1. ESIP Semantic Web Products and Services ‘triples’ “tutorial” aka sausage making ESIP SW Cluster, Jan 4-5 2011 http://wiki.esipfed.org/index.php/Testb ed

2. Why and What Wed PM Session 1 – Introduction – 10 mins – Peter – Use case – motivation and scope – 20 mins - Chris – Model – based on use case, start with domain model and proceed to ontology model and then show them how to engineer it (i.e. a piece of it) – 1 hr – Peter and Rob Wed PM Session 2 – Model ctd – 20 mins – Peter and Rob – Instance generation and population (how to? Write turtle, use Protégé, …), triple store ingest, web form – demo – 1 hr – Peter – Summary and evaluation – 10 mins –Hook

3. What and how Thu AM Session 3 – Intro to these sessions – 10 min - Peter – Application definition – decompose use case, review technology and dependencies, examine requirements for search and report, add/ correct instances, technology dependencies – 1 hr 10 mins – Peter and TBD – Evaluation –10 mins - Hook Thu AM Session 4 – Application/query development – generate SPARQL, follow along, provide a web query form, cut paste query into a textbox and execute and get result, for visual or text traversal, web form in wiki and first class citizen values (URI) - 1hr – Hook – Evaluation –10 mins - Hook Items that would lead into intermediate (next) tutorial

4. What you may need Web browser Text editor Cmap? http://www.ihmc.us/groups/coe/http://www.ihmc.us/groups/coe/ Protégé? http://protege.stanford.edu/ and http://protegewiki.stanford.edu/http://protege.stanford.edu/ http://protegewiki.stanford.edu/

6. 6 Semantic Web Layers http://www.w3.org/2003/Talks/1023-iswc-tbl/slide26-0.html, http://flickr.com/photos/pshab/291147522/

7. 7 Ontology Spectrum Catalog/ ID Selected Logical Constraints (disjointness, inverse, …) Terms/ glossary Thesauri “narrower term” relation Formal is-a Frames (properties) Informal is-a Formal instance Value Restrs. General Logical constraints Originally from AAAI 1999- Ontologies Panel by Gruninger, Lehmann, McGuinness, Uschold, Welty; – updated by McGuinness. Description in: www.ksl.stanford.edu/people/dlm/papers/ontologies-come-of-age-abstract.html

8. 8 Use Case example Plot the neutral temperature from the Millstone-Hill Fabry Perot, operating in the non-vertical mode during January 2000 as a time series. Objects: –Neutral temperature is a (temperature is a) parameter –Millstone Hill is a (ground-based observatory is a) observatory –Fabry-Perot is a interferometer is a optical instrument is a instrument –Non-vertical mode is a instrument operating mode –January 2000 is a date-time range –Time is a independent variable/ coordinate –Time series is a data plot is a data product

9. 9 Elements of KR in Semantic Web Declarative Knowledge Statements as triples: {subject-predicate-object} interferometer is-a optical instrument Fabry-Perot is-a interferometer Optical instrument has focal length Optical instrument is-a instrument Instrument has instrument operating mode Instrument has measured parameter Instrument operating mode has measured parameter NeutralTemperature is-a temperature Temperature is-a parameter A query: select all optical instruments which have operating mode vertical An inference: infer operating modes for a Fabry-Perot Interferometer which measures neutral temperature

10. 10 Class and property example Parameter –Has coordinates (independent variables) Observatory –Operates instruments Instrument –Has operating mode Instrument operating mode –Has measured parameters Date-time interval Data product

11. Remember, in science! Some of the knowledge is lost when it is placed into any particular representation structure, or may not be reusable (e.g. Frames) So, you may ask something that cannot be answered or inferred Knowledge evolves, i.e. changes Knowledge and understanding is very often context dependent (and discipline, language, and skill-level dependent, and …) 11

12. And, if you are used to logic You are working mostly within the world of logic, whereas we are trying to represent knowledge with logic and we are usually dealing with tangible objects, such as trees, clouds, rock, storms, etc. Because of this, we have to be very careful when translating real things into logical symbols - this can, surprisingly, be a difficult challenge. Consider your method of representation (yes, we do want to compute with it) 12

13. Thus A person who wants to encode knowledge needs to decouple the ambiguities of interpretation from the mathematical certainty of (any form of) logic. The nature of interpretation is critical in formal knowledge representation and is carefully formalized by KR scientists in order to guarantee that no ambiguity exists in the logical structure of the represented knowledge. 13

14. Representing Knowledge With Objects Take all individuals that we need to keep track of and place them into different buckets based on how similar they are to each other. Each bucket is given a descriptive based on what objects it contains. Since the individuals in a given bucket are at least somewhat similar, we can avoid needing to describe every inconsequential detail about each individual. Instead, properties that are common to all individuals in a bucket can just be assigned to the entire bucket at once. Properties are typically either primitive values (such as numbers or text strings) or may be references to other buckets. 14

