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Propagation of Policies in Rich Data Flows
1
Enrico Daga†	
  Mathieu d’Aquin†	
  Aldo Gangemi‡	
  Enrico Motta†	
  	
  
†	
  Knowledge	
  Media	
  Ins2tute,	
  The	
  Open	
  University	
  (UK)	
  
‡	
  Université	
  Paris13	
  (France)	
  and	
  ISTC-­‐CNR	
  (Italy)	
  
The	
  8th	
  Interna2onal	
  Conference	
  on	
  Knowledge	
  Capture	
  (K-­‐CAP	
  2015)	
  
October	
  10th,	
  2015	
  -­‐	
  Palisades,	
  NY	
  (USA)	
  
hRp://www.k-­‐cap2015.org/
Feedback	
  welcome:	
  @enridaga	
  #kmiou
Motivation
• Governing the life cycle of data on the web is a
challenging issue for organisations and users.
• Assessing what policies on input data propagate to
the output of a process is a crucial problem.
2
Policies
3
Foundations
Constraints and permissions set by the data owner regarding the reuse of
the data, ie. mostly licences.
We can describe licences/policies (RDF+ODRL).
RDF License Database
http://datahub.io/dataset/rdflicense
Describes ~140 licenses using RDF
and the Open Digital Rights Language
(ODRL).
Reports 113 policies.
lic:cc-by-nc4.0 a odrl:Policy ;
rdfs:label "CC-BY-NC" ;
odrl:permission [
odrl:action cc:Distribution,
ldr:extraction , ldr:reutilization ,
cc:DerivativeWorks , cc:Reproduction ;
odrl:duty [ odrl:action
cc:Attribution , cc:Notice ] ] ;
odrl:prohibition [ odrl:action
cc:CommercialUse ]
Objective
We need to understand how these policies propagate in
data flows!
4
Data flows
5
Foundations
What are the semantic relations
between input and output?
Data flows can be thought as graphs of data objects.
Datanode is an ontology for data centric description of applications.
Built as hierarchy of relations.
More then 100 relations so far.
http://purl.org/datanode/ns/
http://purl.org/datanode/docs/
Policy Propagation Rule (PPR)
6
Relying on Datanode for an enumeration of the possible relations and
on the RDF License Database for the possible policies, we can setup
rules like the following:
A Horn clauses of the form:
propagates(odrl:duty cc:Attribution, dn:isCopyOf)
For example:
It can be simplified as:
Foundations
Problem
A description of policies and data flows implies a huge number of
Policy Propagation Rules to be specified and computed (number
of possible policies times number of possible relations between data
objects).
How to abstract this KB to make the management and
reasoning on policy propagation rules easier?
7
Contributions
(1) A methodology to obtain an abstraction that allows to reduce the
number of rules significantly (using an Ontology).
(2) Evaluate how effective this methodology is when using the
Datanode ontology.
(3) Demonstrate how this ontology can evolve in order to better
represent the behaviour of Policy Propagation Rules.
8
(A)AAAA Methodology
9
1.Aquire rules
2.Analyse the rules: find clusters using Formal Concept Analysis
3.Abstract the rules: match clusters & ontology hierarchy
4.Assess the compression, and diagnose errors or refinements in the
rules or the ontology
5.Adjust the rules or the ontology
repeat from 2.
Preparing the Rules Base
• This phase required a manual supervision of all associations
between policies and relations in order to establish the initial set
of propagation rules.
• We used Contento, it was possible to prepare manually the rule
base with a reasonable effort.
• The initial knowledge base was then composed of 3363 Policy
Propagation Rules
10
Acquisition
Formal Concept Analysis (FCA)
• Input is a Formal Context (a binary matrix of objects/attributes)
• Basic unit is a Close Concept:
– (O,A) => (Extension,Intension)
– Closure operator ’ … (O,A) is a concept when O’=A and A’=O
• Classifies concepts hierarchically in a concept lattice
– Top: all objects, no attr, bottom: all attributes, no obj
11
Analysis
Applying FCA to the Rule Base
80 concepts
Clusters of rules
Relations that have a common behaviour: they
propagate the same policies.
But why do they do it?
12
Analysis
Detect matches with the ontology
13
Abstraction
Search for matches between concepts and branches taken
from the ontology hierarchy.
When found, subtract the rules from the KB accordingly.
Example/1
14
Abstraction
dn:hasPart and dn:isVocabularyOf are valid abstractions
dn:isPartOf is not, because dn:isSelectionOf apparently does
not propagate (all) the policies…
Example/2
15
Abstraction
dn:hasPart (49 rules)
propagates(dn:hasPart,duty	
  cc:ARribu2on)	
  
