ontology mapping

Semantic Web, Ontology MappingSemantic Web, Ontology Mapping Fall 2005 Fall 2005

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Ontology MappingOntology Mapping

Elham PaikariElham [email protected]@ce.sharif.edu

Sharif University Of TechnologySharif University Of Technology

Computer Engineering DepartmentComputer Engineering Department


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AgendaAgenda The Role of OntologyThe Role of Ontology Ontology IntegrationOntology Integration About the ProblemAbout the Problem Ontology MismatchOntology Mismatch

Language Level MismatchesLanguage Level Mismatches Ontology Level MismatchesOntology Level Mismatches

Conceptualization MismatchConceptualization Mismatch Components Of MappingComponents Of Mapping Similarity CalculationSimilarity Calculation Further RefinementsFurther Refinements Path Length MeasurementPath Length Measurement EquivalenceEquivalence InteroperabilityInteroperability


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The Role of OntologyThe Role of Ontology The word ontology comes from the The word ontology comes from the Greek Greek

ontos for beingontos for being. It is a relatively new term . It is a relatively new term in the long history of philosophy, in the long history of philosophy, introduced by the introduced by the 19th century German 19th century German philosophersphilosophers to distinguish the study of to distinguish the study of being.being.

In In information systemsinformation systems, a more pragmatic , a more pragmatic view to ontology is taken, where ontology view to ontology is taken, where ontology is considered as a is considered as a kind of agreement on a kind of agreement on a domain representationdomain representation: ontology is an : ontology is an explicit account or representation of a explicit account or representation of a conceptualizationconceptualization. This conceptualization . This conceptualization includes a set of concepts, their includes a set of concepts, their definitions and their inter-relationships. definitions and their inter-relationships.


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Ontology IntegrationOntology Integration

Many works on ontology comparison Many works on ontology comparison has been motivated by ontology has been motivated by ontology integration: given a set of integration: given a set of independently developed ontologies, independently developed ontologies, construct a single global ontology. construct a single global ontology. The first step in integrating the The first step in integrating the ontologies is:ontologies is:

Identify and characterize inter-Identify and characterize inter-ontology correspondences.ontology correspondences.


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Ontology IntegrationOntology Integration The starting point for comparing and The starting point for comparing and

mapping heterogeneous semantics in mapping heterogeneous semantics in ontology mapping is to semantically ontology mapping is to semantically enrich the ontologies.enrich the ontologies.

Semantic enrichment facilitates ontology Semantic enrichment facilitates ontology mapping by making explicit different mapping by making explicit different kinds of ”hidden” information concerning kinds of ”hidden” information concerning the semantics of the modeled objects. The the semantics of the modeled objects. The underlying assumption is that the more underlying assumption is that the more semantics that are explicitly specified semantics that are explicitly specified about the ontologies, the more feasible about the ontologies, the more feasible their comparison becomes.their comparison becomes.


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Classification Of Ontology Classification Of Ontology Specification LanguageSpecification Language


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Life Cycle Of An Ontology


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A Generic Architecture Of Ontology-Based Applications


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Beneficial Applications

Semantic Web Knowledge Management Information Retrieval Service Retrieval


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About the ProblemAbout the ProblemThe Semantic Web proposes to The Semantic Web proposes to

standardize a semantic markup standardize a semantic markup method for:method for:

Uniform formalism, XMLUniform formalism, XML Organization of knowledge into ontologiesOrganization of knowledge into ontologiesThe scientific difficulties are linked to The scientific difficulties are linked to Exact definition of the formalismsExact definition of the formalisms Impossibility of maintaining a worldwide Impossibility of maintaining a worldwide

centralization of the ontologiescentralization of the ontologiesOther challenges concern Other challenges concern RobustnessRobustness Scalability of these techniquesScalability of these techniques


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SurveySurveyWe start with an introduction to the We start with an introduction to the

problem of problem of ontology heterogeneityontology heterogeneity, which , which is characterized by is characterized by different kinds of different kinds of mismatchesmismatches between ontologies. This kind between ontologies. This kind of heterogeneity hampered us from a of heterogeneity hampered us from a combined usage of multiple ontologies, combined usage of multiple ontologies, which is needed in many applications. To which is needed in many applications. To solve the heterogeneity problem, the solve the heterogeneity problem, the mismatches need to be reconciled. This mismatches need to be reconciled. This means that we need to map and align means that we need to map and align different ontologies. different ontologies.