15. Representing Knowledge With Objects Some buckets will be more similar to each other than others and we can arrange the buckets into a hierarchy based on the similarity. If all buckets in a branch in the tree of buckets share a property, the information can be further simplified by assigning the property only to the parent bucket. Other buckets (and individuals) are said to inherit that property. Buckets may have different names: e.g. Classes, Frames, or Nodes BUT, once we move to (e.g.) DL, not all object rules apply, e.g. cannot override properties Multiple inheritance is not always obvious to people 15

16. 16 Knowledge representation - visual UML – Universal Modeling Language –Ontology Definition Metamodel/Meta Object Facility (OMG) for UML –Provides standardized notation CMAP Ontology Editor (concept mapping tool from IHMC - http://cmap.ihmc.us/coe )http://cmap.ihmc.us/coe –Drag/drop visual development of classes, subclass (is-a) and property relationship –Read and writes OWL –Formal convention (OWL/RDF tags, etc.) White board, text file

17. Use case modeling Interactive session – we’ll add slides at some point after the presentation

18. First iteration Instead of implementing an ‘interface’ as defined in the “add record”, so that we can rapid prototype, we will generate a set of instances (i.e. partial or complete record) ‘by hand’ (instead of a form, for example) We’ll upload it We’ll run some queries (and we’ll be writing SPARQL instead of using an ‘interface’ as we may in a later iteration

19. Working with instances N3 –.n3 –http://www.w3.org/DesignIssues/Notation3http://www.w3.org/DesignIssues/Notation3 –http://hydrogen.informatik.tu- cottbus.de/wiki/index.php/N3_Notationhttp://hydrogen.informatik.tu- cottbus.de/wiki/index.php/N3_Notation Turtle (Terse RDF Triple Language) -.ttl –http://www.w3.org/TeamSubmission/turtle/http://www.w3.org/TeamSubmission/turtle/ RDF/XML -.rdf – http://www.w3.org/TR/REC-rdf-syntax/ http://www.w3.org/TR/REC-rdf-syntax/

20. Ttl - e.g. http://wiki.esipfed.org/index.php/File:Esip.txt Others -

21. e.g. ttl - header @prefix rdfs:. @prefix xsd:. @prefix foaf:. @prefix esip:. @prefix twperson:. @prefix twproject:.

22. e.g. ttl - organization :TWC a foaf:Organization; a foaf:Group; foaf:homepage ; foaf:name "Tetherless World Constellation"^^xsd:string. :GSFC a foaf:Organization; a foaf:Group; foaf:homepage ; foaf:name " NASA Goddard Space Flight Center"^^xsd:string.

23. e.g. ttl -participant :ChrisLynnes a esip:ProjectParticipant; foaf:name "Chris Lynnes"^^xsd:string; esip:memberOf :GSFC; esip:hasProjectParticipation [a esip:ProjectParticipation; esip:onProject twproject:DQSS; esip:hasRole [ a esip:PrincipalInvestigator; rdfs:label "Principal Investigator"^^xsd:string ] ]. twperson:PeterFox a esip:ProjectParticipant; foaf:name "Peter Fox"^^xsd:string; foaf:homepage ; esip:memberOf :TWC; esip:hasProjectParticipation [ a esip:ProjectParticipation; esip:onProject twproject:DQSS; esip:hasRole [ a esip:CoInvestigator; rdfs:label "Co-Investigator"^^xsd:string ] ].

24. e.g. ttl – another person twperson:StephanZednik a esip:ProjectParticipant; foaf:name "Stephan Zednik"^^xsd:string; foaf:homepage ; esip:memberOf :TWC; esip:hasProjectParticipation [ a esip:projectParticipation; esip:onProject twproject:DQSS; esip:hasRole [ a esip:TechnicalExpert; rdfs:label "Technical Expert"^^xsd:string ] ]; esip:knowsTechnology :Jena.

25. e.g. ttl – project/ program twproject:DQSS a esip:Project; foaf:name "Data Quality Screening Service (DQSS)"^^xsd:string; foaf:homepage ; esip:isFundedBy :NASA_ACCESS; esip:worksWithTechnology :Jena. :NASA_ACCESS a esip:Program; foaf:name "NASA ACCESS"^^xsd:string; foaf:homepage.

26. e.g. ttl –technology :Jena a esip:Technology; foaf:homepage ; foaf:name "Jena Semantic Web Framework"^^xsd:string.

27. Validation/Conversion tools http://www.w3.org/RDF/Validator/ http://www.rdfabout.com/demo/validator/

28. v1 PREFIX esip: http://esipfed.org> SELECT ?technology WHERE { ?project esip:worksWithTechnology ?technology. }

29. v1 PREFIX esip: http://esipfed.org> SELECT ?technology ?project WHERE { ?technology esip:worksWithTechnology ?project }

30. v0 SELECT ?who WHERE { ?who http://www.w3.org/2000/01/rdf- schema#subClassOf>.http://localhost/default#ProjectRole>. }