A survey on ontology mapping
---
paper_title: Learning to Match the Schemas of Data Sources: A Multistrategy Approach

paper_content:

The problem of integrating data from multiple data sources—either on the Internet or within enterprises—has received much attention in the database and AI communities. The focus has been on building data integration systems that provide a uniform query interface to the sources. A key bottleneck in building such systems has been the laborious manual construction of semantic mappings between the query interface and the source schemas. Examples of mappings are “element location maps to address” and “price maps to listed-price”. We propose a multistrategy learning approach to automatically find such mappings. The approach applies multiple learner modules, where each module exploits a different type of information either in the schemas of the sources or in their data, then combines the predictions of the modules using a meta-learner. Learner modules employ a variety of techniques, ranging from Naive Bayes and nearest-neighbor classification to entity recognition and information retrieval. We describe the LSD system, which employs this approach to find semantic mappings. To further improve matching accuracy, LSD exploits domain integrity constraints, user feedback, and nested structures in XML data. We test LSD experimentally on several real-world domains. The experiments validate the utility of multistrategy learning for data integration and show that LSD proposes semantic mappings with a high degree of accuracy.
---
paper_title: A framework for ontology integration

paper_content:

One of the basic problems in the development of techniques for the semantic web is the integration of ontologies. Indeed, the web is constituted by a variety of information sources, each expressed over a certain ontology, and in order to extract information from such sources, their semantic integration and reconciliation in terms of a global ontology is required. In this paper, we address the fundamental problem of how to specify the mapping between the global ontology and the local ontologies. We argue that for capturing such mapping in an appropriate way, the notion of query is a crucial one, since it is very likely that a concept in one ontology corresponds to a view (i.e., a query) over the other ontologies. As a result query processing in ontology integration systems is strongly related to view-based query answering in data integration.
---
paper_title: Ontologies for Enterprise Knowledge Management

paper_content:

Several challenges exist related to applying ontologies in real-world environments. The authors present an integrated enterprise-knowledge management architecture, focusing on how to support multiple ontologies and manage ontology evolution.
---
paper_title: Representing and Reasoning about Mappings between Domain Models

paper_content:

Mappings between disparate models are fundamental to any application that requires interoperability between heterogeneous data and applications. Generating mappings is a labor-intensive and error prone task. To build a system that helps users generate mappings, we need an explicit representation of mappings. This representation needs to have well-defined semantics to enable reasoning and comparison between mappings. This paper first presents a powerful framework for defining languages for specifying mappings and their associated semantics. We examine the use of mappings and identify the key inference problems associated with mappings. These properties can be used to determine whether a mapping is adequate in a particular context. Finally, we consider an instance of our framework for a language representing mappings between relational data. We present sound and complete algorithms for the corresponding inference problems.
---
paper_title: Ontology Mapping for Interoperability in Semantic Web

paper_content:

Ontology mapping is the process whereby two ontologies are semantically related at conceptual level and the source ontology instances are transformed into target ontology entities according to those semantic relations. The objective of MAFRA–MApping FRAmework – is to cover all the phases of the ontology mapping process, including analysis, specification, representation, execution and evolution. The MAFRA Toolkit is an implementation of MAFRA, adopting an open architecture in order to observe the Semantic Web requirements, namely performance and transformation capabilities. One of the MAFRA Toolkit novelties respects its service-oriented approach, which claims that the capabilities of an ontology mapping system dependent on what transformations are present. Independent, plug able services are then responsible for the instances transformations, but they also provide support for other ontology mapping tasks like automatic specification of semantic relations, negotiation and evolution.
---
paper_title: Knowledge Engineering: Principles and Methods

paper_content:

This paper gives an overview of the development of the field of Knowledge Engineering over the last 15 years. We discuss the paradigm shift from a transfer view to a modeling view and describe two approaches which considerably shaped research in Knowledge Engineering: Role-limiting Methods and Generic Tasks. To illustrate various concepts and methods which evolved in recent years we describe three modeling frameworks: CommonKADS, MIKE and PROTEGE-II. This description is supplemented by discussing some important methodological developments in more detail: specification languages for knowledge-based systems, problem-solving methods and ontologies. We conclude by outlining the relationship of Knowledge Engineering to Software Engineering, Information Integration and Knowledge Management.
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paper_title: Ontology Mapping: The State of the Art

paper_content:

Ontology mapping is seen as a solution provider in today's landscape of ontology research. As the number of ontologies that are made publicly available and accessible on the Web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support the tasks envisaged by a distributed environment like the Semantic Web. Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. Developing such mappings has been the focus of a variety of works originating from diverse communities over a number of years. In this article we comprehensively review and present these works. We also provide insights on the pragmatics of ontology mapping and elaborate on a theoretical approach for defining ontology mapping.
---
paper_title: Synthesizing an integrated ontology

paper_content:

The Mediator Environment for Multiple Information Sources (Momis) supports semiautomatic building, annotation, and extension of domain ontologies.
---
paper_title: Semantic Coordination: A New Approach and an Application

paper_content:

Semantic coordination, namely the problem of finding an agreement on the meaning of heterogeneous semantic models, is one of the key issues in the development of the Semantic Web. In this paper, we propose a new algorithm for discovering semantic mappings across hierarchical classifications based on a new approach to semantic coordination. This approach shifts the problem of semantic coordination from the problem of computing linguistic or structural similarities (what most other proposed approaches do) to the problem of deducing relations between sets of logical formulae that represent the meaning of concepts belonging to different models. We show how to apply the approach and the algorithm to an interesting family of semantic models, namely hierarchical classifications, and present the results of preliminary tests on two types of hierarchical classifications, web directories and catalogs. Finally, we argue why this is a significant improvement on previous approaches.
---
paper_title: C-OWL : Contextualizing Ontologies

paper_content:

Ontologies are shared models of a domain that encode a view which is common to a set of different parties. Contexts are local models that encode a party's subjective view of a domain. In this paper we show how ontologies can be contextualized, thus acquiring certain useful properties that a pure shared approach cannot provide. We say that an ontology is contextualized or, also, that it is a contextual ontology, when its contents are kept local, and therefore not shared with other ontologies, and mapped with the contents of other ontologies via explicit (context) mappings. The result is Context OWL (C-OWL), a language whose syntax and semantics have been obtained by extending the OWL syntax and semantics to allow for the representation of contextual ontologies.
---
paper_title: SMART : Automated Support for Ontology Merging and Alignment

paper_content:

As researchers in the ontology-design field develop the content of a growing number of ontologies, the need for sharing and reusing this body of knowledge becomes increasingly critical. Aligning and merging existing ontologies, which is usually handled manually, often constitutes a large and tedious portion of the sharing process. We have developed SMART, an algorithm that provides a semi-automatic approach to ontology merging and alignment. SMART assists the ontology developer by performing certain tasks automatically and by guiding the developer to other tasks for which his intervention is required. SMART also determines possible inconsistencies in the state of the ontology that may result from the user’s actions, and suggests ways to remedy these inconsistencies. We define the set of basic operations that are performed during merging and alignment of ontologies, and determine the effects that invocation of each of these operations has on the process. SMART is based on an extremely general knowledge model and, therefore, can be applied across various platforms. 1. Merging Versus Alignment In recent years, researchers have developed many ontologies. These different groups of researchers are now beginning to work with one another, so they must bring together these disparate source ontologies. Two approaches are possible: (1) merging the ontologies to create a single coherent ontology, or (2) aligning the ontologies by establishing links between them and allowing the aligned ontologies to reuse information from one another. As an illustration of the possible processes that establish correspondence between different ontologies, we consider the ontologies that natural languages embody. A researcher trying to find common ground between two such languages may perform one of several tasks. He may create a mapping between the two languages to be used in, say, a machine-translation system. Differences in the ontologies underlying the two languages often do not allow simple one-to-one correspondence, so a mapping must account for these differences. Alternatively, Esperanto language (an international language that was constructed from words in different European languages) was created through merging: All the languages and their underlying ontologies were combined to create a single language. Aligning languages (ontologies) is a third task. Consider how we learn a new domain language that has an extensive vocabulary, such as the language of medicine. The new ontology (the vocabulary of the medical domain) needs to be linked in our minds to the knowledge that we already have (our existing ontology of the world). The creation of these links is alignment. We consider merging and alignment in our work that we describe in this paper. For simplicity, throughout the discussion, we assume that only two ontologies are being merged or aligned at any given time. Figure 1 illustrates the difference between ontology merging and alignment. In merging, a single ontology that is a merged version of the original ontologies is created. Often, the original ontologies cover similar or overlapping domains. For example, the Unified Medical Language System (Humphreys and Lindberg 1993; UMLS 1999) is a large merged ontology that reconciles differences in terminology from various machine-readable biomedical information sources. Another example is the project that was merging the top-most
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paper_title: Rule Induction for Concept Hierarchy Alignment

paper_content:

To manage information like ontology, we usually use categorization with concept hierarchy. Such concept hierarchies are managed individual for each system due to the many differences in concept hierarchies. Consequently, it is difficult to reuse information in computer-based systems. Here, we propose a new concept alignment method for concept hierarchies as a solution to this problem, and construct a system to evaluate the performance of our method. The results of this experiment reveal that the proposed method can be used to induce appropriate align rules for concept hierarchies and classify information into appropriate categories within another concept hierarchy.
---
paper_title: PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment

paper_content:

{PG,1 A dwarf bush rose of the hybrid miniature moss rose class. The plant has many moss-scented stipitate glands on its penduncles, buds, sepals and hips, and blooms almost continuously throughout the growing season. The overall color effect of the flowers is a deep velvety red. There are from about 40 to about 50 relatively broad petals in the flower, with from 7 to 20 petaloids. The plant manifests a better-than-average resistance to mildew, as compared to roses of its class under parallel conditions.
---
paper_title: FCA-MERGE: Bottom-Up Merging of Ontologies

paper_content:

