%0 Book Section %1 Rahm11p3 %A Rahm, Erhard %B Schema Matching and Mapping %C Heidelberg %D 2011 %E Bellahsene, Zohra %E Bonifati, Angela %E Rahm, Erhard) %I Springer %K 01614 springer paper ai algorithm data information knowledge matching ontology engineering xml %P 3--27 %R 10.1007/978-3-642-16518-4_1 %T Towards Large-Scale Schema and Ontology Matching %X The purely manual specification of semantic correspondences between schemas is almost infeasible for very large schemas or when many different schemas have to be matched. Hence, solving such large-scale match tasks asks for automatic or semiautomatic schema matching approaches. Large-scale matching needs especially to be supported for XML schemas and different kinds of ontologies due to their increasing use and size, e.g., in e-business and web and life science applications. Unfortunately, correctly and efficiently matching large schemas and ontologies are very challenging, and most previous match systems have only addressed small match tasks. We provide an overview about recently proposed approaches to achieve high match quality or/and high efficiency for large-scale matching. In addition to describing some recent matchers utilizing instance and usage data, we cover approaches on early pruning of the search space, divide and conquer strategies, parallel matching, tuning matcher combinations, the reuse of previous match results, and holistic schema matching. We also provide a brief comparison of selected match tools. %& 1

@incollection{Rahm11p3, abstract = {The purely manual specification of semantic correspondences between schemas is almost infeasible for very large schemas or when many different schemas have to be matched. Hence, solving such large-scale match tasks asks for automatic or semiautomatic schema matching approaches. Large-scale matching needs especially to be supported for XML schemas and different kinds of ontologies due to their increasing use and size, e.g., in e-business and web and life science applications. Unfortunately, correctly and efficiently matching large schemas and ontologies are very challenging, and most previous match systems have only addressed small match tasks. We provide an overview about recently proposed approaches to achieve high match quality or/and high efficiency for large-scale matching. In addition to describing some recent matchers utilizing instance and usage data, we cover approaches on early pruning of the search space, divide and conquer strategies, parallel matching, tuning matcher combinations, the reuse of previous match results, and holistic schema matching. We also provide a brief comparison of selected match tools.}, added-at = {2015-10-31T17:48:05.000+0100}, address = {Heidelberg}, author = {Rahm, Erhard}, biburl = {https://www.bibsonomy.org/bibtex/24e0b4ef9a6298d029d3d6d3b406e2333/flint63}, booktitle = {Schema Matching and Mapping}, chapter = 1, crossref = {BellahseneBonifatiRahm2011}, doi = {10.1007/978-3-642-16518-4_1}, editor = {Bellahsene, Zohra and Bonifati, Angela and Rahm, Erhard)}, file = {SpringerLink:2011/Rahm11p3.pdf:PDF}, groups = {public}, interhash = {d54ca818bcb3feecb3fa9dd9b010c29b}, intrahash = {4e0b4ef9a6298d029d3d6d3b406e2333}, keywords = {01614 springer paper ai algorithm data information knowledge matching ontology engineering xml}, pages = {3--27}, publisher = {Springer}, timestamp = {2018-04-16T11:59:05.000+0200}, title = {Towards Large-Scale Schema and Ontology Matching}, username = {flint63}, year = 2011 }