Ontology Mapping

YANG YU (yangyu1, UMBC)
A research on how to improve
machine learning based
ontology mapping
Is Apple the Same as Orange?
To: nicholas@csee.umbc.edu
Subject: Yu

Presentation Overview
 Semantic Web
 Ontology
 Ontology Mapping
 Motivation
 Methods (Machine Learning, Text Classification)
 Problem
 My Proposed Research
 Evaluation
 Current Results
 Future Work
 Comments & Questions
 May mistaken something
 EMAIL: yangyu1, UMBC

The Semantic Web
 “in general, computers have no reliable way to process the
semantics”
 Some achievements by complicated algorithm (search engine)
 Apple and orange: Apple is a kind of fruit ?Is there anther way?
 Knowledge Base, Databases, standalone(?) structured
information
 HTML-Web, information not encoded, post-process
 Database, information encoded, pre-process
 Tim Berners-Lee, James Hendler, and Ora Lassila , 2001, the
Semantic Web, Scientific American
 "The Semantic Web is an extension of the current web in which
information is given well-defined meaning, better enabling
computers and people to work in cooperation."

RDF -- well-defined meaning
 “uses URIs to encode information”,
 “the URIs ensure that concepts are not just
words in a document but are tied to a unique
definition that everyone can find on the Web”.
(quoted from The Semantic Web)
 Example:
 http://www.amk.ca/talks/2003-03/

RDF Example
<rdf:RDF
xmlns:FOAF="http://xmlns.com/foaf/0.1/"
xmlns:dc="http://purl.org/dc/elements/1.1/"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rev="http://amk.ca/xml/review/1.0#">

<rev:Review rdf:about="http://example.com/rev1">
<rev:subject rdf:resource="urn:isbn:1930110111"/>
</rev:Review>
<rdf:Description rdf:about="http://example.com/author/0042">
<FOAF:firstName>Bob</FOAF:firstName>
<FOAF:homepage rdf:resource="http://www.snee.com/bob/"/>
<FOAF:pastProject rdf:resource="urn:isbn:1930110111"/>
<FOAF:surname>DuCharme</FOAF:surname>
</rdf:Description>
</rdf:RDF>

RDF Example
Description of
the Author
Even the Author’s
Name is Apple, X
well-defined meaning

Ontology
 What it is?
 “Short answer: an ontology is a specification of a
conceptualization”
 http://www-ksl.stanford.edu/kst/what-is-an-ontology.html
 “The most typical kind of ontology for the Web has a taxonomy and
a set of inference rules”
 From The Semantic Web
 RDF, RDF-S, OWL (www.w3c.org)
 A sample ontology
 Wine Ontology
 http://www.w3.org/TR/owl-guide/wine.rdf
 How to use?
 More sophisticated computing services will be based on Ontology

Some Large Ontologies
 OpenCyc (www.opencyc.org)
 the world's largest and most complete general knowledge base and commonsense
reasoning engine.
 47,000 concepts: an upper ontology whose domain is all of human consensus reality,
interrelated and constrained by 306,000 assertions
 WordNet (wordnet.princeton.edu)
 English nouns synonym sets, verbs synsets, adjectives synsets and adverbs
synsets each representing one underlying lexical concept. Different relations link the
synonym sets.
 OBO(obo.sourceforge.net)
 Open Biomedical Ontology project Supported by NIH, NSF, etc.
 Biological and medical domains, Sequnce, Palnt, etc. Eg, Gene Ontology: 17746 terms,
93.9% with definitions.
 SUMO (IEEE)
 Suggested Upper Merged Ontology
 General-purpose concepts, foundation for more specific ontologies for different
domains.

More ontologies
 www.google.com/search?q=filetype:owl+owl
 UMBC Swoogle (swoogle.umbc.edu)
 My Question: How to use ontologies, still in
research?