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Ontology MismatchOntology MismatchDifferences between ontologies are Differences between ontologies are

called mismatches, Issues:called mismatches, Issues: Practical problemsPractical problems Ontologies Ontologies VersioningVersioning The main concern here is mismatches The main concern here is mismatches

between ontologies. They are between ontologies. They are further divided into language level further divided into language level and ontology level. The former and ontology level. The former conforms to the syntactic layer, and conforms to the syntactic layer, and the latter to the semantic layer.the latter to the semantic layer.


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Ontology HeterogeneityOntology Heterogeneity


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Language Level MismatchesLanguage Level MismatchesMismatches at the language level occur when Mismatches at the language level occur when

ontologies written in different ontology languages ontologies written in different ontology languages are combined. Four types of mismatches are are combined. Four types of mismatches are identified.identified.

Syntax. Different ontology languages often use Syntax. Different ontology languages often use different syntaxes. For example, to define the class different syntaxes. For example, to define the class of car in RDF Schema, one uses <rdfs:Class ID = of car in RDF Schema, one uses <rdfs:Class ID = "Car">. In LOOM, the expression (defconcept Car) is "Car">. In LOOM, the expression (defconcept Car) is used to define the same class.used to define the same class.

Logical representation. A slightly more complicated Logical representation. A slightly more complicated mismatch at this level is the difference in mismatch at this level is the difference in representation of logic notions. For example, in representation of logic notions. For example, in some languages, it is possible to state explicitly some languages, it is possible to state explicitly that two classes are disjoint (e.g. disjoint A B), that two classes are disjoint (e.g. disjoint A B), whereas it is necessary to use negation in subclass whereas it is necessary to use negation in subclass statements in other languages (e.g. A subclassof statements in other languages (e.g. A subclassof (NOT B), B subclass-of (Not A))(NOT B), B subclass-of (Not A))


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Language Level MismatchesLanguage Level Mismatches Semantics of primitives. A more subtle possible Semantics of primitives. A more subtle possible

difference at the language level is the semantics difference at the language level is the semantics of language constructs. Despite the fact that of language constructs. Despite the fact that sometimes the same name is used for a language sometimes the same name is used for a language constructconstruct

in two languages, the semantics may differ, e.g., in two languages, the semantics may differ, e.g., there are several interpretation of A equalTo B.there are several interpretation of A equalTo B.

Language expressivity. The mismatch at the Language expressivity. The mismatch at the language level with the most impact is the language level with the most impact is the difference in expressivity between two languages.difference in expressivity between two languages.

This difference implies that some languages are This difference implies that some languages are able to express things that are not expressible in able to express things that are not expressible in other languages. For example, some languages other languages. For example, some languages have constructs to negation, whereas others have have constructs to negation, whereas others have not.not.


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Ontology Level MismatchesOntology Level Mismatches

Mismatches at the ontology level happen Mismatches at the ontology level happen when two or more ontologies that when two or more ontologies that describe partly overlapping domains are describe partly overlapping domains are combined. In the same language, or combined. In the same language, or different languages. different languages.

conceptualization mismatch is a difference conceptualization mismatch is a difference in the way a domain is interpreted. in the way a domain is interpreted. divided into model coverage and concept divided into model coverage and concept scope (granularity).scope (granularity).

Explication mismatch is a difference in the Explication mismatch is a difference in the way the conceptualization is specified. way the conceptualization is specified.


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Conceptualization MismatchConceptualization Mismatch Scope. Two classes seem to represent the same Scope. Two classes seem to represent the same

concept, but do not have the same instances, concept, but do not have the same instances, although they may intersect. The classical although they may intersect. The classical example is the class ”employee”, where several example is the class ”employee”, where several administrations use slightly different concepts of administrations use slightly different concepts of employee. employee.

Model coverage and granularity. This is a Model coverage and granularity. This is a mismatch in the part of the domain that is mismatch in the part of the domain that is covered by the ontology, or the level of detail to covered by the ontology, or the level of detail to which that domain is modeled. the example of an which that domain is modeled. the example of an ontology about cars: one ontology might model ontology about cars: one ontology might model cars but not trucks. Another one might represent cars but not trucks. Another one might represent trucks but only classify them into a few trucks but only classify them into a few categories, while a third ontology might make categories, while a third ontology might make very finegrained distinctions between types of very finegrained distinctions between types of trucks based on their physical structure, weight, trucks based on their physical structure, weight, purpose, etc.purpose, etc.


