Ontology mapping - an integrated approach. (2004)

by M Ehrig, Y Sure
Venue:Proceedings of the First European Semantic Web Symposium,
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A classification of schema-based matching approaches

by Pavel Shvaiko - 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- ..."
Abstract - Cited by 386 (21 self) - Add to MetaCart
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.

QOM – Quick ontology mapping

by Marc Ehrig, Steffen Staab - In Proc. 3rd International Semantic Web Conference (ISWC04 , 2004
"... Abstract. (Semi-)automatic mapping — also called (semi-)automatic alignment — of ontologies is a core task to achieve interoperability when two agents or services use different ontologies. In the existing literature, the focus has so far been on improving the quality of mapping results. We here cons ..."
Abstract - Cited by 150 (10 self) - Add to MetaCart
Abstract. (Semi-)automatic mapping — also called (semi-)automatic alignment — of ontologies is a core task to achieve interoperability when two agents or services use different ontologies. In the existing literature, the focus has so far been on improving the quality of mapping results. We here consider QOM, Quick Ontology Mapping, as a way to trade off between effectiveness (i.e. quality) and efficiency of the mapping generation algorithms. We show that QOM has lower run-time complexity than existing prominent approaches. Then, we show in experiments that this theoretical investigation translates into practical benefits. While QOM gives up some of the possibilities for producing high-quality results in favor of efficiency, our experiments show that this loss of quality is marginal. 1
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Bootstrapping ontology alignment methods with apfel

by Marc Ehrig, Steffen Staab, York Sure - In ISWC , 2005
"... Abstract. Ontology alignment is a prerequisite in order to allow for interoperation between different ontologies and many alignment strategies have been proposed to facilitate the alignment task by (semi-)automatic means. Due to the complexity of the alignment task, manually defined methods for (sem ..."
Abstract - Cited by 103 (1 self) - Add to MetaCart
Abstract. Ontology alignment is a prerequisite in order to allow for interoperation between different ontologies and many alignment strategies have been proposed to facilitate the alignment task by (semi-)automatic means. Due to the complexity of the alignment task, manually defined methods for (semi-)automatic alignment rarely constitute an optimal configuration of substrategies from which they have been built. In fact, scrutinizing current ontology alignment methods, one may recognize that most are not optimized for given ontologies. Some few include machine learning for automating the task, but their optimization by machine learning means is mostly restricted to the extensional definition of ontology concepts. With APFEL (Alignment Process Feature Estimation and Learning) we present a machine learning approach that explores the user validation of initial alignments for optimizing alignment methods. The methods are based on extensional and intensional ontology definitions. Core to APFEL is the idea of a generic alignment process, the steps of which may be represented explicitly. APFEL then generates new hypotheses for what might be useful features and similarity assessments and weights them by machine learning approaches. APFEL compares favorably in our experiments to competing approaches. 1

A String Metric for Ontology Alignment

by Giorgos Stoilos, Giorgos Stamou, Stefanos Kollias , 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 ..."
Abstract - Cited by 101 (2 self) - Add to MetaCart
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
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The Two Cultures: Mashing up Web 2.0 and the Semantic Web

by Anupriya Ankolekar, Markus Krötzsch, Thanh Tran, Denny Vrandecic - PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB. 2007 MAY 7-8 , 2007
"... A common perception is that there are two competing visions for the future evolution of the Web: the Semantic Web and Web 2.0. A closer look, though, reveals that the core technologies and concerns of these two approaches are complementary and that each field can and must draw from the other’s stren ..."
Abstract - Cited by 67 (3 self) - Add to MetaCart
A common perception is that there are two competing visions for the future evolution of the Web: the Semantic Web and Web 2.0. A closer look, though, reveals that the core technologies and concerns of these two approaches are complementary and that each field can and must draw from the other’s strengths. We believe that future web applications will retain the Web 2.0 focus on community and usability, while drawing on Semantic Web infrastructure to facilitate mashup-like information sharing. However, there are several open issues that must be addressed before such applications can become commonplace. In this paper, we outline a semantic weblogs scenario that illustrates the potential for combining Web 2.0 and Semantic Web technologies, while highlighting the unresolved issues that impede its realization. Nevertheless, we believe that the scenario can be realized in the short-term. We point to recent progress made in resolving each of the issues as well as future research directions for each of the communities.

