An introduction by Jean-Yves Vion-Dury (XRCE) to the LRM, an upper level ontology that is being developed within PERICLES, to enable the description of evolving digital ecosystems.

1. GRANT AGREEMENT: 601138 | SCHEME FP7 ICT 2011.4.3
Promoting and Enhancing Reuse of Information throughout the Content Lifecycle taking account of Evolving
Semantics [Digital Preservation]
“This project has received funding from the European Union’s
Seventh Framework Programme for research, technological
development and demonstration under grant agreement no601138”.
Jean-Yves Vion-Dury, senior scientist,
Xerox Research Centre Europe

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In one glance:
• an OWL ontology to model ecosystems as evolving linked
(dependent) resources
• Focused on change management, but extensible through
domain specific specialisation
• The model will also serve as a data schema: RDF graph
instances stored in triple stores will reflect the evolution
of the preserved resources (LRM-service)

4. In one glance:
• Monitor change impact
• Maintain consistency (proactive mode)
• Detect (potential) inconsistency (tracking mode)
• Maintain an history of the evolution: LRM as a generalized
provenance model
• Ecosystem analysis and governance support
• querying the model
• Risk based analysis
• « Time machine » to reconstruct coherent view of the past

5.  Schema level (conceptual/static)
◦ Organize our knowledge
 Compatible & incompatible classes and subclasses
◦ Make it explicit and sharable
◦ Allows formal verification
 Instance Level (operational/dynamic)
◦ Model and capture the structure of the target ecosystem
◦ Manage its evolution (react to events, undertake actions)
◦ Behave as a super datatype/datamodel

7. Description
ExogenousResource
disjointWith
Resource
EndogenousResource
subClassOf
subClassOf
subClassOf
AbstractResource
subClassOf

8. Abstract Concrete
Exogenous
mutable
immutable
Endogenous
mutable
immutable

10. Abstract Concrete
Exogenous
mutable
immutable
Endogenous
mutable
immutable
Digital resource

11. Abstract Concrete
Exogenous
mutable
immutable
Endogenous
mutable
immutable
Digital resource

12. Abstract Concrete
Exogenous
mutable
immutable
Endogenous
mutable
immutable
Human ⊑ Agent

13. Abstract Concrete
Exogenous
mutable
immutable
Endogenous
mutable
immutable
Software ⊑ Agent
Software ⊑ Agent

14. Description
ExogenousResource
disjointWith
Resource
EndogenousResource
subClassOf
subClassOf
subClassOf
AbstractResource
subClassOf

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La Trahison des images (R. Magritte)

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Les deux mystères (R. Magritte)

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Description
ExogenousResource
disjointWith
Resource
EndogenousResource
subClassOf subClassOf
subClassOf
AbstractResourcesubClassOf

20. 20
NantesTriptych
creationTime
« 1994 »

21. 21
NantesTriptych
creationTime
« 1994 »
« 10/15/2015 »
creationTime

22. 22
NantesTriptych
creationTime
« 1994 »
« 10/15/2015 »
creationTime
« 10/15/2015 »
creationTime

23. 23
NantesTriptych
creationTime
« 1994 »
« 10/15/2015 »
creationTime
creationTime

25.  Schema level (conceptual/static)
◦ Organize our knowledge
 Compatible & incompatible classes and subclasses
◦ Make it explicit and sharable
◦ Allows formal verification
 Instance Level (operational/dynamic)
◦ Model and capture the structure of the target ecosystem
◦ Manage its evolution (react to events, undertake actions)
◦ Embedds a dedicated language (ReAL)
 LRM now becomes a powerful datamodel/typesystem to capture
the dynamics

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Key Ideas
• dependencies may have complex topology
• dependency are characterized by usage intention
• resources may be versioned
• Changes apply both to resources of the ecosystem AND
their descriptions
• Open the way toward reflexive digital preservation

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Key Ideas
• dependencies may have complex topology
• dependency are characterized by usage intention
• resources may be versioned
• Changes apply both to resources of the ecosystem AND
their descriptions
• An LRM model for LRM: reflexive digital preservation

28. Tree transformer
from
to
XML comment stripper
XML LinearizerXML parser
from
from
Conjunctive Dependency

29. Tree transformer
from
to
XML comment stripper
XML Linearizer
Disjunctive Dependency
from
from
Conjunctive Dependency
XML parser 2XML parser 1
from from
to
D-Connector

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Key Ideas
• dependencies may have complex topology
• dependency are characterized by usage intention
• resources may be versioned
• Changes apply both to resources of the ecosystem AND
their descriptions
• An LRM model for LRM: reflexive digital preservation

31. Release intention
dependency path
Develop intention
dependency path
XSLT XMLOrig
xmlTransDep
fromfrom
XMLTrans
rendDevDep
to
IDEDisjunctiveDependency
VS1 VS2
D-Connector
R’
from from
from
from
to
to
XMLOrig
DLL
from
from
from
R
to
rendRelDep
render
intention
intention
release
intention
develop
intention

32.  Direct visualization
 Injection of information
◦ By hand
◦ By automated extraction (PET2LRM tool, others)
◦ By data analysis
 Propagation of changes
◦ Forward propagation (useful for proactive change
management)
◦ Backward propagation (action planning, focused on
demand evaluation)
 Simulation
◦ Inject hypothesis & evaluate potention consequences
◦ Risk analysis

35.  Maintain an instance of LRM ontology
◦ reflects change-related information about the preservation ecosystem
 Offer services to other PERICLES components
◦ General RDF graph queries (à la SparQL)
◦ LRM aware queries
 Consistency check
 Version management
◦ Graph Update request
 React to events
◦ Propagation of changes (resource creation/destruction/modification)
◦ Notifications/alerts
 Ask for external services
◦ Computation on digital objects
 Digests
 delta computation,
 transformation (e.g. compilation)
 Validation
 …