Demo: A community based approach for managing ontology alignments

2002

The appearance of a large number of ontology tools may leave a user looking for an appropriate tool overwhelmed and uncertain on which tool to choose. Thus evaluation and comparison of these tools is important to help users determine which tool is best suited for their tasks. However, there is no "one size fits all" comparison framework for ontology tools: different classes of tools require very different comparison frameworks. For example, ontology-development tools can easily be compared to one another since they all serve the same task: define concepts, instances, and relations in a domain. Tools for ontology merging, mapping, and alignment however are so different from one another that direct comparison may not be possible. They differ in the type of input they require (e.g., instance data or no instance data), the type of output they produce (e.g., one merged ontology, pairs of related terms, articulation rules), modes of interaction and so on. This diversity makes comparing the performance of mapping tools to one another largely meaningless. We present criteria that partition the set of such tools in smaller groups allowing users to choose the set of tools that best fits their tasks. We discuss what resources we as a community need to develop in order to make performance comparisons within each group of merging and mapping tools useful and effective. These resources will most likely come as results of evaluation experiments of stand-alone tools. As an example of such an experiment, we discuss our experiences and results in evaluating PROMPT, an interactive ontology-merging tool. Our experiment produced some of the resources that we can use in more general evaluation. However, it has also shown that comparing the performance of different tools can be difficult since human experts do not agree on how ontologies should be merged, and we do not yet have a good enough metric for comparing ontologies.

The Knowledge Engineering Review, 2003

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.

International Journal of Human-Computer Studies, 2003

Researchers in the ontology-design field have developed the content for ontologies in many domain areas. This distributed nature of ontology development has led to a large number of ontologies covering overlapping domains. In order for these ontologies to be reused, they first need to be merged or aligned to one another. We developed a suite of tools for managing multiple ontologies. These suite provides users with a uniform framework for comparing, aligning, and merging ontologies, maintaining versions, translating between different formalisms. Two of the tools in the suite support semi-automatic ontology merging: iPrompt is an interactive ontologymerging tool that guides the user through the merging process, presenting him with suggestions for next steps and identifying inconsistencies and potential problems. AnchorPrompt uses a graph structure of ontologies to find correlation between concepts and to provide additional information for iPrompt.

ACM Sigmod Record, 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 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.

2007

Abstract. Recently the Ontology Mapping Problem (OMP) has been identified as a key factor towards the success of the Semantic Web and related applications. This problem arises since it is possible for different people to give, through ontologies, different conceptualizations of the same (or overlapping) knowledge domain. In order to tackle the OMP several algorithms have been designed. They aim at discovering correspondences (aka mappings) between ontology entities.

2008 IEEE/WIC/ACM International Conference …, 2008

The Semantic Web aims to create a web of data where contents can be easily discovered and integrated using metadata. Many ontologies have been proposed over the years in different domains, thus producing a semantic heterogeneity that is difficult to manage. Various automated ontology mapping techniques and tools have been developed to facilitate the bridging and integration of distributed data repositories. Nevertheless, such tools are still in need of human supervision to ensure accuracy. The spread of Web 2.0 approaches demonstrate the possibility and the added value of using collaborative techniques for improving data sharing and consensus reaching. In this paper, we describe our prototype for collaborative ontology mapping and data sharing. The possibility to exploit ontology alignments for querying data is a key capability for data sharing in a networked ontology environment.

2006

In the last period there has been a proliferation of tools that support users in discovering mappings among given ontologies in an automatic or semi-automatic way. However the way such tools are developed is unstandardized: they usually differ in input/output formats, type of user interaction and external resources exploited, hindering the possibility of making them interoperate (for example composing different mapping algorithms). In this paper we propose MDSS (Mapping Discovery Support System) a framework for the integration of mapping tools, aiming at solving interoperability issues among different mapping discovery tools and at supporting users in choosing the best suited mapping discovery algorithm for their needs.

Proceedings of International Conference on Emerging Technologies and Future of Libraries: Issues and Challenges January 30-31, 2015 (Ebook) E-ISBN 978-93-5130-657-3, 2015

Ontology was introduced to achieve interoperability across heterogeneous systems. Now we have a number of ontologies with various granularity and objectives. For example in Biomedical or Bioinformatics domain, more than 120 ontologies are existed. Problem is to aggregate them to a particular place to achieve more flexibility. Ontology ­mapping plays the key role to integrate different distributed and heterogeneous ontologies. We need different mapping tools and systems to re­use the existing ontologies. In this paper, we will talk about fundamental concepts of ontology­mapping, features and effectiveness of various different ontology ­mapping tools. We will concentrate on GLUE, SAM along with its salient features. At the end of this paper we will see that the users are now able to complete the process in around half of the time with the help of SAM than manual mapping technique. This mapping ­technique we can adapt in our semantic digital libraries or social semantic digital libraries to get accurate and relevant information of our queries.

Ontologies as means for conceptualizing and structuring domain knowledge within a community of interest are seen as a key to realize the Semantic Web vision. However, the decentralized nature of the Web makes achieving this consensus across communities difficult, thus, hampering efficient knowledge sharing between them. In order to balance the autonomy of each community with the need for interoperability, mapping mechanisms between distributed ontologies in the Semantic Web are required. In this paper we present MAFRA, an interactive, incremental and dynamic framework for mapping distributed ontologies in the Semantic Web.

Journal on data semantics …, 2008

This paper proposes a cooperative approach for composite ontology mapping. We first present an extended classification of automated ontology matching and propose an automatic composite solution for the matching problem based on cooperation. In our proposal, agents apply individual mapping algorithms and cooperate in order to change their individual results. We assume that the approaches are complementary to each other and their combination produces better results than the individual ones. Next, we compare our model with three state of the art matching systems. The results are promising specially for what concerns precision and recall. Finally, we propose an argumentation formalism as an extension of our initial model. We compare our argumentation model with the matching systems, showing improvements on the results.