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592
COMA - A system for flexible combination of Schema Matching Approaches
- In VLDB
, 2002
"... Schema matching is the task of finding semantic correspondences between elements of two schemas. It is needed in many database applications, such as integration of web data sources, data warehouse loading and XML message mapping. To reduce the amount of user effort as much as possible, automati ..."
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Cited by 443 (12 self)
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Schema matching is the task of finding semantic correspondences between elements of two schemas. It is needed in many database applications, such as integration of web data sources, data warehouse loading and XML message mapping. To reduce the amount of user effort as much as possible, automatic approaches combining several match techniques are required. While such match approaches have found considerable interest recently, the problem of how to best combine different match algorithms still requires further work. We have thus developed the COMA schema matching system as a platform to combine multiple matchers in a flexible way. We provide a large spectrum of individual matchers, in particular a novel approach aiming at reusing results from previous match operations, and several mechanisms to combine the results of matcher executions. We use COMA as a framework to com- prehensively evaluate the effectiveness of different matchers and their combinations for real-world sche- mas. The results obtained so far show the superiority of combined match approaches and indicate the high value of reuse-oriented strategies.
A classification of schema-based matching approaches
- JOURNAL ON DATA SEMANTICS
, 2005
"... Schema/ontology matching is a critical problem in many application domains, such as, semantic web, schema/ontology integration, data warehouses, e-commerce, catalog matching, etc. Many diverse solutions to the matching problem have been proposed so far. In this paper we present a taxonomy of schema- ..."
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Cited by 386 (21 self)
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Schema/ontology matching is a critical problem in many application domains, such as, semantic web, schema/ontology integration, data warehouses, e-commerce, catalog matching, etc. Many diverse solutions to the matching problem have been proposed so far. In this paper we present a taxonomy of schema-based matching techniques that builds on the previous work on classifying schema matching approaches. Some innovations are in introducing new criteria which distinguish between matching techniques relying on diverse semantic clues. In particular, we distinguish between heuristic and formal techniques at schemalevel; and implicit and explicit techniques at element- and structure-level. Based on the classification proposed we overview some of the recent schema/ontology matching systems pointing which part of the solution space they cover.
Comparison of Schema Matching Evaluations
- In Proceedings of the 2nd Int. Workshop on Web Databases (German Informatics Society
, 2002
"... Recently, schema matching has found considerable interest in both research and practice. Determining matching components of database or XML schemas is needed in many applications, e.g. for E-business and data integration. ..."
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Cited by 186 (7 self)
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Recently, schema matching has found considerable interest in both research and practice. Determining matching components of database or XML schemas is needed in many applications, e.g. for E-business and data integration.
S-match: an algorithm and an implementation of semantic matching
- In Proceedings of ESWS
, 2004
"... semantic matching ..."
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An API for ontology alignment
- In Proc. 3rd international semantic web conference
, 2004
"... Relating ontologies is very important for many ontology-based applications and more important in open environments like the semantic web. The relations between ontology entities can be obtained by ontology matching and represented as alignments. Hence, alignments must be taken into account in ontolo ..."
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Cited by 176 (28 self)
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Relating ontologies is very important for many ontology-based applications and more important in open environments like the semantic web. The relations between ontology entities can be obtained by ontology matching and represented as alignments. Hence, alignments must be taken into account in ontology management. This chapter establishes the requirements for alignment management. After a brief introduction to matching and alignments, we justify the consideration of alignments as independent entities and provide the life cycle of alignments. We describe the important functions of editing, managing and exploiting alignments and illustrate them with existing components.
Rondo: A Programming Platform for Generic Model Management
, 2003
"... Model management aims at reducing the amount of programming needed for the development of metadata-intensive applications. We present a first complete prototype of a generic modelmanagement system, in which high-level operators are used to manipulate models and mappings between models. We define the ..."
