Transcript Document

A Review of Ontology Mapping,
Merging, and Integration
Presenter: Yihong Ding
Survey Papers
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Ontology Research and Development Part 2 –
A review of Ontology Mapping and Evolving,
Ying Ding and Schubert Foo
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Some Issues on Ontology Integration, H.
Sofia Pinto, A. Gomez-Perez, and Joao P.
Martins
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Ontology Mapping
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Two parties understand each other
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Use the same formal representation
Share the conceptualization (so the same
ontology)
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Not easy to let everybody to agree on the
same ontology for a domain
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The problem of ontology mapping
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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
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Resulting ontology
can be composed of
several “modules”
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Be able to identify
regions taken from
different integrated
ontologies
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Merging
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Hard to identify regions
taken from merged
ontologies
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Knowledge from merged
ontologies is homogenized
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Knowledge from one source
ontology is scattered and
mingled with the knowledge
that comes from other
sources
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Use
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Ontologies should be
compatible among
themselves
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Issues for compatibility
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Ontological commitments
Language
Level of details
Context
etc.
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InfoSleuth’s
reference ontology
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Mapping
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Resource agent
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Explicit specified relationships of terms between ontologies
Encapsulated within resource agents
Encapsulate information about mapping rules
Present information in ontologies (reference ontologies)
Reference ontologies
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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
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Mapping
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Established articulations that enables the knowledge
interoperability
Executed by ontology algebra
Ontology algebra
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Operators
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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
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Mapping and merging
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Ontology concepts with the same extension
Executed by FCA-Merge
FCA-Merge
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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
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Williams & Tsatsoulis
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Tamma & Bench-Capon
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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
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Use a global reference ontology
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ISI’s
OntoMorph
 Syntactic
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Pattern-directed rewrite rules
Concise specification of sentence-level
transformations based on pattern matching
 Semantic
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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
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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
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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
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Lehmann & Cohn (1994)
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Li (1995)
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Need more specialized concept definitions
Identify attribute similarities using neural networks
Borst & Akkermans (1997)
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Resulted mappings could be considered as a new
ontology
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Other
Ontology Mappings
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Hovy (1998)
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Weinstein & Birmingham (1999)
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Graph mapping use description compatibility between elements
McGuinness et.al. (2000)
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Several heuristic rules to support the merging of ontologies
Chimaera system
Term merging from different knowledge sources
Noy & Musen (2000)
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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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