a semantic modelling approach to biological parameter interoperability roy lowry & laura bird...
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A Semantic Modelling Approach A Semantic Modelling Approach to Biological Parameter to Biological Parameter
InteroperabilityInteroperability
Roy Lowry & Laura BirdBritish Oceanographic Data Centre
Pieter HaaringRIKZ, Rijkswaterstaat, The Netherlands
Ocean Biodiversity InformaticsOcean Biodiversity Informatics
Presentation OverviewPresentation 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
The Nature of the ProblemThe 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
Dictionaries and Data ModelsDictionaries 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’
Dictionaries and Data ModelsDictionaries 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
The Starting PositionThe 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
Manual MappingManual 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!
Semantic MatchingSemantic 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
Dictionary to Semantic ModelDictionary 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
Dictionary to Semantic ModelDictionary to Semantic Model
• Semantic model developed from DONAR Semantic model developed from DONAR with an increased semantic element with an increased semantic element count to overcome shoe-horningcount to overcome shoe-horning
• Principle that semantic elements may be Principle that semantic elements may be combined automatically to produce text combined automatically to produce text descriptions maintaineddescriptions maintained
• Currently implemented as three sub-Currently implemented as three sub-modelsmodels
• Element superset will ultimately be Element superset will ultimately be created as a single modelcreated as a single model
Dictionary to Semantic ModelDictionary 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
Mapping Semantic ModelsMapping 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
Mapping Semantic ModelsMapping 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
Mapping Semantic ModelsMapping 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)
Semantic Model ApplicationsSemantic 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)
ConclusionsConclusions
• 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
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