propagates(dn:hasPart,duty	
  cc:Copyle^)	
  
propagates(dn:hasPart,duty	
  cc:No2ce)	
  
propagates(dn:hasPart,duty	
  cc:SourceCode)	
  
propagates(dn:hasPart,duty	
  odrl:aRachPolicy)	
  
propagates(dn:hasPart,duty	
  odrl:aRachSource)	
  
propagates(dn:hasPart,duty	
  odrl:aRribute)
propagates(dn:hasSec2on,duty	
  cc:ARribu2on)	
  
propagates(dn:hasSec2on,duty	
  cc:Copyle^)	
  
propagates(dn:hasSec2on,duty	
  cc:No2ce)	
  
propagates(dn:hasSec2on,duty	
  cc:SourceCode)	
  
propagates(dn:hasSec2on,duty	
  odrl:aRachPolicy)	
  
propagates(dn:hasSec2on,duty	
  odrl:aRachSource)	
  
propagates(dn:hasSec2on,duty	
  odrl:aRribute)	
  
propagates(dn:hasSelec2on,duty	
  cc:ARribu2on)	
  
propagates(dn:hasSelec2on,duty	
  cc:Copyle^)	
  
propagates(dn:hasSelec2on,duty	
  cc:No2ce)	
  
propagates(dn:hasSelec2on,duty	
  cc:SourceCode)	
  
propagates(dn:hasSelec2on,duty	
  odrl:aRachPolicy)	
  
propagates(dn:hasSelec2on,duty	
  odrl:aRachSource)	
  
propagates(dn:hasSelec2on,duty	
  odrl:aRribute)	
  
propagates(dn:hasSample,duty	
  cc:ARribu2on)	
  
propagates(dn:hasSample,duty	
  cc:Copyle^)	
  
propagates(dn:hasSample,duty	
  cc:No2ce)	
  
propagates(dn:hasSample,duty	
  cc:SourceCode)	
  
propagates(dn:hasSample,duty	
  odrl:aRachPolicy)	
  
propagates(dn:hasSample,duty	
  odrl:aRachSource)	
  
propagates(dn:hasSample,duty	
  odrl:aRribute)	
  
propagates(dn:hasPor2on,duty	
  cc:ARribu2on)	
  
propagates(dn:hasPor2on,duty	
  cc:Copyle^)	
  
propagates(dn:hasPor2on,duty	
  cc:No2ce)	
  
propagates(dn:hasPor2on,duty	
  cc:SourceCode)	
  
propagates(dn:hasPor2on,duty	
  odrl:aRachPolicy)	
  
propagates(dn:hasPor2on,duty	
  odrl:aRachSource)	
  
propagates(dn:hasPor2on,duty	
  odrl:aRribute)	
  
propagates(dn:hasIden2fiers,duty	
  cc:ARribu2on)	
  
propagates(dn:hasIden2fiers,duty	
  cc:Copyle^)	
  
propagates(dn:hasIden2fiers,duty	
  cc:No2ce)	
  
propagates(dn:hasIden2fiers,duty	
  cc:SourceCode)	
  
propagates(dn:hasIden2fiers,duty	
  odrl:aRachPolicy)	
  
propagates(dn:hasIden2fiers,duty	
  odrl:aRachSource)	
  
propagates(dn:hasIden2fiers,duty	
  odrl:aRribute)	
  
propagates(dn:hasExample,duty	
  cc:ARribu2on)	
  