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Cited by 155 (10 self)
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Model management aims at reducing the amount of programming needed for the development of metadata-intensive applications. We present a first complete prototype of a generic modelmanagement system, in which high-level operators are used to manipulate models and mappings between models. We define the key conceptual structures: models, morphisms, and selectors, and describe their use and implementation. We specify the semantics of the known model-management operators applied to these structures, suggest new ones, and develop new algorithms for implementing the individual operators. We examine the solutions for two model-management tasks that involve manipulations of relational schemas, XML schemas, and SQL views. 1.
Semantic integration research in the database community: A brief survey
- AI Magazine
, 2005
"... Semantic integration has been a long-standing challenge for the database community. It has received steady attention over the past two decades, and has now become a prominent area of database research. In this article, we first review database applications that require semantic integration, and disc ..."
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Cited by 145 (4 self)
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Semantic integration has been a long-standing challenge for the database community. It has received steady attention over the past two decades, and has now become a prominent area of database research. In this article, we first review database applications that require semantic integration, and discuss the difficulties underlying the integration process. We then describe recent progress and identify open research issues. We will focus in particular on schema matching, a topic that has received much attention in the database community, but will also discuss data matching (e.g., tuple deduplication), and open issues beyond the match discovery context (e.g., reasoning with matches, match verification and repair, and reconciling inconsistent data values). For previous surveys of database research on semantic integration, see (Rahm & Bernstein 2001;
iMAP: discovering complex semantic matches between database schemas
- in: Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, ACM
, 2004
"... Creating semantic matches between disparate data sources is fundamental to numerous data sharing efforts. Manually creating matches is extremely tedious and error-prone. Hence many recent works have focused on automating the matching process. To date, however, virtually all of these works deal only ..."
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Cited by 140 (3 self)
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Creating semantic matches between disparate data sources is fundamental to numerous data sharing efforts. Manually creating matches is extremely tedious and error-prone. Hence many recent works have focused on automating the matching process. To date, however, virtually all of these works deal only with one-to-one (1-1) matches, such as address = location. They do not consider the important class of more complex matches, such as address = concat(city,state) and room-price = room-rate * (1 + tax-rate). We describe the iMAP system which semi-automatically discovers both 1-1 and complex matches. iMAP reformulates schema matching as a search in an often very large or infinite match space. To search effectively, it employs a set of searchers, each discovering specific types of complex matches. To further improve matching accuracy, iMAP exploits a variety of domain knowledge, including past complex matches, domain integrity constraints, and overlap data. Finally, iMAP introduces a novel feature that generates explanation of predicted matches, to provide insights into the matching process and suggest actions to converge on correct matches quickly. We apply iMAP to several real-world domains to match relational tables, and show that it discovers both 1-1 and complex matches with high accuracy. 1.
Ontology Matching: A Machine Learning Approach
- Handbook on Ontologies in Information Systems
, 2003
"... Finally, we describe a set of experiments on several real-world domains, and show that GLUE proposes highly accurate semantic mappings. 1 A Motivating Example: the Semantic Web The current World-Wide Web has well over 1.5 billion pages [2], but the vast majority of them are in human-readable forma ..."
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Cited by 136 (2 self)
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Finally, we describe a set of experiments on several real-world domains, and show that GLUE proposes highly accurate semantic mappings. 1 A Motivating Example: the Semantic Web The current World-Wide Web has well over 1.5 billion pages [2], but the vast majority of them are in human-readable format only (e.g., HTML). As Work done while the author was at the University of Washington, Seattle 2 AnHai Doan et al. a consequence software agents (softbots) cannot understand and process this information, and much of the potential of the Web has so far remained untapped. In response, researchers have created the vision of the Semantic Web [5], where data has structure and ontologies describe the semantics of the data. When data is marked up using ontologies, softbots can better understand the semantics and therefore more intelligently locate and integrate data for a wide variety of tasks. The following example illustrates the vision of the Semantic Web. Example 1. Suppose you want to fi