A Large Scale Taxonomy Mapping Evaluation

by Paolo Avesani, Fausto Giunchiglia, Mikalai Yatskevich - 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 ..."
Abstract - Cited by 55 (18 self) - Add to MetaCart
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
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The DILIGENT Knowledge Processes

by Denny Vrandecic, Denny Vr, Sofia Pinto, York Sure, Christoph Tempich , 2005
"... Case Study Purpose: The ontology engineering methodology DILIGENT is presented, a methodology focussing on the evolution of ontologies instead of the initial design, thus recognizing that knowledge is a tangible and moving target. ..."
Abstract - Cited by 43 (1 self) - Add to MetaCart
Case Study Purpose: The ontology engineering methodology DILIGENT is presented, a methodology focussing on the evolution of ontologies instead of the initial design, thus recognizing that knowledge is a tangible and moving target.

A cognitive support framework for ontology mapping

by Sean M. Falconer, Margaret-anne Storey
"... Abstract. Ontology mapping is the key to data interoperability in the semantic web. This problem has received a lot of research attention, however, the research emphasis has been mostly devoted to automating the mapping process, even though the creation of mappings often involve the user. As industr ..."
Abstract - Cited by 30 (4 self) - Add to MetaCart
Abstract. Ontology mapping is the key to data interoperability in the semantic web. This problem has received a lot of research attention, however, the research emphasis has been mostly devoted to automating the mapping process, even though the creation of mappings often involve the user. As industry interest in semantic web technologies grows and the number of widely adopted semantic web applications increases, we must begin to support the user. In this paper, we combine data gathered from background literature, theories of cognitive support and decision making, and an observational case study to propose a theoretical framework for cognitive support in ontology mapping tools. We also describe a tool called COGZ that is based on this framework. 1
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AlViz—A Tool for Visual Ontology Alignment

by Monika Lanzenberger, Jennifer Sampson - In Proceedings of the 10th International Conference on Information Visulization , 2006
"... We introduce a multiple-view tool called AlViz, which supports the alignment of ontologies visually. Ontologies play an important role for interoperability between orga-nizations and for the semantic web because they aim at capturing domain knowledge in a generic way and provide a consensual underst ..."
Abstract - Cited by 22 (2 self) - Add to MetaCart
We introduce a multiple-view tool called AlViz, which supports the alignment of ontologies visually. Ontologies play an important role for interoperability between orga-nizations and for the semantic web because they aim at capturing domain knowledge in a generic way and provide a consensual understanding of a domain. Alignment is the process where for each entity in one ontology we try to find a corresponding entity in the second ontology with the same or the closest meaning. Existing ontology alignment tools do not adequately provide a way for users to anal-yse the results. While many alignment tools generate lists of mappings it is difficult to analyse these alignments with-out examining every pairwise correspondence in the output files and even then it is an overwhelming task. We pro-pose the use of visualization techniques to facilitate user understanding of the ontology alignment results. AlViz is implemented as a tab plug-in for Protégé.
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A large scale dataset for the evaluation of ontology matching systems

by Fausto Giunchiglia, Mikalai Yatskevich, Paolo Avesani, Pavel Shvaiko - The Knowledge Engineering Review Journal , 2008
"... Recently, the number of ontology matching techniques and systems has increased significantly. This makes the issue of their evaluation and comparison more severe. One of the challenges of the ontology matching evaluation is in building large scale evaluation datasets. In fact, the number of possible ..."
Abstract - Cited by 18 (6 self) - Add to MetaCart
Recently, the number of ontology matching techniques and systems has increased significantly. This makes the issue of their evaluation and comparison more severe. One of the challenges of the ontology matching evaluation is in building large scale evaluation datasets. In fact, the number of possible correspondences between two ontologies grows quadratically with respect to the numbers of entities in these ontologies. This often makes the manual construction of the evaluation datasets demanding to the point of being infeasible for large scale matching tasks. In this paper we present an ontology matching evaluation dataset composed of thousands of matching tasks, called TaxME2. It was built semi-automatically out of the Google, Yahoo and Looksmart web directories. We evaluated TaxME2 by exploiting the results of almost two dozen of state of the art ontology matching systems. The experiments indicate that the dataset possesses the desired key properties, namely it is error-free, incremental, discriminative, monotonic, and hard for the state of the art ontology matching systems. 1
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