Slides presented in the KR conference 2016, Cape Town, South Africa. Authors: Ernesto Jiménez-Ruiz, Terry R. Payne, Alessandro Solimando, Valentina A. M. Tamma

1. Limiting Logical
Violations in Ontology
Alignment Through
Negotiation
Ernesto Jim´enez-Ruiz, Terry R. Payne,
Alessandro Solimando, Valentina Tamma
KR Conference, Cape Town, April 27, 2016
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2. Outline
Introduction
Preliminaries about Ontology Alignment
Correspondence Inclusion Dialogue
Evaluation
Conclusions and future work
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3. Our approach in a nutshell
We have extend an existing decentralised approach to compute
ontology alignments:
agents engage in a dialogue to exchange details of possible
correspondences (Payne and Tamma 2014), and
identify and eliminate alignments that could yield to logical (i.e.
conservativity and consistency) violations (Solimando and
Jimenez-Ruiz et al. 2014).
(Payne and Tamma 2014) Negotiating over ontological
correspondences with asymmetric and incomplete knowledge.
AAMAS 2014
(Solimando and Jimenez-Ruiz et al. 2014). Detecting and
Correcting Conservativity Principle Violations in
Ontology-to-Ontology Mappings. ISWC 2014
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4. Our approach in a nutshell
We have extend an existing decentralised approach to compute
ontology alignments:
agents engage in a dialogue to exchange details of possible
correspondences (Payne and Tamma 2014), and
identify and eliminate alignments that could yield to logical (i.e.
conservativity and consistency) violations (Solimando and
Jimenez-Ruiz et al. 2014).
(Payne and Tamma 2014) Negotiating over ontological
correspondences with asymmetric and incomplete knowledge.
AAMAS 2014
(Solimando and Jimenez-Ruiz et al. 2014). Detecting and
Correcting Conservativity Principle Violations in
Ontology-to-Ontology Mappings. ISWC 2014
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5. Our approach in a nutshell
We have extend an existing decentralised approach to compute
ontology alignments:
agents engage in a dialogue to exchange details of possible
correspondences (Payne and Tamma 2014), and
identify and eliminate alignments that could yield to logical (i.e.
conservativity and consistency) violations (Solimando and
Jimenez-Ruiz et al. 2014).
(Payne and Tamma 2014) Negotiating over ontological
correspondences with asymmetric and incomplete knowledge.
AAMAS 2014
(Solimando and Jimenez-Ruiz et al. 2014). Detecting and
Correcting Conservativity Principle Violations in
Ontology-to-Ontology Mappings. ISWC 2014
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6. Motivation: why ontology alignment?
Autonomous agents (e.g. sensors, devices, services) rely on
internal world models (e.g. ontology)
Ontological models may differ among agents
Typical scenario in dynamic and opportunistic scenarios (e.g.,
e-commerce, open-data or mobile systems)
Agents need to exchange knowledge
An integration phase is necessary to reconcile different views of
the world
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7. Motivation: why ontology alignment?
Autonomous agents (e.g. sensors, devices, services) rely on
internal world models (e.g. ontology)
Ontological models may differ among agents
Typical scenario in dynamic and opportunistic scenarios (e.g.,
e-commerce, open-data or mobile systems)
Agents need to exchange knowledge
An integration phase is necessary to reconcile different views of
the world
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8. Motivation: why decentralised approach?
Traditional approaches are centralised. One of the agents. . .
(or a third party) generates the alignments
has full access to both ontologies
Fragments of the ontological space may be confidential or
commercially sensitive
Disclosure of the full ontology no longer possible
Need to collaborate / negotiate to establish the alignment
Agents are self-interested, but also collaborative
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9. Motivation: why decentralised approach?
Traditional approaches are centralised. One of the agents. . .
(or a third party) generates the alignments
has full access to both ontologies
Fragments of the ontological space may be confidential or
commercially sensitive
Disclosure of the full ontology no longer possible
Need to collaborate / negotiate to establish the alignment
Agents are self-interested, but also collaborative
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10. Motivation: why decentralised approach?
Task-driven ontology alignment
An agent may aggregate several ontologies for a variety of
domains
A task may be relevant to only a (probably small) fragment
within an ontology
Minimize disclosure of knowledge (and maximizing utility)
We focus on a specific signature relevant to an encounter
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11. Motivation: why decentralised approach?
Task-driven ontology alignment
An agent may aggregate several ontologies for a variety of
domains
A task may be relevant to only a (probably small) fragment
within an ontology
Minimize disclosure of knowledge (and maximizing utility)
We focus on a specific signature relevant to an encounter
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12. Motivation: why using logical-based methods?
Previous decentralised approaches solved ambiguity in the
alignment by applying the Stable Marriage and Hungarian
algorithms. Although effective, these algorithms. . .
