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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.
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;
Ontology Mapping - An Integrated Approach
, 2004
"... Ontology mapping is important when working with more than one ontology. Typically similarity considerations are the basis for this. In this paper an approach to integrate various similarity methods is presented. In brief, we determine similarity through rules which have been encoded by ontology e ..."
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Cited by 137 (9 self)
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Ontology mapping is important when working with more than one ontology. Typically similarity considerations are the basis for this. In this paper an approach to integrate various similarity methods is presented. In brief, we determine similarity through rules which have been encoded by ontology experts.
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
A survey on ontology mapping
, 2006
"... Ontology is increasingly seen as a key factor for enabling interoperability across heterogeneous systems and semantic web applications. Ontology mapping is required for combining distributed and heterogeneous ontologies. Developing such ontology mapping has been a core issue of recent ontology resea ..."
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Cited by 133 (0 self)
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Ontology is increasingly seen as a key factor for enabling interoperability across heterogeneous systems and semantic web applications. Ontology mapping is required for combining distributed and heterogeneous ontologies. Developing such ontology mapping has been a core issue of recent ontology research. This paper presents ontology mapping categories, describes the characteristics of each category, compares these characteristics, and surveys tools, systems, and related work based on each category of ontology mapping. We believe this paper provides readers with a comprehensive understanding of ontology mapping and points to various research topics about the specific roles of ontology mapping.
Learning to Match Ontologies on the Semantic Web
, 2003
"... On the Semantic Web, data will inevitably come from many different ontologies, and information processing across ontologies is not possible without knowing the semantic mappings between them. Manually finding such mappings is tedious, error-prone, and clearly not possible at the Web scale. Hence, th ..."
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Cited by 130 (2 self)
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On the Semantic Web, data will inevitably come from many different ontologies, and information processing across ontologies is not possible without knowing the semantic mappings between them. Manually finding such mappings is tedious, error-prone, and clearly not possible at the Web scale. Hence, the development of tools to assist in the ontology mapping process is crucial to the success of the Semantic Web. We describe GLUE, a system that employs machine learning techniques to find such mappings. Given two ontologies, for each concept in one ontology GLUE finds the most similar concept in the other ontology. We give well-founded probabilistic definitions to several practical similarity measures, and show that GLUE can work with all of them. Another key feature of GLUE is that it uses multiple learning strategies, each of which exploits well a different type of information either in the data instances or in the taxonomic structure of the ontologies. To further improve matching accuracy, we extend GLUE to incorporate commonsense knowledge and domain constraints into the matching process. Our approach is thus distinguished in that it works with a variety of well-defined similarity notions and that it efficiently incorporates multiple types of knowledge. We describe a set of experiments on several real-world domains, and show that GLUE proposes highly accurate semantic mappings. Finally, we extend GLUE to find complex mappings between ontologies, and describe experiments that show the promise of the approach.
PROMPTDIFF: A Fixed-Point Algorithm for Comparing Ontology Versions
- IN EIGHTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-2002
, 2002
"... As ontology development becomes a more ubiquitous and collaborative process, the developers face the problem of maintaining versions of ontologies akin to maintaining versions of software code in large software projects. Versioning systems for software code provide mechanisms for tracking versi ..."
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Cited by 110 (10 self)
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As ontology development becomes a more ubiquitous and collaborative process, the developers face the problem of maintaining versions of ontologies akin to maintaining versions of software code in large software projects. Versioning systems for software code provide mechanisms for tracking versions, checking out versions for editing, comparing different versions, and so on. We can directly reuse many of these mechanisms for ontology versioning. However, version comparison for code is based on comparing text files---an approach that does not work for comparing ontologies. Two ontologies can be identical but have different text representation. We have
A String Metric for Ontology Alignment
, 2005
"... Abstract. Ontologies are today a key part of every knowledge based system. They provide a source of shared and precisely defined terms, resulting in system interoperability by knowledge sharing and reuse. Unfortunately, the variety of ways that a domain can be conceptualized results in the creation ..."
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Cited by 101 (2 self)
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Abstract. Ontologies are today a key part of every knowledge based system. They provide a source of shared and precisely defined terms, resulting in system interoperability by knowledge sharing and reuse. Unfortunately, the variety of ways that a domain can be conceptualized results in the creation of different ontologies with contradicting or overlapping parts. For this reason ontologies need to be brought into mutual agreement (aligned). One important method for ontology alignment is the comparison of class and property names of ontologies using stringdistance metrics. Today quite a lot of such metrics exist in literature. But all of them have been initially developed for different applications and fields, resulting in poor performance when applied in this new domain. In the current paper we present a new string metric for the comparison of names which performs better on the process of ontology alignment as well as to many other field matching problems. 1
A Large Scale Taxonomy Mapping Evaluation
- In Proceedings of ISWC
, 2005
"... Abstract. Matching hierarchical structures, like taxonomies or web directories, is the premise for enabling interoperability among heterogenous data organizations. While the number of new matching solutions is increasing the evaluation issue is still open. This work addresses the problem of comparis ..."
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Cited by 55 (18 self)
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Abstract. Matching hierarchical structures, like taxonomies or web directories, is the premise for enabling interoperability among heterogenous data organizations. While the number of new matching solutions is increasing the evaluation issue is still open. This work addresses the problem of comparison for pairwise matching solutions. A methodology is proposed to overcome the issue of scalability. A large scale dataset is developed based on real world case study namely, the web directories of Google, Looksmart and Yahoo!. Finally, an empirical evaluation is performed which compares the most representative solutions for taxonomy matching. We argue that the proposed dataset can play a key role in supporting the empirical analysis for the research effort in the area of taxonomy matching. 1