propagates(dn:hasExample,duty	
  cc:Copyle^)	
  
propagates(dn:hasExample,duty	
  cc:No2ce)	
  
propagates(dn:hasExample,duty	
  cc:SourceCode)	
  
propagates(dn:hasExample,duty	
  odrl:aRachPolicy)	
  
propagates(dn:hasExample,duty	
  odrl:aRachSource)	
  
propagates(dn:hasExample,duty	
  odrl:aRribute)
propagates(dn:isVocabularyOf,duty	
  cc:ARribu2on)	
  
propagates(dn:isVocabularyOf,duty	
  cc:Copyle^)	
  
propagates(dn:isVocabularyOf,duty	
  cc:No2ce)	
  
propagates(dn:isVocabularyOf,duty	
  cc:SourceCode)	
  
propagates(dn:isVocabularyOf,duty	
  odrl:aRachPolicy)	
  
propagates(dn:isVocabularyOf,duty	
  odrl:aRachSource)	
  
propagates(dn:isVocabularyOf,duty	
  odrl:aRribute)
propagates(dn:aRributesOf,duty	
  cc:ARribu2on)	
  
propagates(dn:aRributesOf,duty	
  cc:Copyle^)	
  
propagates(dn:aRributesOf,duty	
  cc:No2ce)	
  
propagates(dn:aRributesOf,duty	
  cc:SourceCode)	
  
propagates(dn:aRributesOf,duty	
  odrl:aRachPolicy)	
  
propagates(dn:aRributesOf,duty	
  odrl:aRachSource)	
  
propagates(dn:aRributesOf,duty	
  odrl:aRribute)	
  
propagates(dn:datatypesOf,duty	
  cc:ARribu2on)	
  
propagates(dn:datatypesOf,duty	
  cc:Copyle^)	
  
propagates(dn:datatypesOf,duty	
  cc:No2ce)	
  
propagates(dn:datatypesOf,duty	
  cc:SourceCode)	
  
propagates(dn:datatypesOf,duty	
  odrl:aRachPolicy)	
  
propagates(dn:datatypesOf,duty	
  odrl:aRachSource)	
  
propagates(dn:datatypesOf,duty	
  odrl:aRribute)	
  
propagates(dn:descriptorsOf,duty	
  cc:ARribu2on)	
  
propagates(dn:descriptorsOf,duty	
  cc:Copyle^)	
  
propagates(dn:descriptorsOf,duty	
  cc:No2ce)	
  
propagates(dn:descriptorsOf,duty	
  cc:SourceCode)	
  
propagates(dn:descriptorsOf,duty	
  odrl:aRachPolicy)	
  
propagates(dn:descriptorsOf,duty	
  odrl:aRachSource)	
  
propagates(dn:descriptorsOf,duty	
  odrl:aRribute)	
  
propagates(dn:typesOf,duty	
  cc:ARribu2on)	
  
propagates(dn:typesOf,duty	
  cc:Copyle^)	
  
propagates(dn:typesOf,duty	
  cc:No2ce)	
  
propagates(dn:typesOf,duty	
  cc:SourceCode)	
  
propagates(dn:typesOf,duty	
  odrl:aRachPolicy)	
  
propagates(dn:typesOf,duty	
  odrl:aRachSource)	
  
propagates(dn:typesOf,duty	
  odrl:aRribute)	
  
propagates(dn:rela2onsOf,duty	
  cc:ARribu2on)	
  
propagates(dn:rela2onsOf,duty	
  cc:Copyle^)	
  
propagates(dn:rela2onsOf,duty	
  cc:No2ce)	
  
propagates(dn:rela2onsOf,duty	
  cc:SourceCode)	
  
propagates(dn:rela2onsOf,duty	
  odrl:aRachPolicy)	
  
propagates(dn:rela2onsOf,duty	
  odrl:aRachSource)	
  
