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Ontology Matching

Ontology Matching. Part III: Systems and evaluation. 6. Overview of matching systems. 1. Schema-level information 2. Instance-level information 3. Both schema-level and instance-level information 4. overview meta-matching system. 6.1 Schema-based systems.

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Ontology Matching

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  1. Ontology Matching Part III: Systems and evaluation

  2. 6. Overview of matching systems • 1. Schema-level information • 2. Instance-level information • 3. Both schema-level and instance-level information • 4. overview meta-matching system

  3. 6.1 Schema-based systems • 6.1.1 DELTA(Data Element Tool-based Analysis) • discover attributes correspondences among database schemas • relational schemas and extended entity-relationship(EER) • use textual similarities • returns a ranked list of documents • 6.1.2 Hovy • heuristics used to match large-scale ontologies • Three types of matchers: • concept names • concept definitions • Taxonomy structure • the combined scores in descending order

  4. 6.1 Schema-based systems • 6.1.3 TransScm • provides data translation and conversion mechanisms • by using rules, alignment is produced • this alignment is used to translate data instances • 6.1.4 DIKE(Database Intentional Knowledge Extractor) • supporting construction of cooperative information(CISs) • takes a set of databases belonging to the CIS • Builds a kind of mediated schema • 6.1.5 SKAT and ONION(Semantic Knowledge Articulation Tool) • discovers mappings between two ontologies • input ontologies -> graphs • rules -> in first order logic • ONION is successor system to SKAT

  5. 6.1 Schema-based systems • 6.1.6 Artemis(Analysis of Requirements: Tool Environment for Multiple Information Systems) • a module of the MOMIS • performs affinity-based analysis and hierarchical clustering of database schema elements • 6.1.7 H-Match • ontology matching system • for open networked systems • inputs two ontologies and output correspondences • 6.1.8 Tess(Type Evolution Software System) • support schema evolution by matching the old and the new versions • Schemas are viewed as collection of types • Matching is viewed as generation of derivation rules

  6. 6.1 Schema-based systems • 6.1.9 Anchor-Prompt • formerly known as SMART • ontology merging and alignment tool • Sequential matching algorithm that takes as input two ontologies • handles OWL and RDF schema • 6.1.10 OntoBuilder • information seeking on the web • operates in two phases: • ontology creation(the training phase) • ontology adaptation(the adaptation phase) • 6.1.11 Cupid • implements an algorithm comprising linguistic and structural schema matching techniques • computing similarity coefficients

  7. 6.1 Schema-based systems • 6.1.12 COMA and COMA++(COmbination of MAtching algorithms) • schema matching tool based on parallel composition of matchers • provides: • extensible library of matching algorithms • a framework for combining obtained results • platform for the evolution of the effectiveness • 6.1.13 Similarity flooding • is based on the idea of similarity propagation • Schemas are presented as directed labeled graphs • 6.1.14 XClust • tool for integrating multiple DTDs • based on clustering

  8. 6.1 Schema-based systems • 6.1.15 ToMAS(Toronto Mapping Adaptation System) • automatically detects and adapts mappings • assumed: • the matching step has already been performed • correspondences have already been made operational • 6.1.16 MapOnto • constructing complex mappings • inputs: • an ontology specified in an ontology representation language(OWL) • relational or XML schema • simple correspondences between XML attributes and ontology datatype properties

  9. 6.1 Schema-based systems • 6.1.17 OntoMerge • ontology translation on the semantic web • dataset translation • generating ontology extensions • query answering from multiple ontologeis • perform ontology translation by ontology merging and automated reasoning • 6.1.18 CtxMatch and CtxMatch2 • uses a semantic matching approach • translates the ontology matching problem into the logical validity problem

  10. 6.1 Schema-based systems • 6.1.19 S-Match • the first version rationalized re-implementation of CtxMatch with a few added functionalities • evolutions • limited to tree-like structures • 6.1.20 HCONE • domain ontology matching and merging • first,an alignment between two input ontologies is computed • then, the alignment is processed • 6.1.21 MoA • ontology merging and alignment tool • consists of: • Library of methods for importing, matching, modifying, merging ontologies • Shell for using those methods • based on concept (dis)similarity derived from linguistic clue

  11. 6.1 Schema-based systems • 6.1.22 ASCO • discovers pairs of corresponding elements in two input ontologies • handles ontologies in RDF Schema and computes alignments between classes, relations, and classes and relations • new version, ASCO2, deals with OWL ontologies • 6.1.23 BayesOWL and BN mapping • probabilistic framework • includes the Bayesian Network mapping module • in three steps: • two input ontologies are translated into two Bayesian networks • matching candidates are generated between two Bayesian networks • concepts of the second ontology are classified with respect to the concepts of the first ontology

  12. 6.1 Schema-based systems • 6.1.24 OMEN(Ontology Mapping ENhancer) • probabilistic ontology matching system based on Bayesian network • inputs: two ontologies and initial probability distribution derived • returns: a structure level matching algorithm • 6.1.25 DCM framework • a middleware system • inputs: multiple schemas • returns: alignment between all of them

