ONTOLOGY MATCHINGPart 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• 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

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

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

6.1 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

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

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

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

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

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

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

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

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

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)

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

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

6.2 Instance-based systems• 6.2.12 sPLMap(probabilistic, logic-based mapping between

schemas)• framework that combines logics with probability theory

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

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

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

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

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

6.3 Mixed, schema-based and instance-based systems• 6.3.11 Corpus-based matching

• Besides input information available from schema under consideration

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