propagates(dn:rela2onsOf,duty	
  odrl:aRribute)
dn:isVocabularyOf (42 rules)
7 rules! 7 rules!
Compression Factor
16
Assessment
By applying the original Datanode ontology, 1925 rules could be
removed out of 3363, for a compression factor of 0.572.
We calculate the CF as the number of abstracted rules divided by the
total number of rules:
Considerations
17
Assessment
2. The Datanode ontology has not been designed for the purpose of
representing a common behaviour of relations in terms of propagation
of policies.
It is possible to refine the ontology in order to make it cover this use
case better (and possibly reduce the number of rules even more).
1. The size of the matrix that was manually supervised is large, and it
is possible that errors have been made at that stage of the process.
Observing the measures
18
Assessment
concepts * relations=~9040 (partial) matches. Hard to explore!
Inspecting a partial match with high precision and low recall highlights a
problem that might be easy to fix, as the number of relations and policies to
compare will be low.
Operations
19
Adjustment
We try to make adjustments in order to improve the compression
factor. We defined a set of operations, targeted to (a) fix errors in the
initial rule base and (b) refine the ontology.
• Fill: makes a branch be fully in a cluster of concept c, attempting to
push Pre up to 1.
• Group: some relations that share a concept, but belong to different
branches, are abstracted by a new relation.
• Merge: two distinct branches are abstracted by a new relation.
• Wedge: change the top relation of a branch to make it fully match
the concept.
(and we run our process again from the Analysis phase to the Assessment)
Example: Fill
20
Adjustment
… dn:isSelectionOf should indeed propagate all the policies listed
in this concept. Fill adds the following rules:
propagates(dn:isSelectionOf, duty cc:Attribution)
propagates(dn:isSelectionOf, duty cc:Copyleft)
propagates(dn:isSelectionOf, duty cc:Notice)
propagates(dn:isSelectionOf, duty cc:SourceCode)
propagates(dn:isSelectionOf, duty odrl:attachPolicy)
propagates(dn:isSelectionOf, duty odrl:attachSource)
propagates(dn:isSelectionOf, duty odrl:attribute)
Example: Wedge
21
Adjustment
… dn:sameCapabilityAs does *not* propagate all the policies
listed in this concept. We Wedge dn:sameIdentityAs
Evaluation
22
As a result we obtained: 3865 rules in total, 78
concepts, 2817 rules abstracted and 1048
rules remaining - for a compression factor of
0.729.
Thanks to this methodology we have been
able to fix many errors in the initial data, and to
refine Datanode by clarifying the semantics of
many properties and adding new ones.
Conclusions
23
• We presented a method to abstract Policy Propagation Rules
applying an ontology, the Datanode ontology, a hierarchical
organisation of the possible relations between data objects.
• Datanode allowed us to reduce the number of rules to a factor of 0.5.
• Applying the ontology and the method we were able to find and correct
errors in the rules.
• Moreover we have been able to analyse the ontology in relation to this
task and enhance it having as result not only a further reduction of rules
- 0.7, but also a better ontology.
Future work
• Apply the rule base to specific use cases (perform a practical
evaluation), and publish it for reuse.
• Extend the Assessment phase of the methodology to also include
consistency check between the hierarchy of the FCA lattice and the
ontology.
• Define additional operations to support the Adjustment phase.
• Study the rule base evolution via continuous integration of new
policies/actions/rules.
• Apply the methodology to other use cases. Should we integrate the
measures and operations of the methodology in the Contento tool?
24
Thank you
Enrico Daga
Twitter: @enridaga
enrico.daga@open.ac.uk
76Bottom-Up Ontology Construction with CONTENTO
http://bit.ly/contento-tool
Contento supports the user in the generation and curation of concept lattices to
produce Semantic Web Ontologies from data.
In the demo, we show how to use CONTENTO with Linked Data.
Formal
Context
Concept
Lattice
Modeling
(Naming &
Pruning)
SPARQL
Export as
OWL
Ontology
(adver2sement)
@ISWC	
  2015	
  Poster	
  and	
  Demo	
  session,	
  next	
  week…
Operations: Fill
27
Annex
Observation: the branch is close to be fully in the concept (high Pre)
Diagnosis: the branch must be fully in the concept…
Effect: The Fill operation makes a branch be fully in a cluster of concept
c, attempting to push Pre up to 1.
Operations: Merge
28
Annex
Observation: two distinct branches match the same concept
Diagnosis: the respective top relations can be abstracted by a new
common relation
Effect: a new relation is added to the ontology
Operations: Group
29
Annex
Observation: A set of relations are all together in the extent of a concept,
but belong to different branches.
Diagnosis: they are actually related, and a common parent is missing.
Effect: a new relation is added to the ontology.
Operations: Wedge
30
Annex
Observation: the top relation is missing, and it does not propagate (all)
the policies of the concept.
Diagnosis: the actual top relation is not a right abstraction.
Effect: a new relation is added to the ontology.