  13. 6.2 Instance-based systems • 6.2.1 T-tree • an environment for generating taxonomies and classes from objects(instances) • Infer dependencies between classes(bridges) of different ontologies • input: a set of source taxonomies(viewpoints) and a destination viewpoint • returns: all the bridges in a minimal fashion • 6.2.2 CAIMAN • a system for document exchange • Calculate a probability measure between the concepts of two ontologies

  14. 6.2 Instance-based systems • 6.2.3 FCA-merge • uses formal concept analysis techniques • tree steps: • Instance extraction • concept lattice computation • Interactive generation of the final merged ontology • 6.2.4 LSD(Learning Source Descriptions) • discovers one-to-one alignments between the elements of source schemas and a mediated schema • learn from the mappings created manually between the mediated schema and some of the source schemas

  15. 6.2 Instance-based systems • 6.2.5 GLUE • a successor of LSD • employs mulitple machine learning techiques • joint distributions of the classes • 6.2.6 iMAP • discovers one-to-one(amount ≡ quantity) and complex(address ≡concat(city, street)) mapping between relational database schemas. • uses multiple basic matchers(searches)

  16. 6.2 Instance-based systems • 6.2.7 Automatch • mappings between the attributes of database schemas • assumption: • several schemas from the domain under consideration have already been manually matched by domain experts • 6.2.8 SBI&NB • SBI(Similarity-Based Integration) • SBI&NB is extension of SBI • Determine correspondences between classes of two classifications by statistically comparing the memberships of the documents to these classes

  17. 6.2 Instance-based systems • 6.2.9 Kang and Naughton • a structural instance-based approach • two table instances are taken as input • 6.2.10 Dumas(DUplicate-based MAtching of Schemas) • identifies one-to-one alignment between attributes by analyzing the duplicates in data instances of the relational schemas • looks for similar rows or tuples • 6.2.11 Wang and colleagues • one-to-one alignments among the web databases • presents a combined schema model • Global-interface, global-result, interface-result, interface-interface, and result-result

  18. 6.2 Instance-based systems • 6.2.12 sPLMap(probabilistic, logic-based mapping between schemas) • framework that combines logics with probability theory

  19. 6.3 Mixed, schema-based and instance-based systems • 6.3.1 SEMINT(SEManticINTegrator) • a tool based on neural networks • supports access to a variety of database system • extracts from two databases all the necessary information • using a neural network as a classifier • 6.3.2 Clio • managing and facilitating data transformation and integration • focused on making the alignment operational • transforms the input schemas into an internal representation • taking the value correspondences(the alignment) together with constraints coming form the input schema

  20. 6.3 Mixed, schema-based and instance-based systems • 6.3.3 IF-Map(Information-Flow-based Map) • based on the Barwise-Seligman theory of information flow • matches two local ontologies by looking at how these are related to a common reference ontology • 6.3.4 NOM(Naïve Ontology Mapping) and QOM(Quick Ontology Mapping) • NOM adopts parallel composition of matchers from COMA • QOM is a variation of the NOM • QOM produces correspondences fast

  21. 6.3 Mixed, schema-based and instance-based systems • 6.3.5 oMap • a system for matching OWL ontologies • built on top of the Alignment API • uses several matchers(classifiers) • 6.3.6 Xu and Embley • proposed composition approach to discover one-to-one alignments, onto-to-many and many-to-many correspondences between graph-like structures • matches by combination of multiple matchers and with the help of external knowledge recourses

  22. 6.3 Mixed, schema-based and instance-based systems • 6.3.7 Wise-Integrator • performs automatic integration of Web Interfaces of Search Engines • unified interface to e-commerce search engines of the same domain of interest • Attribute matching based on two types of matches: positive and predictive • 6.3.8 OLA(OWL Lite Aligner) • balancing the contribution of each of the components that compose an ontology • inputs:OWL

  23. 6.3 Mixed, schema-based and instance-based systems • 6.3.9 Falcon-AO • a system for matching OWL ontologies • components: those for performing linguistic and structure matching • LMO is a linguistic matcher • GMO is a bipartite graph matcher • 6.3.10 RiMOM(Risk Minimization based Ontology Mapping) • inspired by Bayesian decision theory • inputs: two ontologies • Aims at an optimal and automatic discovery of alignments which can be complex

  24. 6.3 Mixed, schema-based and instance-based systems • 6.3.11 Corpus-based matching • Besides input information available from schema under consideration

  25. 6.4 Meta-matching systems • 6.4.1 APFEL(Alignment Process Features Estimation and Learning) • A machine learning approach that explores user validation of initial alignments for optimizing automatically the configuration parameters of some of the matching strategies of the system • 6.4.2 eTuner • Models: • L is library of matching components • G is a directed graph which encodes • K is a set of knobs to be set

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