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(SEMantic INTegrator) 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