Xiaomeng Su & Jon Atle GullaDept. of Computer and Information Science

Norwegian University of Science and Technology

Trondheim Norway

June 2004

Semantic Enrichment for Ontology Mapping

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Semantic interoperability

The Semantic Web vision Ontology heterogeneity problem Comparison of ontologies should be aided by

automatic process Ontology mapping typically involves

identifying correspondences between the source ontologies

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Prerequisite

Focusing on light-weight ontology The ontologies share the same domain The same representation language is assumed Our approach is based on Referent Modelling

Language (RML)

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Idea illustration

Enrich the concept with extension. Documents (textual) that belong to a concept

considered to be extension. Using Information Retrieval techniques to compute

a representative feature vector of the extension information.

When no extension available, using text categorization to assign documents to concepts.

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Architecture

Mapping

Mapper

Mapping

Enhancer

Post-processing

WordNet

Presenter

Presentation&

Refinement

Exportor

Translation&

Storage

Mappingassertions

(XML)

------------

Configurationprofile(XML)

Cns + Manual

Textcategorization ----

--------

WordNetStop word

list

FVC

Semanticenrichment

Categorization results(XML)

Semantic enriching

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Functional view of the system

Document assignment (optional) Feature vector construction

Pre-processing Document representation Concept vector construction

leaf node -- average vector of the documents vectorsnon-leaf node -- weighted sum of the documents vectors,

sub concept vectors and related concept vectors

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Functional view of the system

Similarity calculation The similarity of two concepts – cosine measure The similarity of relations – domain and range The similarity calculation of clusters and that of the two

ontologies – based on the above two

Post-processing the assertions using WordNet to update the rank of assertions.

Mapping assertion generation and user feedback management

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Post-processing

To update ranks according to the concept relatedness in WordNet

We use simple path length measurement JWNL

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A prototype -iMapper

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Validation

To measure the accuracy of the mapping algorithm Focusing on concepts Using users manually identified mappings as gold

standards Measures

Precision (the fraction of automatically discovered mappings that are correct)

Recall (the fraction of the correct mappings that have been discovered)

Experiment on two domains Product catalogue integration Tourism Ontology comparison

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Measure precision at 11 standard recall levels Experiment in both domains The algorithm has identified most of the mappings and ranked them in

the correct order.

Precision vs. recall curves for the two tasks

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recall

precision

product catalogue domain

tourism domain

Preliminary results

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Preliminary results

Using WordNet to update the rank showed different effects on the two domains

No topic related semantics in WordNet

WordNet effect on tourism domain

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recall

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without WordNet

WordNet effect on product domain

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without WordNet

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Evaluation remarks

Encouraging results Failure analysis

Quality of the gold standards Quality of the feature vector

Further evaluation Gold standard Sensitivity tests Alternative measures

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Summing up

An approach to ontology mapping based on the idea of semantic enrichment

The approach has been implemented and evaluated

Explored the possiblity of incorporating WordNet into the system

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The end...

Thank you!