Dr. Jim Bowring Computer Science Department College of Charleston CSIS 690 (633) May Evening 2009 Semantic Web Principles and Practice Class 3: 18 May 2009

Class 3: Roadmap Announcements and Assignments Questions Info Overload responses FOAF responses SWWO Chapter 2 SWP Chapter 3

Semantic Modeling Models: abstraction --> communication provide explanations provide predictions mediate among viewpoints

Models for Human Communication Humans use informal models rely on context for interpretation legislation adjudication required Web examples: newsgroups, mailing lists FAQs categories of goods, etc. community tagging

Talmudic Model Layering achieved over time by incorporating interpretative sources authoritative sources Such informal models advance the collection of human knowledge aid communication require tuning and layering to resolve

Reasoning Inductive reasoning true premises mean likely true conclusions Analogical reasoning = simple induction specific objects, not general premises Deductive reasoning true premises guarantee true conclusions

Explanation and Prediction Explanation = trace of transformations from first principles via rules to a phenomenon Prediction = explanation Note: reading on semantic provenance Formal model provides objective way to state premises and rules What happens to the layers of an informal model when we model formally? SW: formal model with inferences

Mediating Variability Pluto example: 21st century, post 2006; astrological Solution: hierarchy of classes SSB astro:Planet horo:Planet IAU:Planet

What if … No clear class hierarchy? We merge models with conflicting information?

Expressivity in Models for SW RDF = resource description framework basic modeling for AAA, layering RDFS = RDF Schema language RDFS-Plus = subset of OWL relates properties OWL = Ontology Web Language adds logic for constraints between classes, entities and classes

More Models for SW DAML+OIL DAML+OIL = DARPA Agent Markup Language W3C RIF W3C RIF = Rule Interchange Format RuleML = Rule Markup Initiative R2ML R2ML = REWERSE I1 Rule Markup Language integrates OCL, SWRL, RuleML SWRL SWRL = Semantic Web Rule Language integrates OWL and RuleML

RDF SW uses primitive meaning of semantics = referential : symbols refer to things Resource = thing = entity Resource Description Framework

Distributing Data across Web Relational DBs mix strategies (via global DBMS): local tables local records local projections RDF strategy: local cells ==> global reference to row, col Triples: subject predicate object rowcolcell node directed edgenode

Merging Data from Multiple Sources Each triple is a graph Graphs are merged by equating nodes How? Note: SDOs: Service Data ObjectsService Data Objects Java data abstraction leveraging XML and providing results as directed graphs

Namespaces, URIs, Identity Answer: URIs Uniform Resource Identifiers URIs look just like URLs, but can be dereferenced Confused?

Qnames To make readable in print, use qnames namespace:identifier namespace defined elsewhere URIs use CamelCase Standard namespaces: xsd: XML schema definition xmlns: XML namespaces rdf: rdfs: owl:

RDF Identifiers rdf:type rdf:Property basic typing system for predicates lit:Shakespeare lit:wrote lit:King Lear lit:wrote rdf:type rdf:property

Challenge: RDF and Tabular Data Represent RDB table losslessly as RDF graph: URI for the database each record as tableNameUniqueKey each column as tableName_Attribute ==> triples for each cell Additionally need to express types as membership row rdf:type table How many triples used?

Reification Make statement about another statement Strategy 1: add another attribute (RDB) or triple works well for adding info, but not for qualifying Strategy 2: explicit reification ns:stmtrdf:subject lit:Shakespeare; rdf:predicate lit: wrote; rdf: object lit:Hamlet. Note: we have not asserted underlying triple THEN: web:Wikipedia m:says ns:stmt

Serialization Alternatives N-Triples :. Notation 3 RDF (N3) (Berners-Lee): binds local qnames to global URIs subject predicate object; predicate object; predicate object. subject predicate object1, object2, object3. abbreviations: rdf:type ==> “a” RDF/XML : (for machines)

Blank Nodes and Ordered Info If we know something exists, but not its identity: Use existential qualifier by: [ rdf:type bio:Woman; bio:livedIn geo:England ] Order: lit:Shakespeare b:hasChild (b:c1 b:c1 b:c3).