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Explication MismatchesExplication Mismatches Two types of differences can be classified Two types of differences can be classified

as terminological mismatches.as terminological mismatches.– – Synonym terms. Concepts are represented Synonym terms. Concepts are represented

by different names.by different names.One example is the use of term ”car” in one One example is the use of term ”car” in one

ontology and the term ”automobile” in ontology and the term ”automobile” in another ontology.another ontology.

– – Homonym terms. The meaning of the same Homonym terms. The meaning of the same term is different in different context. term is different in different context.

For example, the term ”conductor” has a For example, the term ”conductor” has a different meaning in a music domain than different meaning in a music domain than it has in an electric engineering domain.it has in an electric engineering domain.


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Explication MismatchesExplication Mismatches Modeling style is related to the paradigm and Modeling style is related to the paradigm and

conventions taken by the developers. conventions taken by the developers. – – Paradigm. Different paradigms can be used to Paradigm. Different paradigms can be used to

represent concepts such as time, action, plans, represent concepts such as time, action, plans, causality, propositional attitudes, etc. For example, causality, propositional attitudes, etc. For example, one model might use temporal representations one model might use temporal representations based on interval logic while another might use a based on interval logic while another might use a representation based on point.representation based on point.

– – Concept description. This type of differences are Concept description. This type of differences are called modeling conventions. Several choices can be called modeling conventions. Several choices can be made for the modeling of concepts in the ontologies. made for the modeling of concepts in the ontologies. For example, a distinction between two classes can For example, a distinction between two classes can be modeled using a qualifying attribute or by be modeled using a qualifying attribute or by introducing separate class.introducing separate class.

Encoding mismatches are differences in value Encoding mismatches are differences in value formats, like measuring distance in miles or in formats, like measuring distance in miles or in kilometers.kilometers.


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Approaches and TechniquesApproaches and Techniques The focus of this work is on ontology The focus of this work is on ontology

level mismatch (semantic level mismatch (semantic mismatch).mismatch).

There are also approaches to tackle There are also approaches to tackle syntactic mismatches. We will briefly syntactic mismatches. We will briefly describe some of those in order to describe some of those in order to give a complete picture ofgive a complete picture of

the state.the state.


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Solving Language MismatchesSolving Language MismatchesFour approaches to enable Four approaches to enable

interoperability between different interoperability between different ontologies at the language level ontologies at the language level have been identified.have been identified.

Aligning the metamodel. The Aligning the metamodel. The constructs in the language are constructs in the language are formally specified in a general formally specified in a general model.model.

Layered interoperability. Aspects of Layered interoperability. Aspects of the language are split up in clearly the language are split up in clearly defined layers, and interoperability defined layers, and interoperability is to be resolved layer by layer.is to be resolved layer by layer.


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Solving Language MismatchesSolving Language Mismatches Transformation rules. The relation Transformation rules. The relation

between two specific constructs in between two specific constructs in different ontology language is different ontology language is described in the form of a rule that described in the form of a rule that specifies the transformation from specifies the transformation from the one to the other.the one to the other.

Mapping onto a common knowledge Mapping onto a common knowledge model. The constructs of an ontology model. The constructs of an ontology language are mapped onto a language are mapped onto a common knowledge model, e.g. common knowledge model, e.g. OKBC (Open Knowledge Base OKBC (Open Knowledge Base Connectivity).Connectivity).


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Solving Ontology Level MismatchesSolving Ontology Level Mismatches

The alignment of concepts at the ontology level The alignment of concepts at the ontology level requires requires

Understanding of the meaning of conceptsUnderstanding of the meaning of concepts Cannot be fully automated. Cannot be fully automated.

At the model level, there exist mainly tools that At the model level, there exist mainly tools that suggest alignments and mappings based on suggest alignments and mappings based on heuristics matching algorithm and provide heuristics matching algorithm and provide means to specify these mappings. means to specify these mappings.


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Solving Ontology Level MismatchesSolving Ontology Level Mismatches

Finally, in order to integrate ontologies, Finally, in order to integrate ontologies, it is important to it is important to distinguish distinguish mismatchesmismatches that are hard to solve, and that are hard to solve, and those that are not. those that are not. conceptualization conceptualization mismatchesmismatches often need human often need human intervention to be solved.intervention to be solved.

Most Most explicationexplication mismatches can be mismatches can be solved automatically, but the solved automatically, but the terminological mismatches may be terminological mismatches may be difficult. difficult.