Why Ontology Mapping
 The same term in two ontologies may mean different (previous
example).
 Different Organizations may use different ontologies for the
same domain, resulting different terms representing the same
concept (eg, AI & CI); problems arise when they try to
communicate with each other – “interoperability problem”
 H. S. Pinto. 1999, Some issues on ontology integration. In IJCAI-99
workshop on Ontologies and Problem-Solving Methods (KRR5)
Hi, I want to buy
some apples.
What are you talking about?
I only sell Red and Delicious

Ontology Mapping
 Try to find relationships between each pair of concepts used in
two different ontologies. For example, Equivalent, Subclass_Of,
Superclass_Of, Siblings, Similar (how much similar?), Different
(how much different?)
Ontology A1
Ontology A2
Obtaining probabilistic values (N * M)
that shows how well class ni in Ontology A1
maps to class nj in Ontology A2
N M

Manual Mapping
 OpenCyc
 SENSUS, FIPS 10-4,several large (300k-term) pharmaceutical
thesauri, large portions of WordNet, MeSH/Snomed/UMLS,
and the CIA World Factbook.
 Knowledge worker + domain expert
 Interactive clarification tool + domain expert
 Mapping Ontologies into Cyc, Cyc Corp, 2002
 SUMO  WordNet
 Mapping WordNet to the SUMO Ontology, Teknowledge
Corp, 2002
 Advantages and Disadvantages

Lexical Based Approach
 John Li, 2003, LOM – a Lexicon based
ontology mapping tool. Information
Interpretation and Integration Conference
 String matching, adding some techniques,
like word stem
 MeetingPlace and the_Place_of_Meeting
 Write and Written

Machine Learning Based
Approach
 Machine Learning
 Learning is a process, after which, if success,
enables one to do something one cannot do
before.
 “Machine learning refers to a system capable of
the autonomous acquisition and integration of
knowledge” (AAAI)
 Text Classification
 Supervised Machine Learning
 single-category text classification

Some Machine Learning Based
Ontology Mapping System
 CAIMEN
 Lacher, M.; and Groh, G. May 2001. Facilitating the
Exchange of Explicit Knowledge through Ontology
Mappings. In Proceedings of the 14th International
FLAIRS Conference. Key West, FL, USA
 Glue
 Doan Anhai, et al. 2003. Learning to match
ontologies on the Semantic Web. Volume 12, Issue
4, VLDB Journal

UMBC OntoMapper
 Prasad, S.; Peng, Y.; and Finin, T. 2002. A Tool For Mapping
Between Two Ontologies (Poster), International Semantic
Web Conference (ISWC02).
According to the
researchers:
Results not encouraging
because of very
few samplers

A Problem of Machine Learning
Based Ontology Mapping
 Samplers used to train the learner are
collected or created manually by ontology
workers
 May ensure quality?
 Lack of quantity
 If samplers are not enough, a concept may not be well
represented.

My Proposed Research
Obtaining Samplers from the Web Automatically
for Machine Learning Based Ontology Mapping
 Advantages:
 Ensure samplers quantity
 Web Documents: A lot of Documents created in a distributed
environment, well representing various aspects of a concept.
 Low cost
 By using search engines like Google, documents can be easily
collected
 Disadvantages:
 Quality issue

System Overview
Ontology A1
Ontology A2
parser
Samplers
By Classes
Samplers
By Classes
Queries A1
Queries A2

System Overview (Cont.)
Samplers
For A1
Samplers
For A2
Model A1
Model A2
Text Classifier
1
1
2
2

Text Classifier
System Overview (Cont.)
Ontology A1
Model A2
Samplers
For A1
Samplers for
N classes
Suppose having N classes
models for
M classes
Obtaining probabilistic values
(N * M)
that shows how well class ni in
Ontology A1
maps to class nj in Ontology A2
models for
M classes

Evaluation
 Compare the mapping results of the “enhanced” system with
mapping results obtained from human experts.

Current Result & Future Work
 Text Classifier Rainbow doesn’t work well, considering switching
to other text classification tool, for example Weka or some
sourceforge projects.
 Trying to find how to utilize the raw probabilistic value obtained
from the cross-classification.
 Trying to use clustering algorithms to improve the quality of the
samplers

Questions & Comments

Ontology Mapping

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