PART IV: REPRESENTING, EXPLAINING, AND PROCESSING ALIGNMENTS & PART V: CONCLUSIONS Ontology Matching Jerome Euzenat and Pavel Shvaiko

PART IV:REPRESENTING, EXPLAINING,

AND PROCESSING ALIGNMENTS&

PART V:CONCLUSIONS

Ontology Matching

Jerome Euzenat and Pavel Shvaiko

2

Overview

Alignments Representing alignments

Formants Frameworks Editors

Explaining alignments Justifications Explanations Arguments

Processing alignments Conclusions

3

Representing Alignments

MAFRA Semantic bridge ontology (SBO) Provides a Semantic Bridge Ontology

Entities to be mapped are identified within the ontology (instances) through a path

Mapping = Bridges + Constraints + Information on Ontologies

Example

Alignment formats

4


OWL Language for expressing correspondences

between ontologies Example

Alignment formats

5


Contextualized OWL (C-OWL) Extension of OWL to express mappings between

heterogeneous ontologies Bridge rules are oriented correspondences, from a

source to a target ontology Example

Alignment formats

6


SWRL (Semantic Web Rule Language) Extension of OWL with an explicit notion of

rules Rules are interpreted as first order Horn clauses

Example

Alignment formats

“Whenever the conditions in

the body hold, then the

conditions in the head must

also hold”

7


Alignment format Simple alignment representation that

handles complex alignment definitions Example

Alignment formats

Correspondence

Strength

Relation type

Level

Type

8


SEKT mapping language The alignments can be expressed in a human-

readable language and with the help of an RDF vocabulary

Example

Alignment formats

Equivalence

Equivalence +

Constraint

9


SKOS (Simple Knowledge Organization System) Use to express relationships between lightweight

ontologies, e.g., folksonomies or thesauri Its goal is to be a layer on top of other formalisms able

to express the links between entities in these formalisms It is currently under development

Example

Alignment formats

10


Comparison

Alignment formats- Summary

+ means that the system can be extended; Transf stands for transformation. The relations for the formats are subclass (sc), subproperty (sp), implication between formulas (imp). The terms concerned by the alignments can be classes (C), properties (P) or individuals (I).

11


There is no universal format for expressing alignments

The choice of a format depends on the characteristics of the application

To pick alignment formats consider 1. The expressiveness required for the

alignments2. The need to exchange with other applications

Especially if the applications involve different ontology languages

Alignment formats - Summary

12


Model management Provides metadata manipulation infrastructure to

reduce the amount of programming required to build metadata driven applications

Considers Models, which are information structures, e.g., XML

schema, or relational database schema Mappings are, which are oriented alignments from one

model into another Example

Alignment frameworks

13


COMA++ (University of Leipzig) Schema matching infrastructure built on top

of COMA Provides an extensible library of matching

algorithms, a framework for combining obtained results, and a platform for the evaluation of the effectiveness of the different matchers


14


MAFRA Interactive, incremental and dynamic

framework for mapping distributed ontologies Alignment API

A Java API is available for manipulating alignments in the Alignment format Defines a set of interfaces and a set of functions

that they can perform FOAM

Tool for processing similarity-based ontology matching


15


Ontology editors Edition environments which support matching

and importing ontologies Available editors

Chimaera: Browser-based environment for editing, merging and

testing large ontologies The Protégé Prompt Suite

Interactive framework for comparing, matching, merging, maintaining versions, and translating between different knowledge representation formalisms

KAON2 WSMX editor

Editors

16

Explaining Alignments

Matching systems may produce effective alignments that may not be intuitively obvious to human users For users to trust (and use) the alignments,

they need information about them E.g., users need access to the sources used to

determine semantic correspondences between ontology entities

Justifications

17


Justifications Each correspondence can be assigned one or

several justifications that support or infirm the correspondence Goal: explain why a correspondence should hold o not

Information included in a justification Basic matchers

Users need to understand where the information comes from, with different levels of detail