EncodingEncoding mismatches can be quite easily mismatches can be quite easily solved with a transformation step. solved with a transformation step.


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Definition And Scope Of Ontology Definition And Scope Of Ontology MappingMappingThe concept of ”mapping” has a range of The concept of ”mapping” has a range of

meanings, including integration, meanings, including integration, unification, merging, etc. unification, merging, etc.

Mapping will be a set of formulate that Mapping will be a set of formulate that provide the semantic relationships provide the semantic relationships between the concepts in the models.between the concepts in the models.

Mapping is to establish correspondences Mapping is to establish correspondences among the source ontologies, and to among the source ontologies, and to determine the set of overlapping determine the set of overlapping concepts, concepts that are similar in concepts, concepts that are similar in meaning but have different names or meaning but have different names or structure, and concepts that are unique structure, and concepts that are unique to each of the sources. to each of the sources.


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Definition And Scope Of Ontology Definition And Scope Of Ontology MappingMapping Merging and is to create a single Merging and is to create a single

coherent ontology that includes the coherent ontology that includes the information from all the sources. information from all the sources.

Alignment is to make the source Alignment is to make the source ontologies consistent and coherent ontologies consistent and coherent with one another but kept with one another but kept separately.separately.

The aim of mapping is to map The aim of mapping is to map concepts in the various ontologies to concepts in the various ontologies to each other, so that a concept in one each other, so that a concept in one ontology corresponds to a query (i.e. ontology corresponds to a query (i.e. view) over the other ontologiesview) over the other ontologies


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Definition And Scope Of Ontology Definition And Scope Of Ontology MappingMappingTwo tasks have to be conducted in the Two tasks have to be conducted in the

ontology mapping process:ontology mapping process: Discover the correspondences between Discover the correspondences between

ontology elementsontology elements(1) Applying a set of matching rules (1) Applying a set of matching rules (2) Evaluating interesting similarity (2) Evaluating interesting similarity

measures that compare a set of possible measures that compare a set of possible correspondence and help to choose correspondence and help to choose valid correspondence from them.valid correspondence from them.

Describe and define the discovered Describe and define the discovered mappings so that other follow-up mappings so that other follow-up components could make use of themcomponents could make use of them


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Information IntegrationInformation Integration and the and the Semantic WebSemantic Web. .

In many contexts, data resides in a In many contexts, data resides in a multitude of data sources. In the multitude of data sources. In the Semantic Web context, an ontology Semantic Web context, an ontology captures the semantics of data. Data captures the semantics of data. Data integration enables users to ask queries integration enables users to ask queries in a uniform fashion, without having to in a uniform fashion, without having to access each data source independently. access each data source independently. In addition to query, mappings between In addition to query, mappings between ontologies are necessary for agents to ontologies are necessary for agents to interoperate.interoperate.

Application DomainsApplication Domains


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Application DomainsApplication Domains Ontology mergingOntology merging. Several . Several

applications require that we applications require that we combine multiple ontologies into a combine multiple ontologies into a single coherent ontology. In some single coherent ontology. In some cases, these are independently cases, these are independently developed ontologies that model developed ontologies that model overlapping domains. In others, we overlapping domains. In others, we merge two ontologies that evolved merge two ontologies that evolved from a single base ontology. from a single base ontology.


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Automatic Ontology Mapping ToolsAutomatic Ontology Mapping Tools

Automated tools can significantly Automated tools can significantly speed up the process by proposing speed up the process by proposing plausible mappingsplausible mappings

Some parts need expert interventionSome parts need expert intervention

Approaches for building such toolsApproaches for building such tools Use a wide range of heuristics to Use a wide range of heuristics to

generate mappingsgenerate mappings Learn mappingsLearn mappings


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Systems for Ontology MappingSystems for Ontology Mapping

First present First present ChimaeraChimaera, a webbased , a webbased ontology merging and diagnosing ontology merging and diagnosing environment. environment.

Then, present Then, present PROMPTPROMPT, an algorithm used in , an algorithm used in Protégé for ontology merging. Protégé for ontology merging.

Next is Next is FCA-MergeFCA-Merge, which merges , which merges ontologies using documents on the same ontologies using documents on the same domain for the ontologies to be merged. domain for the ontologies to be merged.

MOMISMOMIS, which merges ontologies by means , which merges ontologies by means of ontology clustering of ontology clustering

finally we present finally we present GLUEGLUE, which performs , which performs ontology mapping by machine learning ontology mapping by machine learning techniques.techniques.