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Propagation of Policies in Rich Data Flows

  • 1. Propagation of Policies in Rich Data Flows 1 Enrico Daga†  Mathieu d’Aquin†  Aldo Gangemi‡  Enrico Motta†     †  Knowledge  Media  Ins2tute,  The  Open  University  (UK)   ‡  Université  Paris13  (France)  and  ISTC-­‐CNR  (Italy)   The  8th  Interna2onal  Conference  on  Knowledge  Capture  (K-­‐CAP  2015)   October  10th,  2015  -­‐  Palisades,  NY  (USA)   hRp://www.k-­‐cap2015.org/ Feedback  welcome:  @enridaga  #kmiou
  • 2. Motivation • Governing the life cycle of data on the web is a challenging issue for organisations and users. • Assessing what policies on input data propagate to the output of a process is a crucial problem. 2
  • 3. Policies 3 Foundations Constraints and permissions set by the data owner regarding the reuse of the data, ie. mostly licences. We can describe licences/policies (RDF+ODRL). RDF License Database http://datahub.io/dataset/rdflicense Describes ~140 licenses using RDF and the Open Digital Rights Language (ODRL). Reports 113 policies. lic:cc-by-nc4.0 a odrl:Policy ; rdfs:label "CC-BY-NC" ; odrl:permission [ odrl:action cc:Distribution, ldr:extraction , ldr:reutilization , cc:DerivativeWorks , cc:Reproduction ; odrl:duty [ odrl:action cc:Attribution , cc:Notice ] ] ; odrl:prohibition [ odrl:action cc:CommercialUse ]
  • 4. Objective We need to understand how these policies propagate in data flows! 4
  • 5. Data flows 5 Foundations What are the semantic relations between input and output? Data flows can be thought as graphs of data objects. Datanode is an ontology for data centric description of applications. Built as hierarchy of relations. More then 100 relations so far. http://purl.org/datanode/ns/ http://purl.org/datanode/docs/
  • 6. Policy Propagation Rule (PPR) 6 Relying on Datanode for an enumeration of the possible relations and on the RDF License Database for the possible policies, we can setup rules like the following: A Horn clauses of the form: propagates(odrl:duty cc:Attribution, dn:isCopyOf) For example: It can be simplified as: Foundations
  • 7. Problem A description of policies and data flows implies a huge number of Policy Propagation Rules to be specified and computed (number of possible policies times number of possible relations between data objects). How to abstract this KB to make the management and reasoning on policy propagation rules easier? 7
  • 8. Contributions (1) A methodology to obtain an abstraction that allows to reduce the number of rules significantly (using an Ontology). (2) Evaluate how effective this methodology is when using the Datanode ontology. (3) Demonstrate how this ontology can evolve in order to better represent the behaviour of Policy Propagation Rules. 8
  • 9. (A)AAAA Methodology 9 1.Aquire rules 2.Analyse the rules: find clusters using Formal Concept Analysis 3.Abstract the rules: match clusters & ontology hierarchy 4.Assess the compression, and diagnose errors or refinements in the rules or the ontology 5.Adjust the rules or the ontology repeat from 2.
  • 10. Preparing the Rules Base • This phase required a manual supervision of all associations between policies and relations in order to establish the initial set of propagation rules. • We used Contento, it was possible to prepare manually the rule base with a reasonable effort. • The initial knowledge base was then composed of 3363 Policy Propagation Rules 10 Acquisition
