 Constraint language definition in RDFS –a richer semantics cleanly layered on top of RDF –contains classes of meta-objects (e.g. cif:entmet, cif:propmet ) like meta-relns for relational DB –other metaclasses capture parse tree of Constraint 4 Advantages –a clear layering, no change of RDF(S) –constraint become self-contained URI cross-ref to domain model (in RDFS) –constraints become resources

Constraints in RDF constraint on domain X in RDF constraint on the “PC config” domain in RDF RDF Schema of the “PC config” domain RDF Schema of domain X RDF Schema of the CIF language RDFS RDF

Constraint Example in RDF uevar1

Knowledge-directed Mapping mapping engine CIF constraint in Prolog CIF def. in RDFS CIF specific mapping rules CIF in RDF

Using RDF-Encoded Knowledge (Continued) 4 Domain-aware constraint fusion –constraint inheritance a constraint that applies to objects of a class also applies to objects of its subclasses need knowledge on the class hierarchy –an RDF constraint contains pointers to its domain model in RDFS look for rdfs:subClassOf triples

Case Study 2 - Conoise 4 formation of virtual organisations by autonomous agents 4 based on the BDI model –desires represented as constraints (CIF/RDF) 4 agents built using JADE –content language in CIF/RDF –use Jena to parse & manipulate CIF/RDF store & queried as Java objects

BDI Agents using RDF knowledge 4 A CONOISE agent has to combine knowledge from different sources. RDF(S) provides a common basis for doing this. 4 It exercises its Autonomy by planning Intentions in order to meet its various Desires acquired from different sources as (RDFS Constraints). 4 It resolves conflicting desires through a Constraint Solver. 4 The Solver’s domain knowledge is held as Beliefs read in as RDF facts.

Conclusions 4 FOL Constraints, referring to Data items defined in an RDFS Ontology, can themselves be captured in RDFS 4 FIPA Agent langs should use RDF(S) for content –stability (W3C standard and XML serialis’n) –portability (esp. through Java) –capability to store DAG of various object types –rich content:domain model+instances+FOL constraints –extensibility by building extra layer(s) on top