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Characteristics of ontology Characteristics of ontology mapping systemsmapping systems


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Characteristics of ontology Characteristics of ontology mapping systemsmapping systems


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Algorithm OverviewAlgorithm Overview

The basic idea of mapping assertion The basic idea of mapping assertion analysis applied in practice for analysis applied in practice for comparison of relevant elements of comparison of relevant elements of two ontologies. two ontologies.


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Components Of MappingComponents Of Mapping The The MapperMapper performs a computation of a performs a computation of a

correspondence measure for the pairs of correspondence measure for the pairs of compared ontology elements, based on compared ontology elements, based on the similarity of their enriched structures.the similarity of their enriched structures.

The The EnhancerEnhancer utilizes an electronic utilizes an electronic lexicon to adjust the similarity values lexicon to adjust the similarity values that have been computed by the mapper, that have been computed by the mapper, with the intention of re-ranking the with the intention of re-ranking the mapping assertions in the result list.mapping assertions in the result list.

The The PresenterPresenter determines which determines which recommendation to suggest to the user, recommendation to suggest to the user, based on the partial ordering of based on the partial ordering of correspondence measures.correspondence measures.


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Components Of MappingComponents Of Mapping The The ExporterExporter translates and exports translates and exports

the mapping results to a desired the mapping results to a desired format so that other follow-up format so that other follow-up applications can import and use the applications can import and use the results in a loosely coupled way.results in a loosely coupled way.

The The Configuration ProfileConfiguration Profile is a user is a user profile to assign individual variable profile to assign individual variable values for different tuning values for different tuning parameters and a threshold value parameters and a threshold value for exclusion of mappings with low for exclusion of mappings with low similarity.similarity.


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Mapping AlgorithmMapping AlgorithmThe mapping algorithm is used to:The mapping algorithm is used to: Semi-Automate the processSemi-Automate the process Comparing & mapping two Comparing & mapping two

semantically enriched ontologiessemantically enriched ontologies The algorithm produces a set of The algorithm produces a set of

ranked suggestions. The user is in ranked suggestions. The user is in control of accepting, rejecting or control of accepting, rejecting or altering the assertions. The level of altering the assertions. The level of automatic exclusion from user automatic exclusion from user presentation is adjustable.presentation is adjustable.


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Similarity Calculation for ConceptsSimilarity Calculation for Concepts

The similarity of two concepts in two The similarity of two concepts in two ontologies is directly calculated as the ontologies is directly calculated as the cosine measure between the two cosine measure between the two representative feature vectors. Let two representative feature vectors. Let two feature vectors for concept a and b feature vectors for concept a and b respectively, both of length n, be respectively, both of length n, be given. The cosine similarity between given. The cosine similarity between concept a and concept b is defined as:concept a and concept b is defined as:


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Similarity Calculation for ConceptsSimilarity Calculation for Concepts

Ca and Cb are feature vectors for concept Ca and Cb are feature vectors for concept a and b, respectivelya and b, respectively

• • n is the dimension of the feature n is the dimension of the feature vectorsvectors

• • |Ca| and |Cb| are the lengths of the two |Ca| and |Cb| are the lengths of the two vectorsvectors

A A thresholdthreshold value is defined by the user value is defined by the user to exclude pairs that have too low to exclude pairs that have too low similarity values. similarity values.


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Path Length MeasurementPath Length Measurement Path length is measured in Path length is measured in nodes nodes

rather than linksrather than links in WordNet, nouns are organized into in WordNet, nouns are organized into

taxonomies where each node is a set taxonomies where each node is a set of synonyms (a synset) representing of synonyms (a synset) representing a single sense.a single sense.

If a word has multiple senses, it will If a word has multiple senses, it will appear in multiple synsets at various appear in multiple synsets at various locations in the taxonomy. locations in the taxonomy.

Verbs are structured in a similar Verbs are structured in a similar hierarchy with the relation being hierarchy with the relation being troponymy in stead of hypernymy.troponymy in stead of hypernymy.


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Path Length MeasurementPath Length Measurement

We use in this experiment is that of We use in this experiment is that of hyponymy/hypernymy, or the is-a-kind-of hyponymy/hypernymy, or the is-a-kind-of relation, which relates more general and relation, which relates more general and more specific senses.more specific senses.

One way to measure the semantic One way to measure the semantic similarity between two words a and b is similarity between two words a and b is to measure the distance between them in to measure the distance between them in WordNet. WordNet.

