NERC DataGrid NERC DataGrid Vocabulary Server Use Cases Vocabulary Workshop, RAL, February 25, 2009

NERC DataGrid Use Cases  Metadata population with verifiable content  Dynamic drop-down lists  Semantic cross-walk  Smart discovery  Vocabulary Server usage models

NERC DataGrid Metadata Population Use Case  SeaDataNet is an EU project building a distributed data system across European and Mediterranean data centres  Semantic infrastructure provided by NDG Vocabulary Server  SeaSearch was a precursor project federating metadata across a slightly smaller network  SeaSearch was plagued by local vocabulary maintenance allowing illegal values into documents  SeaDataNet adopted two strategies to address this

NERC DataGrid Metadata Population Use Case  Strategy 1: constraint through tooling  Provide a metadata editor that  Allows manual entry of XML metadata records  Exports a simple RDBMS schema into XML  Link this up to the Vocabulary Server API to  Populate drop-down lists  Verify fields populated from vocabularies as they are output  SeaDataNet Mikado tool does this

NERC DataGrid Metadata Population Use Case  Strategy 2: constraint through validation  Problem is not everybody uses the tools  SeaDataNet metadata documents include Schematron code to validate field content  Schematron maintained by software polling Vocabulary Server API  Records validated at source using Schematron-aware tool (e.g. Oxygen 8 or later) or on-line validation service

NERC DataGrid Dynamic Drop-Down List Use Case  SeaDataNet marks up data using BODC Parameter Usage Vocabulary (21000 terms)  Navigation of something this size is a potential issue  Addressed by building three layers of increasingly broad terms over the top  Layers linked together using SKOS mappings

NERC DataGrid Dynamic Drop-Down List Use Case  Search client required to exploit this  An obvious design for this is a series of drop-down lists working down the hierarchy  These need to be dynamically populated to keep up to date with the master vocabulary versions

NERC DataGrid Dynamic Drop-Down List Use Case  The following URL gives all terms from the top level hierarchy:  rent rent  This may be used to set up a list of hot- linked labels pointing to Vocabulary Server concept URLs such as:  DS02 DS02  Represents the concept ‘chemical oceanography’  When selected by the user a Vocabulary Server call is issues and…..

NERC DataGrid Dynamic Drop-Down List Use Case  …we get a SKOS document thus - SDN:P081:3:DS02 Chemical oceanography The chemical oceanographic science domain T10:30:

NERC DataGrid Dynamic Drop-Down List Use Case  This delivers a set of URIs from the next level down in the hierarchy  Again, these may be displayed as hot- linked labels and again the user selects one to drill down into the next layer of the hierarchy through another VS call  Maris BV in the Netherlands have linked this to Ajax to produce a client

NERC DataGrid Semantic Crosswalk Use Case  BODC wishes to produce a GCMD DIF document from an EDMED V1.2 document  The “parameter” sections of the two documents are populated using different vocabularies (BODC PDV and GCMD Science Keywords)  This situation was usually addressed by having no parameter section in the output document. We can now do better…

NERC DataGrid Semantic Crosswalk Use Case  A list of BODC PDV terms as parameter URNs is obtained from the EDMED document, for example: SDN:P021:24:TEMP SDN:P021:24:PSAL SDN:P021:24:CPWC  This may then translated into a list of URLs

NERC DataGrid Semantic Crosswalk Use Case  This list may be rolled into an HTTP get request thus: rdByTerm?subjectTerm= urrent/TEMP&subjectTerm= 1/current/PSAL&subjectTerm= 021/current/CPWC&objectList= 41/current&predicate=255&inferences=true  An XML document is returned containing the GCMD Science Keywords that map to the three BODC terms as both text strings and URLs  The document may be reformatted using XSLT or XQuery to generate the “parameters” section for the DIF

NERC DataGrid Smart Discovery Use Case  Ability to find datasets tagged ‘rainfall’ using the search term ‘precipitation’  Also includes so-called ‘faceted searches’  Find one ‘type of thing’ by searching for another ‘type of thing’  For example:  Find datasets tagged ‘CTD’ (an instrument type) using the search term ‘salinity’ (a phenomenon)  Requires semantically rich relation ‘Salinity measuredBy CTD’  System needs to understand ‘measuredBy’ (requires rules)

NERC DataGrid Smart Discovery Use Case  Operational Smart Discovery requires:  An extensively populated full-blown ontology  A state of the art inference engine  VS API has Smart Discovery support methods  Based on SQL search on relational triple store  Inference functionality would need a locally- developed inference engine  Produces impressive demonstrations but not scalable to operational

NERC DataGrid VS Usage Models  The dynamic drop-down list use case may be implemented in at least three ways 1.Client issues a VS call on each user interaction returning a relatively small XML document 2.Client uses one VS call to download the entire thesaurus into an RDF-aware tool and then interacts through a local API 3.Entire thesaurus loaded into RDF-aware tool on the server that is interrogated by the client through something like SPARQL

NERC DataGrid VS Usage Models  Method 1  Experience shows it to work well for first three use cases  Smart Discovery could potentially require hundreds of server call per query.  Method 2  Requires a thick client  Could be part of an installed package.  Provides access to inference engines  Well-suited to Smart Discovery  Untested as far as we know.

NERC DataGrid VS Usage Models  Method 3  Being developed by Marine Metadata Interoperability (MMI) project based on OWL rather than SKOS.  Provides access to inference engines  Well-suited to Smart Discovery support