Understanding ontology evolution: A change detection approach

Plessers, Peter; Troyer, Olga De; Casteleyn, Sven

doi:10.1016/j.websem.2006.11.001

Cited by 66 publications

(38 citation statements)

References 3 publications

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“…A number of studies deal with semantic interoperability through the evolution of a common ontology or a global schema based on the cooperation of distributed participants [31,35,37]. However, the assumption of a common ontology is often too strong or unrealistic in cooperative multi-agent systems, as argued by [2].…”

Section: Related Workmentioning

confidence: 99%

On the emergence of semantic agreement among rational agents

Vakili

Papaioannou

Aberer

2015

WEB

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Abstract. Today's complex online applications often require the interaction of multiple (web) services that belong to potentially different business entities. Interoperability is a core element of such an environment, yet not a straightforward one due to the lack of common data semantics. The problem is often approached by means of standardization procedures in a top-down manner with limited adoption in practice. (De facto) standards for semantic interoperability most commonly emerge in a bottom-up approach, i.e., involving the interaction and information exchange among self-interested industrial agents. In this paper, we argue that the emergence of semantic interoperability can be seen as an economic process among rational agents and, although interoperability can be mutually beneficial for the involved parties, it may also be costly and might fail to emerge. As a sample scenario, we consider the emergence of semantic interoperability among rational web service agents in service-oriented architectures (SOAs), and we analyze their individual economic incentives with respect to utility, risk and cost. We model this process as a positive-sum game and study its equilibrium and evolutionary dynamics. According to our analysis, which is also experimentally verified, certain conditions on the communication cost, the cost of technological adaptation, the expected mutual benefit from interoperability, as well as the expected loss from isolation, drive the process.

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Section: Related Workmentioning

confidence: 99%

On the emergence of semantic agreement among rational agents

Vakili

Papaioannou

Aberer

2015

WEB

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“…Various approaches have proposed in literature to address this problem. Most of these address the problem setting of distributed ontology development and thus provide sophisticated mechanisms to compare and thus find the differences between two ontologies [15,18]. On the other hand we assume a more centralized setting in which we assume that the ontology engineers modify the same copy of the ontology definition file.…”

Section: Related Workmentioning

confidence: 99%

Detecting Dirty Queries during Iterative Development of OWL Based Applications

Soma

Prasanna

2008

On the Move to Meaningful Internet Systems: OTM 2008

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Abstract. Incremental/iterative development is often considered to be the best approach to develop large scale information management applications. In an application using an ontology as a central component at design and/or runtime (ontology based system) that is built using this approach, the ontology itself might be constantly modified to satisfy new and changing requirements. Since many other artifacts, e.g., queries, inter-component message formats, code, in the application are dependent on the ontology definition, changes to it necessitate changes to other artifacts and thus might prove to be very expensive. To alleviate this, we address the specific problem of detecting the SPARQL queries that need to be modified due to changes to an OWL ontology (T-Box). Our approach is based on a novel evaluation function for SPARQL queries, which maps a query to the extensions of T-Box elements. This evaluation is used to match the query with the semantics of the changes made to the ontology to determine if the query is dirty-i.e., needs to be modified. We present an implementation of the technique, integrated with a popular ontology development environment and provide an evaluation of our technique on a real-life as well as benchmark applications.

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“…When an entity changes, the change propagates to other entities resulting intermediate changes to other dependent entities [6]. The propagation is towards the dependent entities of the changing entity.…”

Section: Introductionmentioning

confidence: 99%

“…A systematic and careful analysis for identifying dependent entities and analyzing the propagation of impacts to dependent entities is of vital importance in the evolution process [7] [6]. Some key features of our approach are: -providing the theoretical foundation for dependency analysis in ontologydriven content-bases systems.…”

Section: Introductionmentioning

confidence: 99%

Dependency Analysis in Ontology-Driven Content-Based Systems

Abgaz

Javed

Pahl

2012

Artificial Intelligence and Soft Computing

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Abstract. Ontology-driven content-based systems are content-based systems (ODCBS) that are built to provide a better access to information by semantically annotating the content using ontologies. Such systems contain ontology layer, annotation layer and content layer. These layers contain semantically interrelated and interdependent entities. Thus, a change in one layer causes many unseen and undesired changes and impacts that propagate to other entities. Before any change is implemented in the ODCBS, it is crucial to understand the impacts of the change on other ODCBS entities. However, without getting these dependent entities, to which the change propagates, it is difficult to understand and analyze the impacts of the requested changes. In this paper we formally identify and define relevant dependencies, formalizing them and present a dependency analysis algorithm. The output of the dependency analysis serves as an essential input for change impact analysis process that ensures the desired evolution of the ODCBS.

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Understanding ontology evolution: A change detection approach

Cited by 66 publications

References 3 publications

On the emergence of semantic agreement among rational agents

On the emergence of semantic agreement among rational agents

Detecting Dirty Queries during Iterative Development of OWL Based Applications

Dependency Analysis in Ontology-Driven Content-Based Systems

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