This can be done by finding the paths from This can be done by finding the paths from each sense of a to each sense of b and each sense of a to each sense of b and then selecting the shortest such path.then selecting the shortest such path.


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Example on hyponymy relation in WordNet Example on hyponymy relation in WordNet used for the path length measurementused for the path length measurement


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Similarity Calculation for Complex Similarity Calculation for Complex ElementsElementsBased on the correspondences Based on the correspondences calculated for the concepts, we calculated for the concepts, we could further expand the could further expand the correspondence discovery into other correspondence discovery into other elements and structures in the elements and structures in the ontologiesontologies . .


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RelationsRelationsThe similarity of relations is calculated The similarity of relations is calculated

based on the corresponding domain based on the corresponding domain concepts and range concepts of the concepts and range concepts of the relations. relations.

• • X and X’ are domain concepts of R and R’ X and X’ are domain concepts of R and R’ • • Y and Y’ are the range concepts of R and Y and Y’ are the range concepts of R and

R’R’• • the sim(X, X0) and sim(Y, Y0) can be the sim(X, X0) and sim(Y, Y0) can be

calculated by equation for concepts calculated by equation for concepts similarity.similarity.


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ClustersClustersFor this, we define the concept of cluster. For this, we define the concept of cluster. A cluster is a group of related concepts, A cluster is a group of related concepts,

which includes a center concept a and which includes a center concept a and its k-nearest neighbors.its k-nearest neighbors.

A cluster of 1-nearest neighbor includes A cluster of 1-nearest neighbor includes a center concept and its direct parent, a center concept and its direct parent, and its direct children. and its direct children.

A cluster of 2-nearest neighbor includes A cluster of 2-nearest neighbor includes the grandparent, the siblings and the the grandparent, the siblings and the grandchildren, in addition to the 1-grandchildren, in addition to the 1-nearest neighbor. nearest neighbor.


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Example of calculating cluster similarityExample of calculating cluster similarity


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Similarity Of ClustersSimilarity Of Clusters

The similarity of clusters is calculated The similarity of clusters is calculated based on the weighted percentage based on the weighted percentage of established mappings between of established mappings between member concepts in proportion to member concepts in proportion to the number of all connections the number of all connections between the two clusters. between the two clusters.

The similarity between cluster A and The similarity between cluster A and cluster B is therefore computed as:cluster B is therefore computed as:


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Similarity Of ClustersSimilarity Of Clusters X and Y are clusters of k-nearest X and Y are clusters of k-nearest

neighbor. X = {a1, a2, a3, · · · , an} and neighbor. X = {a1, a2, a3, · · · , an} and Y = {b1, b2, b3, · · · , bm}.Y = {b1, b2, b3, · · · , bm}.

M is a subset of the cartesian product of M is a subset of the cartesian product of X and Y, where M X and Y, where M ⊆⊆ X * Y,M = {(ai, bj)| X * Y,M = {(ai, bj)|(ai (ai ∊∊ X) X) ∩∩ (bj (bj ∊∊ Y) Y) ∩∩ (sim(ai, bj) > 0)} (sim(ai, bj) > 0)}

|X| and |Y| are number of elements in the |X| and |Y| are number of elements in the two sets, respectively.two sets, respectively.

The sim(ai, bj) is calculated by equation The sim(ai, bj) is calculated by equation for concepts similarity.for concepts similarity.


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OntologiesOntologies

The similarity between two ontologies The similarity between two ontologies can be quantified as the weighted can be quantified as the weighted percentage of established mappings percentage of established mappings in proportion to all the connections in proportion to all the connections between concepts in the two between concepts in the two ontologies, as defined in the ontologies, as defined in the following equation.following equation.


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Similarity Of OntologiesSimilarity Of Ontologies O1 and O2 are two ontologies. O1 = {a1, O1 and O2 are two ontologies. O1 = {a1,

a2, a3, · · · , an} and O2 = {b1, b2, b3, · · a2, a3, · · · , an} and O2 = {b1, b2, b3, · · · , bm}. ai (i=1...n) are the concepts in · , bm}. ai (i=1...n) are the concepts in O1 and bj (j=1...m) are the concepts in O1 and bj (j=1...m) are the concepts in O2.O2.