  • 11. Formal Concept Analysis (FCA) • Input is a Formal Context (a binary matrix of objects/attributes) • Basic unit is a Close Concept: – (O,A) => (Extension,Intension) – Closure operator ’ … (O,A) is a concept when O’=A and A’=O • Classifies concepts hierarchically in a concept lattice – Top: all objects, no attr, bottom: all attributes, no obj 11 Analysis
  • 12. Applying FCA to the Rule Base 80 concepts Clusters of rules Relations that have a common behaviour: they propagate the same policies. But why do they do it? 12 Analysis
  • 13. Detect matches with the ontology 13 Abstraction Search for matches between concepts and branches taken from the ontology hierarchy. When found, subtract the rules from the KB accordingly.
  • 14. Example/1 14 Abstraction dn:hasPart and dn:isVocabularyOf are valid abstractions dn:isPartOf is not, because dn:isSelectionOf apparently does not propagate (all) the policies…
  • 15. Example/2 15 Abstraction dn:hasPart (49 rules) propagates(dn:hasPart,duty  cc:ARribu2on)   propagates(dn:hasPart,duty  cc:Copyle^)   propagates(dn:hasPart,duty  cc:No2ce)   propagates(dn:hasPart,duty  cc:SourceCode)   propagates(dn:hasPart,duty  odrl:aRachPolicy)   propagates(dn:hasPart,duty  odrl:aRachSource)   propagates(dn:hasPart,duty  odrl:aRribute) propagates(dn:hasSec2on,duty  cc:ARribu2on)   propagates(dn:hasSec2on,duty  cc:Copyle^)   propagates(dn:hasSec2on,duty  cc:No2ce)   propagates(dn:hasSec2on,duty  cc:SourceCode)   propagates(dn:hasSec2on,duty  odrl:aRachPolicy)   propagates(dn:hasSec2on,duty  odrl:aRachSource)   propagates(dn:hasSec2on,duty  odrl:aRribute)   propagates(dn:hasSelec2on,duty  cc:ARribu2on)   propagates(dn:hasSelec2on,duty  cc:Copyle^)   propagates(dn:hasSelec2on,duty  cc:No2ce)   propagates(dn:hasSelec2on,duty  cc:SourceCode)   propagates(dn:hasSelec2on,duty  odrl:aRachPolicy)   propagates(dn:hasSelec2on,duty  odrl:aRachSource)   propagates(dn:hasSelec2on,duty  odrl:aRribute)   propagates(dn:hasSample,duty  cc:ARribu2on)   propagates(dn:hasSample,duty  cc:Copyle^)   propagates(dn:hasSample,duty  cc:No2ce)   propagates(dn:hasSample,duty  cc:SourceCode)   propagates(dn:hasSample,duty  odrl:aRachPolicy)   propagates(dn:hasSample,duty  odrl:aRachSource)   propagates(dn:hasSample,duty  odrl:aRribute)   propagates(dn:hasPor2on,duty  cc:ARribu2on)   propagates(dn:hasPor2on,duty  cc:Copyle^)   propagates(dn:hasPor2on,duty  cc:No2ce)   propagates(dn:hasPor2on,duty  cc:SourceCode)   propagates(dn:hasPor2on,duty  odrl:aRachPolicy)   propagates(dn:hasPor2on,duty  odrl:aRachSource)   propagates(dn:hasPor2on,duty  odrl:aRribute)   propagates(dn:hasIden2fiers,duty  cc:ARribu2on)   propagates(dn:hasIden2fiers,duty  cc:Copyle^)   propagates(dn:hasIden2fiers,duty  cc:No2ce)   propagates(dn:hasIden2fiers,duty  cc:SourceCode)   propagates(dn:hasIden2fiers,duty  odrl:aRachPolicy)   propagates(dn:hasIden2fiers,duty  odrl:aRachSource)   propagates(dn:hasIden2fiers,duty  odrl:aRribute)   propagates(dn:hasExample,duty  cc:ARribu2on)   propagates(dn:hasExample,duty  cc:Copyle^)   propagates(dn:hasExample,duty  cc:No2ce)   propagates(dn:hasExample,duty  cc:SourceCode)   propagates(dn:hasExample,duty  odrl:aRachPolicy)   propagates(dn:hasExample,duty  odrl:aRachSource)   propagates(dn:hasExample,duty  odrl:aRribute) propagates(dn:isVocabularyOf,duty  cc:ARribu2on)   propagates(dn:isVocabularyOf,duty  cc:Copyle^)   propagates(dn:isVocabularyOf,duty  cc:No2ce)   propagates(dn:isVocabularyOf,duty  cc:SourceCode)   propagates(dn:isVocabularyOf,duty  odrl:aRachPolicy)   propagates(dn:isVocabularyOf,duty  