1. Foundations of the Semantic Web Rocky Dunlap College of Computing Georgia Tech Advisors: Spencer Rugaber Leo Mark

2. Things we can talk about Earth System Curator Including a short demo Semantic Web......versus today’s Web...theoretical foundations...application areas Implications for LogicBlox How could SW technologies be useful for LogicBlox?

3. The short story on Curator Problem statement Model-generated datasets in the climate community lack metadata that is required for understanding and analyzing the dataset Premise The descriptors used for comprehensively specifying a model configuration are also needed for a scientifically useful description of the model output data Solution Develop a standardized metadata formalism for describing climate datasets that is based on the model configuration used to generate the dataset

4. The Google Way Keyword search... Good for expert users who are familiar with the datasets But... Many details of what went into the dataset left out Not machine-readable (e.g., by analysis tools) Limited standardization Not semantic or conceptual in nature 10 lines of metadata Run script: 1500+ lines plus input files

5. NASA GEOS-5 Model http://gmao.gsfc.nasa.gov/systems/geos5/

6. Demo http://cdp.ucar.edu:28080/query/queryESC.htm

7. Demo

8. Beyond Curator Curator is a representative project E-science communities are emerging in many areas of science Prediction Scientific data management -- an emerging area that will continue to grow

9. Today’s Web Primarily intended for human consumption Lots of content, but very little about what the content means One relationship: the hyperlink No semantics really HTML is almost completely about presentation

10. HTML Rocky’s Pizzeria Where you make your own pizza pie! Crust options: Deep Pan Thin and Crispy Toppings: Mozzarella Anchovies....

11. (Non-semantic) Hyperlinks Home Page ? ?

12. The Semantic Web Not a new web A layer on top of the existing web Web-based content should be annotated with explicit semantics Intended primarily for machine- consumption, i.e., “intelligent agents” Markup for describing meaning of content, not just presentation

13. Giving meaning to content Rocky’s Pizzeria Where you make your own pizza pie! 404-123-4567 123 Peachtree Street Deep Pan Thin and Crispy Mozzarella Anchovies

14. Semantic Links hasReview hasPhotoAlbum hasNewVersion

15. SW Application Areas Semantic interoperability and data integration health care, drug industry, e-science Semantic search Can we outsmart Google? Specific areas: Geographic Information Systems B2B Mediation Legal Automobile diagnostics and repair http://www.w3.org/2001/sw/sweo/public/UseCases/

16. SW Technology Stack HTTP, URIs Internet XML, XML Schema RDF(S)/OWL Rules Reliable transport Hypertext transfer Structured data transfer Conceptual descriptions TechnologyWhat it provides Advanced reasoning

17. SW Ontology Languages Resource Description Framework (RDF) 1 Provides the ability to make statements (propositions) about resources on the web Web Ontology Language (OWL) 2 More expressive features than RDF Strong theoretical basis on Description Logics

18. RDF Statements “The Curator meeting is at GFDL.” Curator meeting GFDL hasLocation subjectpredicateobject

19. RDF Statements “The Curator meeting is Oct 18-19.” Curator meeting GFDL “18 Oct 2007” “19 Oct 2007” hasLocation starts ends resource literal

20. RDF Statements “Balaji works at GFDL.” Curator meeting GFDL “18 Oct 2007” “19 Oct 2007” Balaji hasLocation worksAt starts ends

21. RDF XML Representation 18 Oct 2007 19 Oct 2007

22. RDF Schema Define a domain specific data model for RDF Includes classes and properties (along with subclasses and subproperties) Properties are first class (they are not defined as part of a particular class)

23. RDF Schema Classes Properties Event MeetingFlight Person hasLocation domain: Event range: Place starts domain: Event range: date Place ends domain: Event range: date worksAt domain: Person range: Place

24. OWL (Web Ontology Language) Motivations Expressive ontology language Precise semantics Understanding of formal properties such as decidability and complexity of inferencing Ease of use for a wide audience Influences DAML+OIL (existing language) Description Logics Frames paradigm