M is a subset of the cartesian product of M is a subset of the cartesian product of O1 and O2, where M O1 and O2, where M ⊆⊆ O1 * O2,M = {(ai, O1 * O2,M = {(ai, bj)|(ai bj)|(ai ∊∊ O1) O1) ∩∩ (bj (bj ∊∊ O2) O2) ∩∩ (sim(ai, bj) > (sim(ai, bj) > 0)}0)}

|O1| and |O2| are number of concepts in |O1| and |O2| are number of concepts in the two ontologies, respectively.the two ontologies, respectively.

The sim(ai, bj) is calculated by equation The sim(ai, bj) is calculated by equation 6.1 for concepts similarity.6.1 for concepts similarity.


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Further RefinementsFurther RefinementsTo achieve more accurate mapping To achieve more accurate mapping

results, further refinements of the results, further refinements of the results are always welcome.results are always welcome.

There are mainly three kinds of efforts There are mainly three kinds of efforts which fall in the realm of our further which fall in the realm of our further refinement techniques. refinement techniques.


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Heuristics for Mapping Refinement Heuristics for Mapping Refinement Based on the Calculated SimilarityBased on the Calculated SimilarityThe heuristic rules can be The heuristic rules can be domain domain

independentindependent or or domain dependentdomain dependent. . Some example domain independent Some example domain independent

heuristic rules are:heuristic rules are: If all children of concept A match If all children of concept A match

concept B, then A also matches B.concept B, then A also matches B. Two concepts match if their children Two concepts match if their children

also match.also match. Two concepts match if their parent Two concepts match if their parent

match and k% of their children also match and k% of their children also match.match.


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Heuristics for Mapping Refinement Heuristics for Mapping Refinement Based on the Calculated SimilarityBased on the Calculated SimilarityThe domain dependent heuristic rules The domain dependent heuristic rules

incorporate domain knowledge into incorporate domain knowledge into the mapping process. the mapping process.

For example, a domain dependent For example, a domain dependent heuristic rule in the tourism domain heuristic rule in the tourism domain can be that can be that

if concept B is a descendant of if concept B is a descendant of concept A and B matches hotel, it is concept A and B matches hotel, it is unlikely that A matches Bed and unlikely that A matches Bed and BreakfastBreakfast


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Managing User FeedbackManaging User FeedbackFully automatic solutions to the mapping Fully automatic solutions to the mapping

problem are not possible due to the problem are not possible due to the potentially high degrees of semantic potentially high degrees of semantic heterogeneity between ontologies.heterogeneity between ontologies.

We thus allow an interactive mapping We thus allow an interactive mapping process, e.g. to allow users to manually process, e.g. to allow users to manually add, confirm, reject, or alter mapping add, confirm, reject, or alter mapping assertions. assertions.

On the other hand, users’ actions on the On the other hand, users’ actions on the mapping results is a good source to mapping results is a good source to improve the algorithm performance in the improve the algorithm performance in the next round of mapping result calculation next round of mapping result calculation or updating.or updating.


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Other Matchers and Combination Other Matchers and Combination of Similarity Valuesof Similarity ValuesIt is necessary to It is necessary to combine different combine different

approachesapproaches in an effective way. in an effective way.We therefore introduce the We therefore introduce the coordinator coordinator

componentcomponent in the system architecture to in the system architecture to be responsible for combining the be responsible for combining the similarity values returned by different similarity values returned by different mapping components. The coordinator mapping components. The coordinator assigns to each mapping strategy a assigns to each mapping strategy a weight that indicates how much that weight that indicates how much that particular strategy will contribute to the particular strategy will contribute to the whole picture. Then the coordinator whole picture. Then the coordinator combines the returned similarity values combines the returned similarity values via a weighted sum.via a weighted sum.


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EquivalenceEquivalence

equivalentClass, equivalentClass, equivalentPropertyequivalentProperty

While putting together a set of While putting together a set of component ontologies as part of a component ontologies as part of a third ontology it is useful to be able third ontology it is useful to be able to indicate that a particular class or to indicate that a particular class or property in one ontology is property in one ontology is equivalent to a class or property in a equivalent to a class or property in a second ontology.second ontology.


