Transitioning Relational Databases to Ontologies Farid Cerbah Dassault Aviation farid.cerbahdassault

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Transitioning Relational Databases to
OntologiesFarid CerbahDassault
Aviationfarid.cerbah_at_dassault-aviation.fr
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Outline

• Problem statement
• Previous work
• The RDBToOnto tool and the RTAXON method
• Improving the process through database
  optimisation
• A case study in aircraft maintenance
• Extending RDBToOnto
• Conclusion

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Problem statement

• Relational databases are valuable heterogeneous
  sources for ontology learning
• Better accuracy can be expected than from text
  corpora
• Ontology learning from relational databases is
  not a new research issue
• Limitations of existing support
• Problem often restricted to finding automated
  ways to import tables into ontologies
• Derivation of ontologies with flat structure that
  look like the source databases

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Our contribution

• RDBToOnto Platform
• A comprehensive software support to learn
  fine-tuned ontologies
• A framework that eases the development and the
  experimentation of transitioning methods
• RTAXON Method
• To find out taxonomies hidden in the data

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A motivating example
Typical mappings covered by several methods
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Previous work (1)

• RDB -gt Ontology Transformation
• Database Reverse Engineering
• Many transformation rules from this domain are
  reused for ontology learning
• Behm et al. 1997, Ramanathan Hodges 1997,
  
• Approaches mostly based on an analysis of the RDB
  schema
• Data correlations are considered but
• with the restriction "Data Key Values"
• Key inclusion may express inheritance
• Exploiting null values semantics Lammari et al.
  2007
• Partitioning of a table on the basis of null
  values may reveal concept hierarchies
• Involves data from non-key attributes

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Previous work (2)

• Mapping languages and tools
• D2RQ
• RDB to OWL/RDF mapping
• Ontology-based access to relational databases
• Rewriting SPARQL queries into SQL
• Relational.OWL
• A minimal ontology of tables and column and
  a processor to populate this ontology with data
  from relational databases
• Can be used to exchange data between databases
• Triplify
• Plugin for web applications
• Converts the result of SQL queries into RDF
• KAON Reverse
• Software support to interactively map an RDB
  schema to a predefined ontology
• DataMaster
• Protégé Plugin to import table data into
  ontologies

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RDBToOnto

• A user-oriented tool with a full-fledged user
  interface
• Supports an extensive process from the access to
  the data to ontology generation
• Includes the RTAXON converter

• Though automated to a large extent, local
  constraints can be interactively included to
  progressively refine the ontologies
• Types of local constraints
• Table and column exclusion
• Naming patterns for classes and instances
• Categorisation patterns

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The RTAXON method

• Major improvement over existing methods
• Further refine the classes derived from the
  schema with subclasses found in the content of
  the relations
• Focus on reliable categorisation patterns

Categorising attribute
Access Zone
Door
Panel
Fairing
Floor

• Two sources involved in the identification of
  categ. attributes
• Attribute names
• Revealed by lexical clues
• Redundancy in attribute extensions
• Entropy-based approach to find good profiles

Formal definition of RTAXON
Demo
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Optimising the source databases

• Another key improvement is the inclusion of a
  database optimisation step
• Many input databases suffer from data duplication
  problems
• Optimisation -gt eliminate data duplication
  through the processing of inclusion dependencies

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Effect of inclusion dependency processing

• Inclusion dependencies ? more inter-class
  relations (i.e. object properties).

Without ID identification
With ID identification
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Identification of inclusion dependencies

• RDBToOnto includes an editor to interactively
  define inclusion dependencies
• Automated identification of inclusion
  dependencies
• A data mining approach Based on LATINO

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Mining inclusion dependencies with LATINO
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A case study in aircraft maintenance
KCIT(GATE-based annotator)
RDBToOnto LATINO
Radiant
OWLIM
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The ontology acquisition process

• The legacy data
• LSA database an heterogeneous relational
  database that gathers all information related to
  maintenance activity
• Required logistic resources
• Aircraft parts (Product tree)
• Scheduling data
• Standards Documents including widely shared
  conceptual models
• The ontology acquisition process
• A multi-step transitioning process that favours
  modular design

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Model Boostrapping Ontology Normalisation
MSG-3
SNS/ATA
FOAF
Reusable Ontologies
ltgtlt/gt ltgt lt/gt . ltgt lt/gt
Model Bootstrapping
Legacy Data
Ontology Learning Tools
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The defined RDBToOnto conversion project

• 75 constraints
• Mostly naming patterns and inclusion dependencies
• Resulting ontology
• Ontology model
• 115 classes, 334 datatypes, 54 object properties
• Population
• 49617 class instances, 51449 object property
  instances
• No constraints for categorisation
• The ten discovered hierarchies by RTAXON are
  relevant
• Good behaviour when faced with categorisation
  conflicts

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The generated class hierarchy
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Identified object properties
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RDBToOnto extension capabilities

• RDBToOnto is a user-oriented tool but it is also
  a framework
• Written in Java
• OWL as target language (exploiting Jena 2.5 API)
• Two types of components can be added
• Database readers to cover more database formats
• Converters to implement new learning methods
• New converters can have their specific global
  options, local constraints and GUI

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Structure of RDBToONTO
Database
DBReader Database getDatabase() Table
ReadData(String name)
RDBToOntoConverter OntModel
Convert(Database db) OntClass CreateClass(TableDef
)
RTAXON
BasicConverter
MSAccessReader
DB2Reader
can be extended by the users
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The neutral database model


Database
DBSchema
Table
Column
Attribute


TableDef
friendlyNames
Values
String


Key
Input to any converter

PrimaryKey
ForeignKey
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Conclusion

• We presented a significant support for
  transitioning relational databases to ontologies
• RDBToOnto and RTAXON method have been evaluated
  on significant databases
• RTAXON is just a first step as many extensions
  can be studied
• Learning two-level hierarchies
• Automatically generating local constraints (e.g.
  naming patterns)
• More resources are available on TAO project web
  site, including
• User Guide and demos
• Development Guide
• A fully implemented sample showing how to extend
  the tool