odrl:aRachSource)   propagates(dn:isVocabularyOf,duty  odrl:aRribute) propagates(dn:aRributesOf,duty  cc:ARribu2on)   propagates(dn:aRributesOf,duty  cc:Copyle^)   propagates(dn:aRributesOf,duty  cc:No2ce)   propagates(dn:aRributesOf,duty  cc:SourceCode)   propagates(dn:aRributesOf,duty  odrl:aRachPolicy)   propagates(dn:aRributesOf,duty  odrl:aRachSource)   propagates(dn:aRributesOf,duty  odrl:aRribute)   propagates(dn:datatypesOf,duty  cc:ARribu2on)   propagates(dn:datatypesOf,duty  cc:Copyle^)   propagates(dn:datatypesOf,duty  cc:No2ce)   propagates(dn:datatypesOf,duty  cc:SourceCode)   propagates(dn:datatypesOf,duty  odrl:aRachPolicy)   propagates(dn:datatypesOf,duty  odrl:aRachSource)   propagates(dn:datatypesOf,duty  odrl:aRribute)   propagates(dn:descriptorsOf,duty  cc:ARribu2on)   propagates(dn:descriptorsOf,duty  cc:Copyle^)   propagates(dn:descriptorsOf,duty  cc:No2ce)   propagates(dn:descriptorsOf,duty  cc:SourceCode)   propagates(dn:descriptorsOf,duty  odrl:aRachPolicy)   propagates(dn:descriptorsOf,duty  odrl:aRachSource)   propagates(dn:descriptorsOf,duty  odrl:aRribute)   propagates(dn:typesOf,duty  cc:ARribu2on)   propagates(dn:typesOf,duty  cc:Copyle^)   propagates(dn:typesOf,duty  cc:No2ce)   propagates(dn:typesOf,duty  cc:SourceCode)   propagates(dn:typesOf,duty  odrl:aRachPolicy)   propagates(dn:typesOf,duty  odrl:aRachSource)   propagates(dn:typesOf,duty  odrl:aRribute)   propagates(dn:rela2onsOf,duty  cc:ARribu2on)   propagates(dn:rela2onsOf,duty  cc:Copyle^)   propagates(dn:rela2onsOf,duty  cc:No2ce)   propagates(dn:rela2onsOf,duty  cc:SourceCode)   propagates(dn:rela2onsOf,duty  odrl:aRachPolicy)   propagates(dn:rela2onsOf,duty  odrl:aRachSource)   propagates(dn:rela2onsOf,duty  odrl:aRribute) dn:isVocabularyOf (42 rules) 7 rules! 7 rules!
  • 16. Compression Factor 16 Assessment By applying the original Datanode ontology, 1925 rules could be removed out of 3363, for a compression factor of 0.572. We calculate the CF as the number of abstracted rules divided by the total number of rules:
  • 17. Considerations 17 Assessment 2. The Datanode ontology has not been designed for the purpose of representing a common behaviour of relations in terms of propagation of policies. It is possible to refine the ontology in order to make it cover this use case better (and possibly reduce the number of rules even more). 1. The size of the matrix that was manually supervised is large, and it is possible that errors have been made at that stage of the process.
  • 18. Observing the measures 18 Assessment concepts * relations=~9040 (partial) matches. Hard to explore! Inspecting a partial match with high precision and low recall highlights a problem that might be easy to fix, as the number of relations and policies to compare will be low.
  • 19. Operations 19 Adjustment We try to make adjustments in order to improve the compression factor. We defined a set of operations, targeted to (a) fix errors in the initial rule base and (b) refine the ontology. • Fill: makes a branch be fully in a cluster of concept c, attempting to push Pre up to 1. • Group: some relations that share a concept, but belong to different branches, are abstracted by a new relation. • Merge: two distinct branches are abstracted by a new relation. • Wedge: change the top relation of a branch to make it fully match the concept. (and we run our process again from the Analysis phase to the Assessment)