× can also prune out valid alignments
× cannot guarantee that the final alignment does not lead to errors
Logical violations may compromise the information exchange
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13. Motivation: why using logical-based methods?
Previous decentralised approaches solved ambiguity in the
alignment by applying the Stable Marriage and Hungarian
algorithms. Although effective, these algorithms. . .
× can also prune out valid alignments
× cannot guarantee that the final alignment does not lead to errors
Logical violations may compromise the information exchange
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14. Assumptions: initial state
Each agent commits to a private ontology:
sender’s ontology: Ox
recipient’s ontology: Oˆx
Σx and Σˆx are the public signatures for that encounter
The agents had acquired correspondences from past encounters
which are stored in their Correspondence Stores: ∆x and ∆ˆx
Agent correspodences are initially private
Each agent associated some weight/utility to each known
correspondence
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15. Assumptions: initial state
Each agent commits to a private ontology:
sender’s ontology: Ox
recipient’s ontology: Oˆx
Σx and Σˆx are the public signatures for that encounter
The agents had acquired correspondences from past encounters
which are stored in their Correspondence Stores: ∆x and ∆ˆx
Agent correspodences are initially private
Each agent associated some weight/utility to each known
correspondence
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16. Assumptions: initial state
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17. Outline
Introduction
Preliminaries about Ontology Alignment
Correspondence Inclusion Dialogue
Evaluation
Conclusions and future work
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18. Alignment between Ontologies
An alignment A is a set of a triples (i.e. correspondences)
c = e, e , r such that:
e ∈ Σx
, e ∈ Σˆx
r is the semantic relationship between e and e (e.g.
subsumption or equivalence)
Formalized as OWL 2 axioms
Where the semantic relationship r is one of {≡, , }
No extra semantics
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19. Alignment between Ontologies
An alignment A is a set of a triples (i.e. correspondences)
c = e, e , r such that:
e ∈ Σx
, e ∈ Σˆx
r is the semantic relationship between e and e (e.g.
subsumption or equivalence)
Formalized as OWL 2 axioms
Where the semantic relationship r is one of {≡, , }
No extra semantics
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20. Alignment leading to violations
An alignment A violates the consistency principle w.r.t Ox
and Oˆx if:
there exists a class A ∈ Σ(Ox
) ∪ Σ(Oˆx
) such that
Ox
∪ Oˆx
∪ A |= A ⊥, and
Ox
∪ Oˆx
|= A ⊥.
An alignment A violates the conservativity principle w.r.t Ox
and Oˆx if:
there exists an axioms A B with A, B belonging to Σ(Ox
)
(resp. Σ(Oˆx
)) such that
Ox
∪ Oˆx
∪ A |= A B, and
Ox
|= A B (resp. Oˆx
|= A B).
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21. Alignment repair
R is a repair for A wrt Ox and Oˆx
if A R does not lead to violations
R = A is a candidate repair
R is a diagnosis for A wrt Ox and Oˆx
if R ⊂ R is not a repair for A
Diagnosis are (typically) expensive to obtain
(*) Note that an alignment repair R may suggest splitting an
equivalence correspondence (≡) into or
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22. Alignment repair
R is a repair for A wrt Ox and Oˆx
if A R does not lead to violations
R = A is a candidate repair
R is a diagnosis for A wrt Ox and Oˆx
if R ⊂ R is not a repair for A
Diagnosis are (typically) expensive to obtain
(*) Note that an alignment repair R may suggest splitting an
equivalence correspondence (≡) into or
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23. Computing alignment repairs
Standard justification-based ontology debugging
are expensive since require complete reasoning
do not scale when the number of violations is large
(Solimando and Jimenez-Ruiz et al. 2015)
An approximate Repair R≈
will not guarantee that A R≈
is violations free
but aims at reducing the number of violations
while preserving A as much as possible
(Solimando and Jimenez-Ruiz et al. 2015). On the Feasibility of
Using OWL 2 Reasoners in Ontology Alignment Repair Problems.
ORE 2015
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24. Computing alignment repairs
Standard justification-based ontology debugging
are expensive since require complete reasoning
do not scale when the number of violations is large
(Solimando and Jimenez-Ruiz et al. 2015)
An approximate Repair R≈
will not guarantee that A R≈
is violations free
but aims at reducing the number of violations
while preserving A as much as possible
(Solimando and Jimenez-Ruiz et al. 2015). On the Feasibility of
Using OWL 2 Reasoners in Ontology Alignment Repair Problems.