25. Description Logics “A formal language for representing knowledge and reasoning about it.” 3 “Description” Primary facilities are concept and role descriptions “Logics” Equipped with a FOL-based formal semantics Inference procedures reveal implied knowledge

26. DL Knowledge Base TBox Contains the terminology, i.e., the vocabulary of an application domain, i.e., concepts and roles Think database schema ABox Contains the assertions about named individuals Think database data

27. In the TBox Two symbols atomic concepts (A, B) atomic roles (R) Complex descriptions through concept constructors (C, D) role constructors Concept and role constructors determine: The expressiveness of the DL Decidability/complexity of reasoning

28. C  A |(atomic concept) | (universal concept) | (bottom concept) A |(atomic negation) C D |(intersection) R.C |(value restriction) R.(limited existential quant.) DL Language AL A E Grammar for concept construction

29. DL Language AL Atomic concepts: Food, Pizza, CheeseTopping Atomic role: hasTopping “Non-pizza foods” Food Pizza “Pizza with only cheese toppings” Pizza hasTopping.CheeseTopping “Pizza with some topping” Pizza hasTopping. A E

30. NameSyntax Symbol UnionC D U Existential quant. R.C E Unqual. cardinality restriction≥ n R, ≤ n R, = n R N Qual. cardinality restriction≥ n R.C, ≤ n R.C, = n R.C Q Role hierarchyR  S H Inverse propertiesR - I Functional properties F Nominals (enumeration) O DL Concept/Role Constructors E

31. Pizza(x) y.hasTopping(x,y)  CheeseTopping(y) Pizza(x) y.hasTopping(x,y) Pizza(x) y.hasTopping(x,y)  CheeseTopping(y) Pizza hasTopping.CheeseTopping Food(x) Pizza(x) Food Pizza FOL Representations A A Pizza hasTopping. E E

32. In the ABox Assertions about individuals Pizza(p1) Pizza(p2) CheeseTopping(mozzarella) CheeseTopping(goatCheese) hasTopping(p1, mozzerella) hasTopping(p1, goatCheese) hasTopping(p2, goatCheese)

33. Reasoning on the KB TBox Concept satisfiability Are there possible instances? Concept subsumption Is concept C almost more general than D? Concept equivalence ABox Instance checking Is individual X an instance of concept C? Consistency checking Is individual X a possible model of the TBox?

34. From DL to OWL OWL has three “species” or dialects OWL-DL equivalent to DL SHOIN(D) OWL-Lite equivalent to DL SHIF(D) OWL-Full -- messy... Must adhere to constraints of the Web Standard XML syntax URIs for identifying concepts, roles, and individuals Concrete datatypes based on XML Schema Protégé Demo 4

35. Adding Rules to OWL Semantic Web Rule Language (SWRL) 5 hasParent(?x,?y) hasBrother(?y,?z)  hasUncle(?x,?z) Artist(?x) artistStyle(?x, ?y) Style(?y) creator(?z,?x)  style/period(?z, ?y)

36. Semantic Web Apps DBPedia Convert Wikipedia articles into RDF Freebase Wikipedia + semantic links Twine Social networking + semantic web GeoNames Ontology of geospatial semantic information WordNet Ontology RDF/OWL representation of WordNet

37. Discussion Implications for LogicBlox?

38. References [1] Manola, et. al. RDF Primer. http://www.w3.org/TR/rdf-primer/http://www.w3.org/TR/rdf-primer/ [2] McGuiness, et al. OWL Web Ontology Language Overview. http://www.w3.org/TR/owl-features/ http://www.w3.org/TR/owl-features/ [3] Baader, et. al. The Description Logic Handbook, 2007. [4]Horridge, at. al. A Practical Guide To Building OWL Ontologies Using the Protégé-OWL Plugin and CO-ODE Tools, Edition 1.0., 2004. http://www.co- ode.org/resources/tutorials/ProtegeOWLTutorial.pdfhttp://www.co- ode.org/resources/tutorials/ProtegeOWLTutorial.pdf [5]Horrocks, et. al. SWRL: A Semantic Web Rule Language Combining OWL and RuleML. http://www.w3.org/Submission/SWRL/ http://www.w3.org/Submission/SWRL/

39. AE