  • 20. Example: Fill 20 Adjustment … dn:isSelectionOf should indeed propagate all the policies listed in this concept. Fill adds the following rules: propagates(dn:isSelectionOf, duty cc:Attribution) propagates(dn:isSelectionOf, duty cc:Copyleft) propagates(dn:isSelectionOf, duty cc:Notice) propagates(dn:isSelectionOf, duty cc:SourceCode) propagates(dn:isSelectionOf, duty odrl:attachPolicy) propagates(dn:isSelectionOf, duty odrl:attachSource) propagates(dn:isSelectionOf, duty odrl:attribute)
  • 21. Example: Wedge 21 Adjustment … dn:sameCapabilityAs does *not* propagate all the policies listed in this concept. We Wedge dn:sameIdentityAs
  • 22. Evaluation 22 As a result we obtained: 3865 rules in total, 78 concepts, 2817 rules abstracted and 1048 rules remaining - for a compression factor of 0.729. Thanks to this methodology we have been able to fix many errors in the initial data, and to refine Datanode by clarifying the semantics of many properties and adding new ones.
  • 23. Conclusions 23 • We presented a method to abstract Policy Propagation Rules applying an ontology, the Datanode ontology, a hierarchical organisation of the possible relations between data objects. • Datanode allowed us to reduce the number of rules to a factor of 0.5. • Applying the ontology and the method we were able to find and correct errors in the rules. • Moreover we have been able to analyse the ontology in relation to this task and enhance it having as result not only a further reduction of rules - 0.7, but also a better ontology.
  • 24. Future work • Apply the rule base to specific use cases (perform a practical evaluation), and publish it for reuse. • Extend the Assessment phase of the methodology to also include consistency check between the hierarchy of the FCA lattice and the ontology. • Define additional operations to support the Adjustment phase. • Study the rule base evolution via continuous integration of new policies/actions/rules. • Apply the methodology to other use cases. Should we integrate the measures and operations of the methodology in the Contento tool? 24
  • 25. Thank you Enrico Daga Twitter: @enridaga enrico.daga@open.ac.uk
  • 26. 76Bottom-Up Ontology Construction with CONTENTO http://bit.ly/contento-tool Contento supports the user in the generation and curation of concept lattices to produce Semantic Web Ontologies from data. In the demo, we show how to use CONTENTO with Linked Data. Formal Context Concept Lattice Modeling (Naming & Pruning) SPARQL Export as OWL Ontology (adver2sement) @ISWC  2015  Poster  and  Demo  session,  next  week…
  • 27. Operations: Fill 27 Annex Observation: the branch is close to be fully in the concept (high Pre) Diagnosis: the branch must be fully in the concept… Effect: The Fill operation makes a branch be fully in a cluster of concept c, attempting to push Pre up to 1.
  • 28. Operations: Merge 28 Annex Observation: two distinct branches match the same concept Diagnosis: the respective top relations can be abstracted by a new common relation Effect: a new relation is added to the ontology
  • 29. Operations: Group 29 Annex Observation: A set of relations are all together in the extent of a concept, but belong to different branches. Diagnosis: they are actually related, and a common parent is missing. Effect: a new relation is added to the ontology.
  • 30. Operations: Wedge 30 Annex Observation: the top relation is missing, and it does not propagate (all) the policies of the concept. Diagnosis: the actual top relation is not a right abstraction. Effect: a new relation is added to the ontology.