ORE 2015
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25. Computing alignment repairs
Our approach. . .
projects the input ontologies into Horn propositional and graph
encodings
relies on a SAT solver for propositional Horn logic, and a
combination of graph theory and logic programming
is sound but incomplete (e.g. some violations may be
misreported)
(Solimando and Jimenez-Ruiz et al. 2016). Minimizing
Conservativity Violations in Ontology Alignments: Algorithms and
Evaluation. Submitted to a Journal. 2016
(Solimando and Jimenez-Ruiz et al. 2014). Detecting and
Correcting Conservativity Principle Violations in
Ontology-to-Ontology Mappings. ISWC 2014
(Jimenez-Ruiz and Cuenca Grau 2011). LogMap: Logic-based and
Scalable Ontology Matching. ISWC 2011
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26. Outline
Introduction
Preliminaries about Ontology Alignment
Correspondence Inclusion Dialogue
Evaluation
Conclusions and future work
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27. The dialogue in a nutshell
Dialogue is made of a sequence of moves
Agents take turns to assert correspondences (c) and its
associated weight
Admissible responses to an assertions are:
confirm the admissibility of c
propose a possible repair to A ∪ c
reject the assertion of c
Correspondences must have a joint weight greater than an
agreed admissibility-threshold
Accepted correspondences are stored in A
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28. The dialogue state diagram
The proponent (P) initiates the dialogue with the opponent (O)
Both O and P can play the role of sender x and recipient ˆx
assert
rejectC
acceptC
repair
acceptR
P
S5
O
S8
P
S3
O
S4
P
S7
O
S6
rejectR
assert
rejectC
acceptCrepair
acceptR
rejectR
join
matched-close
join
O
S2
P
S1
matched-close
close
close
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29. Aggregating weights and the upper bound
The function joint : C → [0, 1] returns the joint weight for
some correspondence c
Correspondence with the highest weight κx
c are proposed first
Correspondences with a weight lower than the admissibility
threshold, , are filtered
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30. Correspondence repair dialogue example
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31. Correspondence repair dialogue results
Improvement wrt original CID dialogue
One correspondence less than the centralized approach
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32. Outline
Introduction
Preliminaries about Ontology Alignment
Correspondence Inclusion Dialogue
Evaluation
Conclusions and future work
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33. Empirical evaluation
Experiments with the Ontology Evaluation Alignment Initiative
(OAEI) Conference track dataset
21 ontology pairs with reference alignment
18 ontology alignment systems (i.e. source of alignments)
Each experiments included:
1 ontology pair (one ontology for each agent)
1 alignment (generated by a system)
Each agents assigned a weight to each correspondence
according to a probabilistic function
Baseline: randomly picking up an alignment generated by a
system
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34. Empirical evaluation: results
Delta F-score between
the average (for the 18 systems) F-score using the dialogue
the average F-score of the baseline.
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35. Completeness of the alignment repair methods
Comprehensive testing with OAEI datasets and participating
systems
Our incomplete techniques removed more than 99% of the
violations
The consistency repair algorithm removed up to 11% of the
original correspondences
Negligible impact on F-score
The conservativity repair algorithm removed up to 22% of the
original correspondences
(Solimando and Jimenez-Ruiz et al. 2016). Minimizing
Conservativity Violations in Ontology Alignments: Algorithms and
Evaluation. Submitted to a Journal. 2016
(Jimenez-Ruiz et al. 2013). Evaluating Mapping Repair Systems
with Large Biomedical Ontologies. DL 2013
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36. Outline
Introduction
Preliminaries about Ontology Alignment
Correspondence Inclusion Dialogue
Evaluation
Conclusions and future work
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37. Concluding remarks
Conservativity violations as false positives?
Due to errors or incompleteness in the ontologies
In some scenarios the ontologies are not modifiable
Our repair strategy follows a “better safe than sorry” approach
Future work:
Allow the disclosure of some additional knowledge about the
(private) ontologies
Extend the dialogue to consider scenarios involving more than
two agents
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38. Concluding remarks
Conservativity violations as false positives?
Due to errors or incompleteness in the ontologies
In some scenarios the ontologies are not modifiable
Our repair strategy follows a “better safe than sorry” approach
Future work:
Allow the disclosure of some additional knowledge about the
(private) ontologies
Extend the dialogue to consider scenarios involving more than
two agents
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39. Concluding remarks
Conservativity violations as false positives?
Due to errors or incompleteness in the ontologies
In some scenarios the ontologies are not modifiable
Our repair strategy follows a “better safe than sorry” approach
Future work:
Allow the disclosure of some additional knowledge about the
(private) ontologies
Extend the dialogue to consider scenarios involving more than
two agents
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40. Questions?
Thank you for your attention
ernesto.jimenez.ruiz@gmail.com
ernesto@cs.ox.ac.uk
Acknowledgements:
Bernardo Cuenca Grau
Bijan Parsia
Giovanna Guerrini
Ian Horrocks
J´erˆome Euzenat
EU project Optique and the EPSRC project DBOnto.
Visiting fellowship (University of Liverpool)
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