Post on 22-Dec-2015
Ontology matching
ΠΑΝΕΠΙΣΤΗΜΙΟ ΑΙΓΑΙΟΥΠΑΝΕΠΙΣΤΗΜΙΟ ΑΙΓΑΙΟΥ
ΤΜΗΜΑ ΜΗΧΑΝΙΚΩΝ ΠΛΗΡΟΦΟΡΙΑΚΩΝ ΚΑΙ ΕΠΙΚΟΙΝΩΝΙΑΚΩΝ ΤΜΗΜΑ ΜΗΧΑΝΙΚΩΝ ΠΛΗΡΟΦΟΡΙΑΚΩΝ ΚΑΙ ΕΠΙΚΟΙΝΩΝΙΑΚΩΝ ΣΥΣΤΗΜΑΤΩΝΣΥΣΤΗΜΑΤΩΝ
Πρόγραμμα Μεταπτυχιακών ΣπουδώνΠρόγραμμα Μεταπτυχιακών Σπουδών
http://www.icsd.aegean.gr/kotis/
Κώτης Κων/νος - Copyright Ai-Lab, ICSEng. Dept. University of the Aegean - 2007 - Copyright Ai-Lab, ICSEng. Dept. University of the Aegean - 2007
Outline
• Information integrationInformation integration• Schema and Ontology (Semantic) integrationSchema and Ontology (Semantic) integration• The matching problem - Terminology• Matching Dimensions (classification)• Techniques• Matching strategies (methods)• Systems
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Integration
• a large number of information sources need a single point of global access via a single and unified view
• Need for a specific conceptualization and a specific vocabulary whose entries provide lexicalizations of the concepts used for shaping information
• Ontologies play a key role to shaping information as they provide conceptualizations of domains
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Information/data integration
• for meaningful information exchange or integration, providers and consumers (humans or software agents) need compatible semantics
• A traditional example for information integration is the Catalog Integration example
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Information/data integration• B2B applications represent and store their products in
electronic catalog-type models. • A typical example of such a model is the product directory
of http://www.amazon.com• A company to participate in a specific marketplace in
which amazon.com participates, it must identify correspondences between entries of its catalogs and entries of the catalogs of www.amazon.com.
• Having identified the correspondences between the entries of the catalogs, it can be assumed that the catalogs are aligned.
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New faculty member
Find houses with 2 bedrooms priced under
200K
homes.comrealestate.com homeseekers.com
Information/data integration
…sources on the Web which provide house listings
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Architecture of Data Integration System
mediated schema
homes.comrealestate.com
source schema 2
homeseekers.com
source schema 3source schema 1
Find houses with 2 bedrooms priced under 200K
simply pose the query in the
mediated schema
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price agent-name address
Semantic Matches between Schemas
1-1 match complex match
homes.com
listed-price contact-name city state
Mediated schema
320K Jane Brown Seattle WA240K Mike Smith Miami FL
Source schema
the schema-matching problem is to find semantic mappings between the elements of the two schemas
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Schema Matching is Everywhere!• Fundamental problem in numerous applications• Databases
– data integration– data translation– schema/view integration– data warehousing– semantic query processing– model management– peer data management
• AI– knowledge bases, ontology merging, information gathering
agents, ...• Web
– e-commerce– marking up data using ontologies (e.g., on Semantic Web)
Schema matching vs. ontology matching: differences
• Schemas often do not provide explicit semantics for their data– Relational schemas provide no generalization
• Ontologies are logical systems that constrain the meaning– Ontology definitions as a set of logical axioms
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Schema matching vs. ontology matching: commonalities
• Schemas and ontologies provide a vocabulary of terms that describes a– domain of interest
• Schemas and ontologies constrain the meaning of terms used in the– vocabulary
Techniques developed for both problems are of a mutual benefit
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Why Schema Matching is Difficult• Schema & data never fully capture semantics!
– not adequately documented – schema creator has retired to Florida!
• Must rely on clues in schema & data – using names, structures, types, data values, etc.
• Such clues can be unreliable– same names => different entities: area => location or square-feet– different names => same entity: area & address => location
• Intended semantics can be subjective– house-style = house-description?– military applications require committees to decide!
• Cannot be fully automated, needs user feedback!
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Ontology Matching• Increasingly critical for
– knowledge bases, Semantic Web
• An ontology – concepts organized into a taxonomy tree– each concept has
• a set of attributes• a set of instances
– relations among concepts
• Matching– concepts – attributes – relations
name: Mike Burnsdegree: Ph.D.
Entity
UndergradCourses
GradCourses
People
StaffFaculty
AssistantProfessor
AssociateProfessor
Professor
CS Dept. US
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Matching Taxonomies of Concepts
Entity
Courses Staff
Technical StaffAcademic Staff
Lecturer Senior Lecturer
Professor
CS Dept. Australia
Entity
UndergradCourses
GradCourses
People
StaffFaculty
AssistantProfessor
AssociateProfessor
Professor
CS Dept. US
Outline
• Information integration• Schema and Ontology (Semantic) integration• The matching problem - TerminologyThe matching problem - Terminology• Matching Dimensions (classification)• Techniques• Matching strategies (methods)• Systems
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Ontology definition
• An ontology is a pair O=(S, A)
– S = ontological signature (terms that lexicalize concepts and the relations between concepts)
– A = ontological axioms (restricting the intended meaning of the terms included in the signature)
(Kalfoglou & Schorlemmer, 2003; Kotis et al, 2006)
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Ontology Mapping
O1 = (S1, A1) to O2 = (S2, A2)
a morphism f:S1→S2 such that A2 f(A⊨ 1),
i.e. all interpretations that satisfy O2’s axioms also satisfy O1’s translated axioms
(Kalfoglou & Schorlemmer, 2003)04/19/23 Ontology Matching - K Kotis 18
a set of binary relations between
the ontological signatures
inclusion ( ) and ⊑equivalence (≡)
relations
Definition
• Mapping of ontologies:
The mapping between two ontologies can be defined as a morphism from one ontology to the other i.e. a collection of functions assigning the symbols used in one vocabulary to the symbols of the other
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Definition
• Alignment of ontologies:
The task of establishing a collection of binary relations between the vocabularies of two ontologies, i.e. pairs of ontology mappings.