Ontologies have been established for knowledge sharing and are widely used as a means for conceptually structuring domains of interest. With the growing usage of ontologies, the problem of overlapping knowledge in a common domain becomes critical. We propose the new method FCA-MERGE for merging ontologies following a bottom-up approach which offers a structural description of the merging process. The method is guided by application-specific instances of the given source ontologies, that are to be merged. We apply techniques from natural language processing and formal concept analysis to derive a lattice of concepts as a structural result of FCA-MERGE. The generated result is then explored and transformed into the merged ontology with human interaction.
---
paper_title: Learning to Match the Schemas of Data Sources: A Multistrategy Approach

paper_content:

The problem of integrating data from multiple data sources—either on the Internet or within enterprises—has received much attention in the database and AI communities. The focus has been on building data integration systems that provide a uniform query interface to the sources. A key bottleneck in building such systems has been the laborious manual construction of semantic mappings between the query interface and the source schemas. Examples of mappings are “element location maps to address” and “price maps to listed-price”. We propose a multistrategy learning approach to automatically find such mappings. The approach applies multiple learner modules, where each module exploits a different type of information either in the schemas of the sources or in their data, then combines the predictions of the modules using a meta-learner. Learner modules employ a variety of techniques, ranging from Naive Bayes and nearest-neighbor classification to entity recognition and information retrieval. We describe the LSD system, which employs this approach to find semantic mappings. To further improve matching accuracy, LSD exploits domain integrity constraints, user feedback, and nested structures in XML data. We test LSD experimentally on several real-world domains. The experiments validate the utility of multistrategy learning for data integration and show that LSD proposes semantic mappings with a high degree of accuracy.
---
paper_title: Corpus-based schema matching

paper_content:

Schema matching is the problem of identifying corresponding elements in different schemas. Discovering these correspondences or matches is inherently difficult to automate. Past solutions have proposed a principled combination of multiple algorithms. However, these solutions sometimes perform rather poorly due to the lack of sufficient evidence in the schemas being matched. In this paper we show how a corpus of schemas and mappings can be used to augment the evidence about the schemas being matched, so they can be matched better. Such a corpus typically contains multiple schemas that model similar concepts and hence enables us to learn variations in the elements and their properties. We exploit such a corpus in two ways. First, we increase the evidence about each element being matched by including evidence from similar elements in the corpus. Second, we learn statistics about elements and their relationships and use them to infer constraints that we use to prune candidate mappings. We also describe how to use known mappings to learn the importance of domain and generic constraints. We present experimental results that demonstrate corpus-based matching outperforms direct matching (without the benefit of a corpus) in multiple domains.
---
paper_title: Ontology Mapping for Interoperability in Semantic Web

paper_content:

Ontology mapping is the process whereby two ontologies are semantically related at conceptual level and the source ontology instances are transformed into target ontology entities according to those semantic relations. The objective of MAFRA–MApping FRAmework – is to cover all the phases of the ontology mapping process, including analysis, specification, representation, execution and evolution. The MAFRA Toolkit is an implementation of MAFRA, adopting an open architecture in order to observe the Semantic Web requirements, namely performance and transformation capabilities. One of the MAFRA Toolkit novelties respects its service-oriented approach, which claims that the capabilities of an ontology mapping system dependent on what transformations are present. Independent, plug able services are then responsible for the instances transformations, but they also provide support for other ontology mapping tasks like automatic specification of semantic relations, negotiation and evolution.
---
paper_title: PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment

paper_content:

{PG,1 A dwarf bush rose of the hybrid miniature moss rose class. The plant has many moss-scented stipitate glands on its penduncles, buds, sepals and hips, and blooms almost continuously throughout the growing season. The overall color effect of the flowers is a deep velvety red. There are from about 40 to about 50 relatively broad petals in the flower, with from 7 to 20 petaloids. The plant manifests a better-than-average resistance to mildew, as compared to roses of its class under parallel conditions.
---
paper_title: Learning to Match the Schemas of Data Sources: A Multistrategy Approach

paper_content:

The problem of integrating data from multiple data sources—either on the Internet or within enterprises—has received much attention in the database and AI communities. The focus has been on building data integration systems that provide a uniform query interface to the sources. A key bottleneck in building such systems has been the laborious manual construction of semantic mappings between the query interface and the source schemas. Examples of mappings are “element location maps to address” and “price maps to listed-price”. We propose a multistrategy learning approach to automatically find such mappings. The approach applies multiple learner modules, where each module exploits a different type of information either in the schemas of the sources or in their data, then combines the predictions of the modules using a meta-learner. Learner modules employ a variety of techniques, ranging from Naive Bayes and nearest-neighbor classification to entity recognition and information retrieval. We describe the LSD system, which employs this approach to find semantic mappings. To further improve matching accuracy, LSD exploits domain integrity constraints, user feedback, and nested structures in XML data. We test LSD experimentally on several real-world domains. The experiments validate the utility of multistrategy learning for data integration and show that LSD proposes semantic mappings with a high degree of accuracy.
---
paper_title: A framework for ontology integration

paper_content:

One of the basic problems in the development of techniques for the semantic web is the integration of ontologies. Indeed, the web is constituted by a variety of information sources, each expressed over a certain ontology, and in order to extract information from such sources, their semantic integration and reconciliation in terms of a global ontology is required. In this paper, we address the fundamental problem of how to specify the mapping between the global ontology and the local ontologies. We argue that for capturing such mapping in an appropriate way, the notion of query is a crucial one, since it is very likely that a concept in one ontology corresponds to a view (i.e., a query) over the other ontologies. As a result query processing in ontology integration systems is strongly related to view-based query answering in data integration.
---
paper_title: Synthesizing an integrated ontology

paper_content:

The Mediator Environment for Multiple Information Sources (Momis) supports semiautomatic building, annotation, and extension of domain ontologies.
---
paper_title: PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment

paper_content:

{PG,1 A dwarf bush rose of the hybrid miniature moss rose class. The plant has many moss-scented stipitate glands on its penduncles, buds, sepals and hips, and blooms almost continuously throughout the growing season. The overall color effect of the flowers is a deep velvety red. There are from about 40 to about 50 relatively broad petals in the flower, with from 7 to 20 petaloids. The plant manifests a better-than-average resistance to mildew, as compared to roses of its class under parallel conditions.
---
paper_title: Representing and Reasoning about Mappings between Domain Models

paper_content:

Mappings between disparate models are fundamental to any application that requires interoperability between heterogeneous data and applications. Generating mappings is a labor-intensive and error prone task. To build a system that helps users generate mappings, we need an explicit representation of mappings. This representation needs to have well-defined semantics to enable reasoning and comparison between mappings. This paper first presents a powerful framework for defining languages for specifying mappings and their associated semantics. We examine the use of mappings and identify the key inference problems associated with mappings. These properties can be used to determine whether a mapping is adequate in a particular context. Finally, we consider an instance of our framework for a language representing mappings between relational data. We present sound and complete algorithms for the corresponding inference problems.
---
paper_title: A framework for ontology integration

paper_content:

One of the basic problems in the development of techniques for the semantic web is the integration of ontologies. Indeed, the web is constituted by a variety of information sources, each expressed over a certain ontology, and in order to extract information from such sources, their semantic integration and reconciliation in terms of a global ontology is required. In this paper, we address the fundamental problem of how to specify the mapping between the global ontology and the local ontologies. We argue that for capturing such mapping in an appropriate way, the notion of query is a crucial one, since it is very likely that a concept in one ontology corresponds to a view (i.e., a query) over the other ontologies. As a result query processing in ontology integration systems is strongly related to view-based query answering in data integration.
---
paper_title: Ontologies for Enterprise Knowledge Management

paper_content:

Several challenges exist related to applying ontologies in real-world environments. The authors present an integrated enterprise-knowledge management architecture, focusing on how to support multiple ontologies and manage ontology evolution.
---
paper_title: Semantic Coordination: A New Approach and an Application

paper_content:

Semantic coordination, namely the problem of finding an agreement on the meaning of heterogeneous semantic models, is one of the key issues in the development of the Semantic Web. In this paper, we propose a new algorithm for discovering semantic mappings across hierarchical classifications based on a new approach to semantic coordination. This approach shifts the problem of semantic coordination from the problem of computing linguistic or structural similarities (what most other proposed approaches do) to the problem of deducing relations between sets of logical formulae that represent the meaning of concepts belonging to different models. We show how to apply the approach and the algorithm to an interesting family of semantic models, namely hierarchical classifications, and present the results of preliminary tests on two types of hierarchical classifications, web directories and catalogs. Finally, we argue why this is a significant improvement on previous approaches.
---
paper_title: Learning to Match the Schemas of Data Sources: A Multistrategy Approach

paper_content:

The problem of integrating data from multiple data sources—either on the Internet or within enterprises—has received much attention in the database and AI communities. The focus has been on building data integration systems that provide a uniform query interface to the sources. A key bottleneck in building such systems has been the laborious manual construction of semantic mappings between the query interface and the source schemas. Examples of mappings are “element location maps to address” and “price maps to listed-price”. We propose a multistrategy learning approach to automatically find such mappings. The approach applies multiple learner modules, where each module exploits a different type of information either in the schemas of the sources or in their data, then combines the predictions of the modules using a meta-learner. Learner modules employ a variety of techniques, ranging from Naive Bayes and nearest-neighbor classification to entity recognition and information retrieval. We describe the LSD system, which employs this approach to find semantic mappings. To further improve matching accuracy, LSD exploits domain integrity constraints, user feedback, and nested structures in XML data. We test LSD experimentally on several real-world domains. The experiments validate the utility of multistrategy learning for data integration and show that LSD proposes semantic mappings with a high degree of accuracy.
---
paper_title: A framework for ontology integration

paper_content:

One of the basic problems in the development of techniques for the semantic web is the integration of ontologies. Indeed, the web is constituted by a variety of information sources, each expressed over a certain ontology, and in order to extract information from such sources, their semantic integration and reconciliation in terms of a global ontology is required. In this paper, we address the fundamental problem of how to specify the mapping between the global ontology and the local ontologies. We argue that for capturing such mapping in an appropriate way, the notion of query is a crucial one, since it is very likely that a concept in one ontology corresponds to a view (i.e., a query) over the other ontologies. As a result query processing in ontology integration systems is strongly related to view-based query answering in data integration.
---
paper_title: Ontologies : A Silver Bullet for Knowledge Management and Electronic Commerce

paper_content:

This second editionsystematically introduces the notion of ontologies to the non-expert reader and demonstrates in detail how to apply this conceptual framework for improved intranet retrieval of corporate information and knowledge and for enhanced Internet-based electronic commerce. He also describes ontology languages (XML, RDF, and OWL) and ontology tools, and the application of ontologies. In addition to structural improvements, the second edition covers recent developments relating to the Semantic Web, and emerging web-based standard languages.
---
paper_title: Ontologies for Enterprise Knowledge Management

paper_content:

Several challenges exist related to applying ontologies in real-world environments. The authors present an integrated enterprise-knowledge management architecture, focusing on how to support multiple ontologies and manage ontology evolution.
---
paper_title: Synthesizing an integrated ontology

paper_content:

The Mediator Environment for Multiple Information Sources (Momis) supports semiautomatic building, annotation, and extension of domain ontologies.
---
paper_title: PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment

paper_content:

{PG,1 A dwarf bush rose of the hybrid miniature moss rose class. The plant has many moss-scented stipitate glands on its penduncles, buds, sepals and hips, and blooms almost continuously throughout the growing season. The overall color effect of the flowers is a deep velvety red. There are from about 40 to about 50 relatively broad petals in the flower, with from 7 to 20 petaloids. The plant manifests a better-than-average resistance to mildew, as compared to roses of its class under parallel conditions.
---
paper_title: FCA-MERGE: Bottom-Up Merging of Ontologies

paper_content:

Ontologies have been established for knowledge sharing and are widely used as a means for conceptually structuring domains of interest. With the growing usage of ontologies, the problem of overlapping knowledge in a common domain becomes critical. We propose the new method FCA-MERGE for merging ontologies following a bottom-up approach which offers a structural description of the merging process. The method is guided by application-specific instances of the given source ontologies, that are to be merged. We apply techniques from natural language processing and formal concept analysis to derive a lattice of concepts as a structural result of FCA-MERGE. The generated result is then explored and transformed into the merged ontology with human interaction.
---
paper_title: Ontology Evolution: Not the Same as Schema Evolution

paper_content:

As ontology development becomes a more ubiquitous and collaborative process, ontology versioning and evolution becomes an important area of ontology research. The many similarities between database-schema evolution and ontology evolution will allow us to build on the extensive research in schema evolution. However, there are also important differences between database schemas and ontologies. The differences stem from different usage paradigms, the presence of explicit semantics and different knowledge models. A lot of problems that existed only in theory in database research come to the forefront as practical problems in ontology evolution. These differences have important implications for the development of ontology-evolution frameworks: The traditional distinction between versioning and evolution is not applicable to ontologies. There are several dimensions along which compatibility between versions must be considered. The set of change operations for ontologies is different. We must develop automatic techniques for finding similarities and differences between versions.
---
paper_title: Learning to Match the Schemas of Data Sources: A Multistrategy Approach

paper_content:

The problem of integrating data from multiple data sources—either on the Internet or within enterprises—has received much attention in the database and AI communities. The focus has been on building data integration systems that provide a uniform query interface to the sources. A key bottleneck in building such systems has been the laborious manual construction of semantic mappings between the query interface and the source schemas. Examples of mappings are “element location maps to address” and “price maps to listed-price”. We propose a multistrategy learning approach to automatically find such mappings. The approach applies multiple learner modules, where each module exploits a different type of information either in the schemas of the sources or in their data, then combines the predictions of the modules using a meta-learner. Learner modules employ a variety of techniques, ranging from Naive Bayes and nearest-neighbor classification to entity recognition and information retrieval. We describe the LSD system, which employs this approach to find semantic mappings. To further improve matching accuracy, LSD exploits domain integrity constraints, user feedback, and nested structures in XML data. We test LSD experimentally on several real-world domains. The experiments validate the utility of multistrategy learning for data integration and show that LSD proposes semantic mappings with a high degree of accuracy.
---
paper_title: A framework for ontology integration

paper_content:

