Ontology Change Detection Using a Version Log

Plessers, Peter; Troyer, Olga De

doi:10.1007/11574620_42

Cited by 61 publications

(48 citation statements)

References 9 publications

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“…This change ontology is used to display the applied changes to the user. Similarly, change logs are used to manage different ontology versions in [1]. The change logs are realized by a version ontology that represents instances for each class, property and individual of the analyzed ontology.…”

Section: Related Workmentioning

confidence: 99%

“…Quite often, ontology engineers have to compare different versions and analyze or recognize changes. In order to improve and ease the understandability of changes, it is more beneficial for an engineer to view a more abstract and high-level change description instead of a large number of changed axioms (elementary changes) or ontology version logs like in [1]. Combinations of elementary syntactic changes into more intuitive change patterns are described as refactorings [2] or as composite changes [3].…”

Section: Introductionmentioning

confidence: 99%

“…The need to detect high-level changes is already stated in [1,4,5]. High-level understanding of changes provides a foundation for further engineering support like visualization of changes and extended pinpointing focusing on entailments of refactorings rather than individual axiom changes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Semantic Recognition of Ontology Refactoring

Gröner¹,

Parreiras²,

Staab³

2010

Lecture Notes in Computer Science

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Abstract. Ontologies are used for sharing information and are often collaboratively developed. They are adapted for different applications and domains resulting in multiple versions of an ontology that are caused by changes and refactorings. Quite often, ontology versions (or parts of them) are syntactical very different but semantically equivalent. While there is existing work on detecting syntactical and structural changes in ontologies, there is still a need in analyzing and recognizing ontology changes and refactorings by a semantically comparison of ontology versions. In our approach, we start with a classification of model refactorings found in software engineering for identifying such refactorings in OWL ontologies using DL reasoning to recognize these refactorings.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Semantic Recognition of Ontology Refactoring

Gröner¹,

Parreiras²,

Staab³

2010

Lecture Notes in Computer Science

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“…However, in most cases, the changes are recorded as sequences of changes rather than a set of instances in a change ontology [6,11]. While one form of representation can be inferred from the other and vice versa, representation in the form of ontology instances facilitates querying for concept histories and enables attaching annotations to changes.…”

Section: Related Workmentioning

confidence: 99%

A Framework for Ontology Evolution in Collaborative Environments

Noy

Chugh

Liu

et al. 2006

Lecture Notes in Computer Science

153

127

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Abstract. With the wider use of ontologies in the Semantic Web and as part of production systems, multiple scenarios for ontology maintenance and evolution are emerging. For example, successive ontology versions can be posted on the (Semantic) Web, with users discovering the new versions serendipitously; ontology-development in a collaborative environment can be synchronous or asynchronous; managers of projects may exercise quality control, examining changes from previous baseline versions and accepting or rejecting them before a new baseline is published, and so on. In this paper, we present different scenarios for ontology maintenance and evolution that we have encountered in our own projects and in those of our collaborators. We define several features that categorize these scenarios. For each scenario, we discuss the high-level tasks that an editing environment must support. We then present a unified comprehensive set of tools to support different scenarios in a single framework, allowing users to switch between different modes easily. Evolution of Ontology EvolutionAcceptance of ontologies as an integral part of knowledge-intensive applications has been growing steadily. The word ontology became a recognized substrate in fields outside the computer science, from bioinformatics to intelligence analysis. With such acceptance, came the use of ontologies in industrial systems and active publishing of ontologies on the (Semantic) Web. More and more often, developing an ontology is not a project undertaken by a single person or a small group of people in a research laboratory, but rather it is a large project with numerous participants, who are often geographically distributed, where the resulting ontologies are used in production environments with paying customers counting on robustness and reliability of the system.The Protégé ontology-development environment 1 has become a widely used tool for developing ontologies, with more than 50,000 registered users. The Protégé group works closely with some of the tool's users and we have a continuous stream of requests from them on the features that they would like to have supported in terms of managing and developing ontologies collaboratively. The configurations for collaborative development differ significantly however. For instance, Perot Systems 2 uses a client-server mode of Protégé with multiple users simultaneously accessing the same copy of the ontology on the server. The NCI Center for Bioinformatics, which develops the NCI The-1 http://protege.stanford.edu 2

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“…• KE SB ® KE¢ SB triggers the invocation of a change-detection tool 12 that analyzes the SB content and issues a report to the annotator.…”

Section: Figure 2 Semantic Binding Service (Sbs) Functionality the mentioning

confidence: 99%

Requirements and Services for Metadata Management

Missier

Alper

Corcho

et al. 2007

IEEE Internet Comput.

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Knowledge-intensive applications pose new challenges to metadata management, including distribution, access control, uniformity of access, and evolution in time. The authors identify general requirements for metadata management and describe a simple model and service that focuses on Resource Description Framework (RDF) metadata to address these requirements. In important application domains, data and service providers are increasingly making their resources publicly available to the community for re-use in complex workflows. To make those resources useful in practice, however, providers must also provide annotations that describe the data and services' nature and function. This is the case for e-science, the realm of in silico experiments or "procedures that use computer-based information repositories and computational analysis tools to test a hypothesis, derive a summary, search for patterns, or demonstrate a known fact."1 In e-science, it's quite common for providers and consumers to independently add annotations to resources to facilitate their discovery or to record details of their use as part of an experiment. Thus, when scaled to hundreds or thousands of resources and users of those resources, the annotations themselves will form a new and large corpus of heterogeneous metadata distributed over many organizations, with no central control over its maintenance. As a new and complex type of data resource, such metadata requires some form of management to be of any practical use.In this article, we present a middleware service for metadata management that addresses the issue. Its design is based on the observation that, regardless of their differences in format and content, two simple properties are common to all metadata: namely, that it's invariably associated to some underlying resource, and, optionally, separate meta-information for interpreting metadata -an ontology, for instance -might be available. We refer to such meta-information as a knowledge entity to underline the fact that it's used to interpret the metadata. This is A Service-Oriented Approach to Metadata ManagementTo illustrate the need for annotations, let's take a look at the myGrid infrastructure for e-Science (www.mygrid.org.uk). myGrid offers a middleware services suite to facilitate the specification of in silico experiments, mainly in the bioinformatics domain. 2 In particular, biologists can compose and execute scientific workflows like the one in Figure 1 (the workflow is part of a myGrid collection, available at http://workflows.mygrid.org.uk/repository/myGrid) that orchestrate access to multiple resources -public databases and data analysis tools, for example -and use them to derive biologically significant results. We can view myGrid workflows, specified using the Taverna workflow model, 3 as a composition of Web services. Hundreds of Taverna services for biology, for example, are currently available through bioinformatics domain experts' contributions. Service providers can create several types of annotations to facilitate the ...

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Ontology Change Detection Using a Version Log

Cited by 61 publications

References 9 publications

Semantic Recognition of Ontology Refactoring

Semantic Recognition of Ontology Refactoring

A Framework for Ontology Evolution in Collaborative Environments

Requirements and Services for Metadata Management

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