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Ontology Alignment• Articulate a set of binary relations (inclusion
( ) and equivalence (≡)) between the ⊑ontological signatures
==> an alignment of the two ontologies • Instead of aligning two ontologies “directly”
through their signatures, we may specify the alignment of two ontologies O1 and O2 by means of a pair of ontology mappings from an intermediate source ontology O3
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S-morphism
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We can consider O3 to be part of a larger intermediate ontology and define the alignment of ontologies O1 and O2 by means of morphisms f1: S1→S3 and f2: S2 →S3, i.e. by means of their mapping to the intermediate ontology
Hidden intermediate ontology and the
semantic morphism
WordNet plays the role of an “intermediate”
Outline
• Information integration• Schema and Ontology (Semantic) integration• The matching problem - Terminology• Matching Dimensions (classification)Matching Dimensions (classification)• Techniques• Matching strategies (methods)• Systems
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Mediated vs point-to-point• Mediated approaches = use of an intermediate reference
ontology that provides more general concepts and adequate axioms for clarifying the meaning of domain-specific concepts– possibly not work in the “real world” of the Web, since a intermediate-
reference ontology that preserves the axioms of the source ontologies may not be always available or may be hard to be constructed
• Point-to-point approaches = missing the valuable knowledge that a reference ontology can provide in respect to the semantic relations among concepts
• Alternative = hidden intermediate reference ontology that is built on the fly using lexicons (senses) that express the intended meaning of ontologies’ concepts and user-specified semantic relations among concepts
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External knowledge• Apart from using an intermediate ontology as
an external source for facilitating the mapping/merging process other external source of information can be used as well:– Instances of concepts, – corpora of documents that have been annotated
using the specific ontologies, – previously identified mappings between ontologies, – other ontologies or lexicons.
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Classification – most common
• OWL models• Schema-level• Exact matching (0 or 1)• 1:1 and 1:many cardinality• Equivalence and Subsumption relations• Absolute Confidence (trust the mappings)
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Matching dimensions
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Granularitylayer
P. Shvaiko, J. Euzenat: A Survey of Schema-based Matching Approaches Journal on Data Semantics, 2005.
Techniquelayer
Input typelayer
Outline
• Information integration• Schema and Ontology (Semantic) integration• The matching problem - Terminology• Matching Dimensions (classification)• TechniquesTechniques• Matching strategies (methods)• Systems
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Semantic (informal semantics)
• semantic matching explores the mapping between the meanings of concept specifications by exploiting domain knowledge.
• Semantic matching specifies a similarity function in the form of a semantic relation (hyperonym, hyponym, meronym, part-of, etc) between the intension (necessary and/or sufficient conditions) of concepts.
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Semantic (informal semantics)• Semantic matching may rely to external information found in
lexicons, thesauruses or reference ontologies, incorporating semantic knowledge (mostly domain-dependent) into the process.
• An example is the exploitation of semantic knowledge in the WordNet lexicon by mapping senses to ontology concepts using information retrieval techniques (Kotis et al, 2006).
• Although semantic matching is considered to be the most important of the three, it is still rather difficult to be done completely automatically, avoiding any user involvement (Uschold, 2003; Kotis et al, 2006).
Human involvement vs. automation
• Automating the ontology matching process is still a key research issue. – There must always be a minimum set of human decisions
present. – Early techniques require human involvement in the final
stages of the process, for the users to verify the results and specify further mappings.
– Latest efforts, (e.g. in Kotis et al, 2006) place human involvement at the early stages of the mapping process, where humans validate and/or provide the intended informal meaning of ontology concepts.
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Human involvement vs. automation
• Automating the ontology matching process is still a key research issue. – Fully Automated methods result in lower precision and
recall of resulted mapping pairs (concepts and properties)– OAEI contest (since 2005) evaluates ontology matching
tools towards automating the process with the minimum precision and recall cost
– http://oaei.ontologymatching.org/
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Human involvement vs. automation
• Latest algorithms attempt to approximate similarities between concepts in an iterative way– combining also different kinds of matching
algorithms, without any user involvement.
Although they are promising efforts, more need to be done towards improving the mapping results.
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Outline
• Information integration• Schema and Ontology (Semantic) integration• The matching problem - Terminology• Matching Dimensions (classification)• Techniques• Matching strategies (methods)Matching strategies (methods)• Systems
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Matching strategies
• Ontology matching:
the computation of similarity functions towards discovering similarities between ontology concepts or/and properties pairs using combinations of lexical, structural, and semantic knowledge.
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Outline
• Information integration• Schema and Ontology (Semantic) integration• The matching problem - Terminology• Matching Dimensions (classification)• Techniques• Matching strategies (methods)• SystemsSystems
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Challenges
• Prior Matches (background knowledge)• Large scale evaluation
– Match very large real ontologies (e.g. Gene Ontology, Anatomy Ontology)
• Interactive approaches – Human involvement for 100% precision/recall
• Performance of systems– Precision/recall/complexity/speed– Runtime matching (e.g. in SW query answering)
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