One of the basic problems in the development of techniques for the semantic web is the integration of ontologies. Indeed, the web is constituted by a variety of information sources, each expressed over a certain ontology, and in order to extract information from such sources, their semantic integration and reconciliation in terms of a global ontology is required. In this paper, we address the fundamental problem of how to specify the mapping between the global ontology and the local ontologies. We argue that for capturing such mapping in an appropriate way, the notion of query is a crucial one, since it is very likely that a concept in one ontology corresponds to a view (i.e., a query) over the other ontologies. As a result query processing in ontology integration systems is strongly related to view-based query answering in data integration.
---
paper_title: Synthesizing an integrated ontology

paper_content:

The Mediator Environment for Multiple Information Sources (Momis) supports semiautomatic building, annotation, and extension of domain ontologies.
---
paper_title: Ontologies for Enterprise Knowledge Management

paper_content:

Several challenges exist related to applying ontologies in real-world environments. The authors present an integrated enterprise-knowledge management architecture, focusing on how to support multiple ontologies and manage ontology evolution.
---
paper_title: C-OWL : Contextualizing Ontologies

paper_content:

Ontologies are shared models of a domain that encode a view which is common to a set of different parties. Contexts are local models that encode a party's subjective view of a domain. In this paper we show how ontologies can be contextualized, thus acquiring certain useful properties that a pure shared approach cannot provide. We say that an ontology is contextualized or, also, that it is a contextual ontology, when its contents are kept local, and therefore not shared with other ontologies, and mapped with the contents of other ontologies via explicit (context) mappings. The result is Context OWL (C-OWL), a language whose syntax and semantics have been obtained by extending the OWL syntax and semantics to allow for the representation of contextual ontologies.
---
paper_title: C-OWL : Contextualizing Ontologies

paper_content:

Ontologies are shared models of a domain that encode a view which is common to a set of different parties. Contexts are local models that encode a party's subjective view of a domain. In this paper we show how ontologies can be contextualized, thus acquiring certain useful properties that a pure shared approach cannot provide. We say that an ontology is contextualized or, also, that it is a contextual ontology, when its contents are kept local, and therefore not shared with other ontologies, and mapped with the contents of other ontologies via explicit (context) mappings. The result is Context OWL (C-OWL), a language whose syntax and semantics have been obtained by extending the OWL syntax and semantics to allow for the representation of contextual ontologies.
---
paper_title: Anytime problem solving using dynamic programming

paper_content:

In previous work, we have advocated explicitly scheduling computation time for planning and problem solving (deliberation) using a framework called expectation-driven iterative refinement. Within this framework, we have explored the problem of allocating deliberation time when the procedures used for deliberation implement anytime algorithms: algorithms that return some answer for any allocation of time. In our search for useful techniques for constructing anytime algorithms, we have discovered that dynamic programming shows considerable promise for the construction of anytime algorithms for a wide variety of problems. In this paper, we show how dynamic programming techniques can be used to construct useful anytime procedures for two problems: multiplying sequences of matrices, and the Travelling Salesman Problem. ::: ::: Dynamic programming can be applied to a wide variety of optimization and control problems, many of them relevant to current AI research (e.g., scheduling, probabilistic reasoning, and controlling machinery). Being able to solve these kinds of problems using anytime procedures increases the range of problems to which expectation-driven iterative refinement can be applied.
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paper_title: OMEN: A Probabilistic Ontology Mapping Tool

paper_content:

Most existing ontology mapping tools are inexact. Inexact ontology mapping rules, if not rectified, result in imprecision in the applications that use them. We describe a framework to probabilistically improve existing ontology mappings using a Bayesian Network. Omen, an Ontology Mapping ENhancer, is based on a set of meta-rules that captures the influence of the ontology structure and the existing matches to match nodes that are neighbours to matched nodes in the two ontologies. We have implemented a protype ontology matcher that can either map concepts across two input ontologies or enhance existing matches between ontology concepts. Preliminary experiments demonstrate that Omen enhances existing ontology mappings in our test cases.
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paper_title: Exploiting interaction contexts in P2P ontology mapping

paper_content:

Agents in peer-to-peer networks may gather into virtual communities, interacting continuously with agents that represent disparate actors, each of them with different interests, needs and views, and having dissimilar ontologies. Mapping all the combination of ontologies in advance is not feasible simply because all the possible combinations cannot be foreseen. Mapping complete ontologies at run time is a computationally expensive task. The framework proposed in this paper maps only the terms encountered in a dialogue, or those needed to map them. The efficiency in the mapping process is increased by accumulating experience and exploiting it in order to reduce the number of mapping candidates to verify, focusing only on the most likely ones.
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paper_title: QOM - Quick Ontology Mapping

paper_content:

(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.
---
paper_title: SMART : Automated Support for Ontology Merging and Alignment

paper_content:

As researchers in the ontology-design field develop the content of a growing number of ontologies, the need for sharing and reusing this body of knowledge becomes increasingly critical. Aligning and merging existing ontologies, which is usually handled manually, often constitutes a large and tedious portion of the sharing process. We have developed SMART, an algorithm that provides a semi-automatic approach to ontology merging and alignment. SMART assists the ontology developer by performing certain tasks automatically and by guiding the developer to other tasks for which his intervention is required. SMART also determines possible inconsistencies in the state of the ontology that may result from the user’s actions, and suggests ways to remedy these inconsistencies. We define the set of basic operations that are performed during merging and alignment of ontologies, and determine the effects that invocation of each of these operations has on the process. SMART is based on an extremely general knowledge model and, therefore, can be applied across various platforms. 1. Merging Versus Alignment In recent years, researchers have developed many ontologies. These different groups of researchers are now beginning to work with one another, so they must bring together these disparate source ontologies. Two approaches are possible: (1) merging the ontologies to create a single coherent ontology, or (2) aligning the ontologies by establishing links between them and allowing the aligned ontologies to reuse information from one another. As an illustration of the possible processes that establish correspondence between different ontologies, we consider the ontologies that natural languages embody. A researcher trying to find common ground between two such languages may perform one of several tasks. He may create a mapping between the two languages to be used in, say, a machine-translation system. Differences in the ontologies underlying the two languages often do not allow simple one-to-one correspondence, so a mapping must account for these differences. Alternatively, Esperanto language (an international language that was constructed from words in different European languages) was created through merging: All the languages and their underlying ontologies were combined to create a single language. Aligning languages (ontologies) is a third task. Consider how we learn a new domain language that has an extensive vocabulary, such as the language of medicine. The new ontology (the vocabulary of the medical domain) needs to be linked in our minds to the knowledge that we already have (our existing ontology of the world). The creation of these links is alignment. We consider merging and alignment in our work that we describe in this paper. For simplicity, throughout the discussion, we assume that only two ontologies are being merged or aligned at any given time. Figure 1 illustrates the difference between ontology merging and alignment. In merging, a single ontology that is a merged version of the original ontologies is created. Often, the original ontologies cover similar or overlapping domains. For example, the Unified Medical Language System (Humphreys and Lindberg 1993; UMLS 1999) is a large merged ontology that reconciles differences in terminology from various machine-readable biomedical information sources. Another example is the project that was merging the top-most
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paper_title: CROSI Mapping System (CMS) - Result of the 2005 Ontology Alignment Contest

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In this results report we summarize our experiences from running the CROSI Mapping System (CMS) over three test cases for this year’s OAEI contest: bibliography, Web directories and medical ontologies alignment case studies. CMS successfully parsed and aligned all input ontologies in all three case studies. We also elaborate on the insights gained and potential research directions towards building more robust alignment systems to cope with the increasing diversity of alignment requirements.
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paper_title: The Chimaera Ontology Environment

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Ontologies have become central components in many applications including search, e-commerce, configuration and, arguably, every large web site (at least for organization and navigation). As ontologies become larger, more distributed, and longer-lived, the need for ontology creation and maintenance environments grows. In our work with ontologies and tool environments over the last few years, we have observed growing needs for automated support of two tasks: (1) merging multiple ontologies and (2) diagnosing (and evolving) ontologies. Chimaera is aimed at supporting these two tasks.
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paper_title: Semantic Coordination: A New Approach and an Application

paper_content:

Semantic coordination, namely the problem of finding an agreement on the meaning of heterogeneous semantic models, is one of the key issues in the development of the Semantic Web. In this paper, we propose a new algorithm for discovering semantic mappings across hierarchical classifications based on a new approach to semantic coordination. This approach shifts the problem of semantic coordination from the problem of computing linguistic or structural similarities (what most other proposed approaches do) to the problem of deducing relations between sets of logical formulae that represent the meaning of concepts belonging to different models. We show how to apply the approach and the algorithm to an interesting family of semantic models, namely hierarchical classifications, and present the results of preliminary tests on two types of hierarchical classifications, web directories and catalogs. Finally, we argue why this is a significant improvement on previous approaches.
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paper_title: Learning to Match the Schemas of Data Sources: A Multistrategy Approach

paper_content:

The problem of integrating data from multiple data sources—either on the Internet or within enterprises—has received much attention in the database and AI communities. The focus has been on building data integration systems that provide a uniform query interface to the sources. A key bottleneck in building such systems has been the laborious manual construction of semantic mappings between the query interface and the source schemas. Examples of mappings are “element location maps to address” and “price maps to listed-price”. We propose a multistrategy learning approach to automatically find such mappings. The approach applies multiple learner modules, where each module exploits a different type of information either in the schemas of the sources or in their data, then combines the predictions of the modules using a meta-learner. Learner modules employ a variety of techniques, ranging from Naive Bayes and nearest-neighbor classification to entity recognition and information retrieval. We describe the LSD system, which employs this approach to find semantic mappings. To further improve matching accuracy, LSD exploits domain integrity constraints, user feedback, and nested structures in XML data. We test LSD experimentally on several real-world domains. The experiments validate the utility of multistrategy learning for data integration and show that LSD proposes semantic mappings with a high degree of accuracy.
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paper_title: A framework for ontology integration