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ExampleExampleDefine the class TexasThings that contains Define the class TexasThings that contains exactly those things located in the Texas exactly those things located in the Texas regionregion

<<owl:Class rdf:ID="TexasThingsowl:Class rdf:ID="TexasThings>">"<<owl:eqivalentClassowl:eqivalentClass>>

<<owl:Restrictionowl:Restriction>><<owl:onProperty rdf:resource="#locatedInowl:onProperty rdf:resource="#locatedIn>/">/"

<<owl:allValuesFrom owl:allValuesFrom rdf:resource="#TexasRegionrdf:resource="#TexasRegion>/">/"

/</<owl:Restrictionowl:Restriction>>/</<owl:equivalentClassowl:equivalentClass>>

/</<owl:Classowl:Class>>


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NoteNote

The difference between using The difference between using owl:equivalentClass owl:equivalentClass and and rdf:subClassOfrdf:subClassOf is the difference is the difference between necessary condition and between necessary condition and necessary and sufficient condition.necessary and sufficient condition.

With subClassOf things that are With subClassOf things that are located in Texas not necessarily located in Texas not necessarily TexasThings With equivalentClass if TexasThings With equivalentClass if something is located in Texas then it something is located in Texas then it must be in the class TexasThingsmust be in the class TexasThings


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InteroperabilityInteroperabilityAn ontology is used to describe the meaning An ontology is used to describe the meaning

of concepts in agent communication. of concepts in agent communication. This ontology depends on the subject of the This ontology depends on the subject of the

communication. Since communication. Since The number of possible subjects is almost The number of possible subjects is almost

infiniteinfinite The concepts used for a subject can be The concepts used for a subject can be

described by different ontologiesdescribed by different ontologiesThe development of generally accepted The development of generally accepted

standards will take a long time. This lack of standards will take a long time. This lack of standardization, hampers communication standardization, hampers communication and collaboration betwbetween agents, is and collaboration betwbetween agents, is known as known as interoperability interoperability problem.problem.


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InteroperabilityInteroperabilityDifferences between ontologies: Differences between ontologies: (1)(1)Different semantic structures, Different semantic structures,

structural conflictstructural conflict,,(2)(2)Different names for the same type Different names for the same type

of information or the same name for of information or the same name for (slightly) different types of (slightly) different types of information, information, naming conflicts.naming conflicts.

(3)(3)Different representations of the Different representations of the same data, same data, data conflictsdata conflicts. .


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Ontologies 1 and 2Ontologies 1 and 2


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OntologiesOntologies Extracts from a possible contextual ontology published by Extracts from a possible contextual ontology published by

an inquireran inquirer::

defproperty(b, capital).defproperty(b, capital).about(b,capital, domain(country)).about(b,capital, domain(country)).item(b,country, italy).item(b,country, italy).

Extracts from the contextual ontology published by the Extracts from the contextual ontology published by the oracle:oracle:

defclass(a,location).defclass(a,location).defproperty(a,has capital).defproperty(a,has capital).defproperty(a,is capital of).defproperty(a,is capital of).about(a,has capital, (domain, state)).about(a,has capital, (domain, state)).about(a,is capital of, (domain, town)).about(a,is capital of, (domain, town)).item(a,town, rome).item(a,town, rome).item(a,state, italy).item(a,state, italy).about(a,italy, (has capital,rome)).about(a,italy, (has capital,rome)).


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MappingMapping


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ReferencesReferences [1] Xiaomeng Su. Semantic Enrichment for Ontology Mapping, Department of

Computer and Information Science, Norwegian University of Science and Technology, 2004.

[2] S. Banerjee and T. Pedersen. Extended gloss overlaps as a measure of semantic relatedness, In Proceedings of the Eighteenth International Joint Conference on Articificial Intelligence IJCAI-2003, 2003.

[3] D. Beneventano and S. Bergamaschi and I. Benetti and A. Corni and F. Guerra, and G. Malvezzi. Si-designer: A tool for intelligent integration of information, In 34th Annual Hawaii International Conference on System Sciences (HICSS-34). IEEE Computer Society, 2001.

[4] S. Bergamaschi and F. Guerra and M. Vincini. A data integration framework for e-commerce product classification, In Proceeding of the first semantic web conference (ISWC-2002), pages 379–393, 2002.

[5] T. Berners-Lee and E. Miller. The semantic web lifts off, In ERCIM News NO. 51, October 2002.

In Proceedings of the First International Semantic Web Conference 2002, pages 84–101, 2002.

[6] S. Bailin and W. Truszkowski. Ontology negotiation between scientific archives, In Proceedings of the Thirteenth International Conference on Scientific and Statistical Database Management (SSDBM 2001). IEEE Press, July 2001.

[7] T. Brasethvik. Conceptual modelling for domain specific document description and retrieval- An approach to semantic document modelling. PhD thesis, IDI, Norwegian University of Science and Technology (NTNU), 2004.


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