Transcript Document
A Review of Ontology Mapping,
Merging, and Integration
Presenter: Yihong Ding
Survey Papers
Ontology Research and Development Part 2 –
A review of Ontology Mapping and Evolving,
Ying Ding and Schubert Foo
Some Issues on Ontology Integration, H.
Sofia Pinto, A. Gomez-Perez, and Joao P.
Martins
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Ontology Mapping
Two parties understand each other
Use the same formal representation
Share the conceptualization (so the same
ontology)
Not easy to let everybody to agree on the
same ontology for a domain
The problem of ontology mapping
Different ontologies on the same domain
Parties with different ontologies do not understand
each other
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Ontology Integration
Building
a new ontology and reusing
other available ontologies (integration)
Merging
different ontologies into a single
one that “unifies” all of them (merging)
Integration
of ontologies into
applications (use)
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Integration
Resulting ontology
can be composed of
several “modules”
Be able to identify
regions taken from
different integrated
ontologies
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Merging
Hard to identify regions
taken from merged
ontologies
Knowledge from merged
ontologies is homogenized
Knowledge from one source
ontology is scattered and
mingled with the knowledge
that comes from other
sources
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Use
Ontologies should be
compatible among
themselves
Issues for compatibility
Ontological commitments
Language
Level of details
Context
etc.
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InfoSleuth’s
reference ontology
Mapping
Resource agent
Explicit specified relationships of terms between ontologies
Encapsulated within resource agents
Encapsulate information about mapping rules
Present information in ontologies (reference ontologies)
Reference ontologies
Represented in OKBC
Stored in OKBC server
Ontology agents provide specifications
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To users (for request formulation)
To resource agents (for mapping)
Stanford’s
ontology algebra
Mapping
Established articulations that enables the knowledge
interoperability
Executed by ontology algebra
Ontology algebra
Operators
Unary: filter, extract
Binary: intersection, union, difference
Inputs: ontology graphs
Semi-automatic graph mapping
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Domain experts define a variety of fuzzy matching
Use articulation ontology (abstract mathematical entities with
some properties)
AIFB’s
formal concept analysis
Mapping and merging
Ontology concepts with the same extension
Executed by FCA-Merge
FCA-Merge
Create a concept hierarchy - the concept lattice -containing
the original concepts based on the source ontologies
Process
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Objects annotated by both ontologies: directly compute lattice
Else: create annotated objects first.
Else if cannot annotate: use documents as artificial objects. I.e.,
concepts which always appear in the same documents are
supposed to be merged
ECAI2000’s methods
Williams & Tsatsoulis
Tamma & Bench-Capon
Supervised inductive learning
Create semantic concept descriptions
Apply concept clustering algorithm to find mapping
Name-based matching
Relate classes in bottom-up and top-down ways
Priority functions to solve inconsistency
Human experts adjust priority functions
Uschold
Use a global reference ontology
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ISI’s
OntoMorph
Syntactic
Pattern-directed rewrite rules
Concise specification of sentence-level
transformations based on pattern matching
Semantic
rewriting
rewriting
Modulate syntactic rewriting via semantic
models and logical inference
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KRAFT’s
ontology clustering
Based
on the similarities between the
concepts known to different agents
Method
Use a domain ontology describe abstract
information (global reference)
Each ontology cluster define certain part of
its parent ontology
Name, instance, relation, compound
matchers
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Heterogeneous
Database Integration
A database scheme is a lightweight ontology
Typical researches
Batini et.al. (1986), five steps of integrating
schemata of existing or proposed databases into a
global, unified schema
Sheth & Kashyap (1992), semantic similarities in
schema integration
Palopoli et.al. (2000), two techniques to integrate
and abstract database schemes
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Other
Ontology Mappings
Lehmann & Cohn (1994)
Li (1995)
Need more specialized concept definitions
Identify attribute similarities using neural networks
Borst & Akkermans (1997)
Resulted mappings could be considered as a new
ontology
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Other
Ontology Mappings
Hovy (1998)
Weinstein & Birmingham (1999)
Graph mapping use description compatibility between elements
McGuinness et.al. (2000)
Several heuristic rules to support the merging of ontologies
Chimaera system
Term merging from different knowledge sources
Noy & Musen (2000)
PROMPT algorithm for Protégé system
Ontology merging and alignment for OKBC compatible format
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Conclusion
Depend
very much on the inputs of
human experts
Focus on 1-1 mappings
Further needs n:1, 1:n, m:n mappings
Ontology mapping can be viewed as the
projection of the general ontologies from
different point of views
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