1. ESIP Semantic Web Products and Services ‘triples’ “tutorial” aka sausage making ESIP SW Cluster, Jan 6 2011 http://wiki.esipfed.org/index.php/Testb ed See also http://wiki.esipfed.org/index.php/Sema ntic_Web_Tutorials

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 – 30 mins – Peter – Evaluation –10 mins - Hook Thu AM Session 4 – Query development – query introduction, SPARQL tutorial, 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 - 1hr – Hook – Evaluation –10 mins - Hook Items that would lead into intermediate (next) tutorial – 20 mins

4. Application definition Means examine the use case Scope the first iteration Useful to think about metrics and record baselines (if they exist)

5. 5 Implementation Basics Review your use case with team and experts Go into detail of your ontology; test it using the tools you have Look at the use case document and examine the actors, process flow, artifacts, etc. Start to develop a design and an architecture Keep in mind that it is more flexible to place the formal semantics within your interfaces, i.e. between layers and components in your architecture or between ‘users’ and ‘information’ to mediate the exchange

6. Actors The initial analysis will often have many human actors Look to see where the human actors can be replaced with machine actors – many will require additional semantics, i.e. knowledge encoding If you are doing this in a team, take steps to ensure that actors know their role and what inputs, outputs and preconditions are expected Often, you may be able to ‘run’ the use case (really the model) before you build anything 6

7. Process flow Each element in the process flow usually denotes a distinct stage in what will need to be implemented Often, actors mediate the process flow Consider the activity diagram (and often a state diagram) as a means to turn the written process flow into a visual one that your experts can review Make sure the artifacts and services have an entry in the resources section Often the time you may do some searching 7

8. Preconditions Often the preconditions are very syntactic and may not be ready to fit with your semantically-rich implementation Some level of modeling of these preconditions may be required (often this will not be in your first-pass knowledge encoding, which focuses on the main process flow, i.e. goal, description, etc.) Beware of using other entities data and services: e.g., policies, access rights, registration, and ‘cost’ 8

9. Artifacts Add artifacts that the use case generates to the resources list in the table It is often useful to record which artifacts are critical and which are of secondary importance Be thinking of provenance and the way these artifacts were produced, i.e. what semantics went into them use this information to produce suitable metadata or annotations Engage the actors to determine the names of these artifacts and who should have responsibility for them (usually you want the actors to have responsibility for evolution) 9

10. Reviewing the resources Apart from the artifacts and actor resources, you may find gaps Your knowledge encoding is also a resource, make it a first class citizen, i.e. give it a namespace and a URI Often, a test-bed with local data is very useful at the start the implementation process, i.e. pull the data, maybe even implement their service (database, etc.) 10

11. Back to the knowledge encoding Declarative: OWL, RDFS, SKOS? Need rules? Need query? Science expert review and iteration Means you need something that they can review, with precise names, properties, relations, etc. The knowledge engineering stage is much like a software engineering process 11

12. Chris: Sample Queries Which projects are working with semantic web technology? What technologies are being used in NASA’s ACCESS program? Are any projects working with the same dataset? Which datasets are being used in projects working with semantic web?

13. Use Case 1: Enter Project Data (cont.) 1.Use Case 1: Enter Project Data 2.Short Definition Someone from the project is assigned to populate the triple store with the project information. This capability is supplied via a simple Web interface. 3.Primary Actor: Project staff member 4.Purpose: Enter project information into the triple store. 5.Assumptions: The staff member knows enough about his/her project to populate the fields

14. Use Case 1: Enter Project Data 6.Scenario 1.Interface asks for overall project information: name and program 2.User enters basic project information 3.Web interface saves root project triple 4.User selects Add Staff and Web interface presents existing staff, plus blank for staff not yet in triple store 5.User selects existing staff and possible role, and Web interface saves triples linking staff to project 6.User selects Add Technology and Web interface presents existing technologies, plus blank for technologies not yet in triple store 7.User selects existing technology, and Web interface saves triples linking technology to project 8.User selects Add Datasets and Web interface presents existing datasets, plus blank for datasets not yet in triple store 9.User selects existing dataset, and Web interface saves triples linking technology to project 7.Extensions 5b.User enters new staff and picks existing role and Web interface saves triples linking staff to project 7b.User enters new technology and Web interface saves triples linking technology to project 9b. User enters new dataset and Web interface saves triples linking dataset to project 8. Definition of success: triples are added to the database

15. So, recall – our first iteration So that we can rapid prototype, instead of implementing an ‘interface’ as defined in the “add record” We generated a set of instances (i.e. partial or complete record) Who tried it? How did it work/ not?

16. Second iteration Implementing an ‘interface’ as defined in the “add record”, to generate instances Let’s talk about what they may look like

17. What’s next …

18. Use Case 2: Produce Program Technology Inventory 1.Use Case 2: Produce Program Technology Inventory 2.Short Definition An agency program manager gets a report on what technologies are being used in his program. 3.Primary Actor: Agency program manager 4.Purpose: Get a report on technology usage for planning purposes (or occasional “fire drill”). 5.Assumptions: The program’s projects have been entered into the triple store

19. Produce Technology Inventory, cont. 6.Scenario 1.User pulls up Technology Inventory Report and selects from available programs. 2.Web Interface queries triple store for technologies used by projects within the select program and generates a report. 7. Extensions: None 8. Definition of success: Program Manager gets a formatted list of which projects are using which technologies within his / her program. 9. Notes: This scenario is not uncommon, particularly for new technologies.