E.g.. external knowledge source (WordNet), reliability of the source

Process traces Users may want to see a trace of the performed

manipulations to yield the final alignment E.g.. trace of rules or strategies applied

Justifications

18


Explanation approaches Transform “justifications” into an understandable

explanation for each of the correspondences Goal: represent explanations in a simple and clear way Transformation requires:

Explanations

19


Approaches Proof presentation approach

Displays and explains proofs usually generated by semantic matchers

Strategic flow approach Explains to users the decision flow that capture

why some results are favored over other when a matcher is composed of other matchers

Argumentation approach Considers the justifications/arguments in

favor/against specific correspondences and explains which ones will hold

Explanations

20


A default explanation using S-Match

Explanations

Why S-Match suggested a set of documents stored under the node with label Europe in o as the result to the query – ‘find European pictures’?

21


Explaining basic matchers using S-Match

Explanations

Sources of background knowledge used to determine the correspondence

22


Explaining the matching process using iMAP

Explanations

Creation and flow for the correspondence month-posted = monthly-fee-rate

23


Arguing about correspondences Give arguments in favor/against the correspondences

1. Negotiating an alignment between two agents2. Achieving an alignment through matching, i.e., treat

alignments negotiation as an aggregation technique between two alignments

Example

Arguments

A1) all the known Company on the one side are Firm on the other side and vice versa;A2) the two names Company and Firm are synonyms in WordNet;

24

Processing Alignments

Processing alignment according to application needs Goal: determine how the alignments can be

specifically used by the applications

25


Ontology merging Goal: obtaining a new ontology o’’ from two matched

ontologies o and o’ so that the matched entities in o and o’ are related as prescribed by the alignment

Operations performed from alignments

26


Ontology transformation Goal: generating a new ontology o’’

expressing the entities of o with respect to those of o’ according to the correspondences in the alignment A

Not well supported by tools. It is useful when one wants to express one

ontology with regard to another one


27


Data translation Goal: translating instances from entities of

ontology o into instances of connected entities of matched ontology o’


28


Mediation Mediator as an independent software component

that is introduced between two other components in order to help them interoperate

Mediation

29

Alignment Service

Applications using ontology matching could benefit from sharing ontology matching techniques and results

It is useful to provide an alignment service able to store, retrieve and manipulate existing alignments as well as to generate new alignments on-the-fly Such a service

Would be shared by the applications using ontologies on the semantic web

Would require a standardization support, such as the choice of an alignment format or at least of metadata format

Service

30

Trends in the field

Increase awareness of the existing matching efforts across the relevant communities and facilitate the cross-fertilization between them

Conclusion

31

Future Challenges

Applications Basic techniques Matching strategies Matching systems Evaluation of matching systems

Pursue current efforts on extensive evaluation of ontology matching systems using benchmark datasets

Exploit evaluation results to help users in choosing the appropriate matching or combining multiple matchers for their tasks

Conclusion

32

Future Challenges

Representing alignments Establish one/two standard alignment formats for

exchanging the alignments Scalable alignment visualization techniques should

also be developed Explaining alignments

In order for matching systems to gain a wider acceptance, it will be necessary that they can provide arguments for their results to users or to other programs that use them. Explanation is thus an important challenge for ontology matching as well as user interfaces in general

Processing alignments

Conclusion

33

Final Words

For finding the correspondences between concepts, it is necessary to understand their meaning

The ultimate meaning of concepts is in the head of the people who developed those concepts and we cannot program a computer to learn it

Communication can be viewed as a continuous task of negotiating the relations between concepts, i.e., arguing about alignments, building new ones, questioning them, etc.

Matching ontologies is an on-going work and further substantial progress in the field can be made by considering communication in its dynamics

Conclusion

Documents

PART IV: REPRESENTING, EXPLAINING, AND PROCESSING ALIGNMENTS & PART V: CONCLUSIONS Ontology Matching Jerome Euzenat and Pavel Shvaiko