Dynamic%20Ontology%20Matching

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Dynamic Ontology Matching
Pavel Shvaiko
OpenKnowledge meetings 9 February, 13 March, 2006
Trento, Italy
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Introduction (Trento view)
Information sources (e.g., catalogs) can be
viewed as graph-like structures containing terms
and their inter-relationships
Matching takes two graph-like structures and
produces a mapping between the nodes of the
graphs that correspond semantically to each other
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P2P scenario (match-oriented view)
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P2P scenario more details

• Peers are autonomous
• They appear and disappear on the network
• They use different terminology
• Matching (on-the-fly)
• Determine the relationships between peer schemas
• Use these relationships for query answering
• An assumption that all peers rely on one global
  schema, as in data integration, can not be
  made, because the global schema might need to be
  updated any time the system evolves

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Requirements

• Input size of ontologies
• At most 100 entinties per ontology
• Domains of interest
• Bioinformatics and GIS Emergency response
• Matching Performance
• At most 2 seconds per matching task
• Memory limit 256Mb
• Matching Quality
• Mistakes are acceptable

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Discussion - I

• Input
• OWL, RDF, XML
• Will the instances be available?
• Quality/charachteristics of entities
• Partial vs Complete ontology matching
• Perhaps we might not need to have a complete
  alignment to answer a query
• Quality/Efficiency trade off
• QOM example
• Online vs Offline vs Mixed match and QA

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Discussion - II

• What is in the alignment ?
• 1-1, 1-n, n-m
• Is any relation suitable?
• Output format
• Test cases
• The sooner we have them, the better
• Matching quality measures
• User/task related measures
• What is more important in the application
• Precision or recall or both?

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Discussion - III

• Alignment negotiation
• Explanation and argumentation

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A comparison of techniques for dynamic ontology
matching

1. Introduction 2p All
2. The dynamic ontology matching problem 19p Pavel
3. P2P information management systems 3p Ilya
4. Motivating scenarios (2 our applications) 12p
   MaurizioMarco Marta?
5. Requirements (functional vs non-functional)
   2pPavel Marta?
6. Problem statement 2p PavelIlya
7. why is it different from previous works
8. A conceptual basis for comparison of dynamic
   matching techniques 13p Marco
9. The framework taxonomy 43p
   MarcoPavelMikalai
10. Ontology matching (standard) 3pPavelMikalai
11. Plausible DOM methods (transitivity)
    3pPavelMikalai
12. Systems and evaluation 10p Mikalai
13. State of the art prototypes 2pPavel
14. Evaluation methodology 3pMikalai
15. Comarative evaluation results 5pMikalai
16. Discussion/Open Issues and challenges towards DOM
    3p All
17. Conclusions 2p

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A comparison of techniques for dynamic ontology
matching

• Index solid Feb 17 (DONE)
• Parallel 2,3,4 March 10 (DONE)
• Use case details of what should be matched by
  Fiona (March 22)
• A first draft (circulated to partners) April 5
• Trento
• Feedback by April 19
• Second draft (circulated to parnters)
• Barcelona
• Feedback by
• Final version by mid May?
• Trento

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Motivating scenarios (P2P 2 our applications)

• Intuitive description (environment, actors,
  operations for the system) 1p
• Requirements (Tropos) 2p 1 fig
• domain description (Peers and peer goals)
• use case
• QA (functional requirements)
• Measure quality (GEA) ?Trade quality for speed?
• Transitivity in GEA
• Logical architecture (organization of users and
  C/S Ps) 1p1fig
• Physical architecture (bioinformaticslogical
  arcitechture)
• Non-functional requirements 1p P2P
• Number of peers and connectivity
• Size and shape of ontologies/data
• Run-time vs offline time response, mixed
  initiative
• Memory limit (256 mb)

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Conceptual Framework (Marco-gtMarch) m6

1. Introduction
2. P2P
3. P2P information management systems
4. Motivating examples
5. Basic notation, terminology
6. Ontology Matching
7. Running examples (semantic matching IF-MAP)
8. DOM
9. Dynamics (peers, ontologies, )
10. Transitivity (compositionality of mappings and
    queries)
11. The basic theorem
12. DOM interaction model
13. Formalizing motivating examples

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Methodological Framework (Trento-gtMarch)

• Routing / Navigation / Search Ilya Maxym m6
• Basic operations
• Node matching
• Navigation
• Query answering (substeps rewriting)
• Composite operations
• Matching
• QA
• Interaction models for the above 2
• Two case studies
• Approximation / Quality m12
• ???

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A potential example of DOM - 1
Source Bin He
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A potential example of DOM - 2
Source Bin He
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DOM open questions

• What do we technically mean by dynamic? ontology
  matching
• Business cases technical use cases
• Technically, what do we match in our scenarios?
• Messages between agents
• Functionalities of web services
• Classifications/Ontologies