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One of the basic problems in the development of techniques for the semantic web is the integration of ontologies. Indeed, the web is constituted by a variety of information sources, each expressed over a certain ontology, and in order to extract information from such sources, their semantic integration and reconciliation in terms of a global ontology is required. In this paper, we address the fundamental problem of how to specify the mapping between the global ontology and the local ontologies. We argue that for capturing such mapping in an appropriate way, the notion of query is a crucial one, since it is very likely that a concept in one ontology corresponds to a view (i.e., a query) over the other ontologies. As a result query processing in ontology integration systems is strongly related to view-based query answering in data integration.
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paper_title: Ontologies for Enterprise Knowledge Management

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Several challenges exist related to applying ontologies in real-world environments. The authors present an integrated enterprise-knowledge management architecture, focusing on how to support multiple ontologies and manage ontology evolution.
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paper_title: Exploiting interaction contexts in P2P ontology mapping

paper_content:

Agents in peer-to-peer networks may gather into virtual communities, interacting continuously with agents that represent disparate actors, each of them with different interests, needs and views, and having dissimilar ontologies. Mapping all the combination of ontologies in advance is not feasible simply because all the possible combinations cannot be foreseen. Mapping complete ontologies at run time is a computationally expensive task. The framework proposed in this paper maps only the terms encountered in a dialogue, or those needed to map them. The efficiency in the mapping process is increased by accumulating experience and exploiting it in order to reduce the number of mapping candidates to verify, focusing only on the most likely ones.
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paper_title: QOM - Quick Ontology Mapping

paper_content:

(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.
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paper_title: Synthesizing an integrated ontology

paper_content:

The Mediator Environment for Multiple Information Sources (Momis) supports semiautomatic building, annotation, and extension of domain ontologies.
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paper_title: Semantic Coordination: A New Approach and an Application

paper_content:

Semantic coordination, namely the problem of finding an agreement on the meaning of heterogeneous semantic models, is one of the key issues in the development of the Semantic Web. In this paper, we propose a new algorithm for discovering semantic mappings across hierarchical classifications based on a new approach to semantic coordination. This approach shifts the problem of semantic coordination from the problem of computing linguistic or structural similarities (what most other proposed approaches do) to the problem of deducing relations between sets of logical formulae that represent the meaning of concepts belonging to different models. We show how to apply the approach and the algorithm to an interesting family of semantic models, namely hierarchical classifications, and present the results of preliminary tests on two types of hierarchical classifications, web directories and catalogs. Finally, we argue why this is a significant improvement on previous approaches.
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paper_title: C-OWL : Contextualizing Ontologies

paper_content:

Ontologies are shared models of a domain that encode a view which is common to a set of different parties. Contexts are local models that encode a party's subjective view of a domain. In this paper we show how ontologies can be contextualized, thus acquiring certain useful properties that a pure shared approach cannot provide. We say that an ontology is contextualized or, also, that it is a contextual ontology, when its contents are kept local, and therefore not shared with other ontologies, and mapped with the contents of other ontologies via explicit (context) mappings. The result is Context OWL (C-OWL), a language whose syntax and semantics have been obtained by extending the OWL syntax and semantics to allow for the representation of contextual ontologies.
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Title: A Survey on Ontology Mapping

Section 1: Introduction
Description 1: Introduce the necessity for ontology mapping for interoperability, outline the paper's purpose, scope, and organization.

Section 2: Terminology: ontology mapping, ontology integration, merging, and alignment
Description 2: Define the concept of ontology mapping in the context of integration, merging, and alignment and discuss the broad scope of these concepts.

Section 3: Ontology merging, integration, and alignment
Description 3: Discuss the processes of ontology merging, integration, and alignment, and explain how these processes serve as an ontology reuse mechanism.

Section 4: Ontology mapping
Description 4: Explain different types of ontology mapping including between a global ontology and local ontologies, between local ontologies, and in ontology merging and alignment.

Section 5: Categories of Ontology Mapping
Description 5: Examine the three categories of ontology mapping in detail, including their characteristics and application domains.

Section 6: Tools, systems, and related work for Ontology mapping between an integrated global ontology and local ontologies
Description 6: Survey and compare various tools, systems, and frameworks that support ontology mapping between an integrated global ontology and local ontologies.

Section 7: Tools, systems, and related work for Ontology mapping between local ontologies
Description 7: Survey and compare various tools, systems, and frameworks that support ontology mapping between local ontologies.

Section 8: Tools, systems, and related work for Ontology mapping (matching) in ontology merging and alignment
Description 8: Survey and compare various tools, systems, and frameworks that support ontology mapping in the process of merging and aligning ontologies.

Section 9: A Comparison of ontology mapping tools or systems
Description 9: Provide a comparative analysis of various ontology mapping tools and systems based on specific evaluation criteria.

Section 10: Conclusion
Description 10: Summarize the findings, categories, and characteristics of ontology mapping and suggest areas for future research.