1. A Semantic Modelling Approach to Biological Parameter Interoperability Roy Lowry & Laura Bird British Oceanographic Data Centre Pieter Haaring RIKZ, Rijkswaterstaat, The Netherlands Ocean Biodiversity Informatics

2. Presentation Overview The nature of the problem Dictionaries and data models The starting position Manual mapping Automation through semantic matching From dictionary to semantic model Mapping semantic models Semantic model applications Conclusions and lessons learned

3. The Nature of the Problem BODC and Rijkswaterstaat both have marine databases holding a wide range of physical, chemical and biological parameters Both were to be included pan-European metadatabases (EDIOS and SEA-SEARCH CDI) using a common discovery vocabulary BODC set up the vocabulary and obviously included a mapping to the BODC Parameter Dictionary Problem arose of how to provide a similar mapping for the Rijkswaterstaat If the Rijkswaterstaat data markup vocabulary could be mapped to the BODC Parameter Dictionary then the BODC discovery vocabulary mapping could be used

4. Dictionaries and Data Models BODC systems have roots in the GF3 model, which means:  Data values are linked to a parameter code  Parameter code is defined in a Parameter Dictionary  The parameter code specifies more than one metadata item for the data value  For chemical and biological data ‘more than one’ becomes ‘a lot’

5. Dictionaries and Data Models Rijkswaterstaat uses data models (DONAR becoming WADI)  Measurements are accompanied by attributes containing specific atomic metadata items  Each attribute is populated from a controlled vocabulary  DONAR constrains attribute term combinations using a ‘parameter dictionary’ concept  WADI reduces maintenance overheads by allowing any combination

6. The Starting Position BODC  Parameter Codes defined by two plain-text fields  Related semantic information not necessarily in the same field  Fields would not concatenate sensibly  OK for humans, but not for machines Rijkswaterstaat  Consistently located semantics  Metadata fields that concatenate sensibly in both Dutch and English

7. Manual Mapping Manual mapping protocol  For each entry in the Rijkswaterstaat ‘dictionary’ spreadsheet  Look up code with identical meaning using BODC Dictionary search tools (Access Filter by Form)  If found –Copy BODC code from Access and paste into spreadsheet  Else –Prepare dictionary update record and submit for QA and load Error prone and 500 entries is pushing the limit of human endurance!

8. Semantic Matching When code lists run into thousands, automation is required Rijkswaterstaat developed a semantic matching tool to pull matching terms (preferably one) from the BODC dictionary Defeated by the lack of standardisation in the BODC plain-text fields e.g.  Calanus abundance  Abundance of Calanus  Calanus count  Number of Calanus

9. Dictionary to Semantic Model Became apparent that the BODC Dictionary required significant improvement if it was to support mapping automation Development strategy was to model the parameter code in the same way DONAR models a measurement Semantic model developed to cover all codes in BODC Dictionary

10. Dictionary to Semantic Model Semantic model developed from DONAR with an increased semantic element count to overcome shoe-horningSemantic model developed from DONAR with an increased semantic element count to overcome shoe-horning Principle that semantic elements may be combined automatically to produce text descriptions maintainedPrinciple that semantic elements may be combined automatically to produce text descriptions maintained Currently implemented as three sub- modelsCurrently implemented as three sub- models Element superset will ultimately be created as a single modelElement superset will ultimately be created as a single model

11. Dictionary to Semantic Model Biological sub-model semantic elements  Parameter (Abundance, Biomass)  Taxon_code (ITIS code)  Taxon_name  Taxon_subgroup (gender, size, stage)  Parameter_compartment_relationship (per unit volume of the, per unit area of the)  Compartment (water column, bed, sediment)  Sample_preparation  Analysis  Data_processing Needs further refinement e.g. subdivide Taxon_subgroup

12. Mapping Semantic Models Two stage process  First map the semantic elements  DONAR Parameter = BODC Parameter + Parameter_compartment_relationship  DONAR Compartment = BODC Compartment  Then map vocabularies for mapped elements  Surface water = water column Relational database designers will recognise this as normalisation

13. Mapping Semantic Models Number of ‘look-ups’ required is reduced by an order of magnitude Vocabulary elements have simple semantics so automation is possible Approximately 90% of the Rijkswaterstaat to BODC mapping accomplished by a single SQL statement Straightforward extension of vocabulary maps (different names for same thing) sorted out most of the rest Thesauri could help reduce the need for this

14. Mapping Semantic Models ‘Hard Core’ problems required manual resolution  Unclear or ambiguous semantics in Rijkswaterstaat element vocabularies (residual beta)  Problems with Dutch to English translation Some mapping errors were detected  Caused by homonyms (Branchiura)  Emphasises the need for more than just a name for a taxon (reference or ITIS code)

15. Semantic Model Applications Semantic modelling is a lowest common denominator approach to metadata This is what makes it good for mapping The approach also offers the basis for user-controlled data discovery and interoperability  User chooses the semantic element subset  User data selection interaction based on the subset vocabulary  Automated interoperability requires more sophistication (thesauri, ontologies)

16. Conclusions Don’t even think about manual mapping of large parameter dictionaries 99% of a map is completed in the first 10% of the time More standardisation means fewer errors and problems Semantic model vocabularies need ontologies and thesauri to achieve their full interoperability potential

17. Conclusions Semantic modelling works for mappings between dictionaries and data models It also has great potential for parameter discovery and interoperability