Ontology Matching: State of the Art and Future Challenges

Shvaiko, Pavel; Euzenat, Jérôme

doi:10.1109/tkde.2011.253

Cited by 873 publications

(508 citation statements)

References 137 publications

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“…Research in the fields of schema and ontology matching (cf. [18,21]) shows a similar tendency to evaluate the performance of matching techniques based on binary values. However, these fields use a broader range of evaluation metrics to suit needs related to specific applications.…”

Section: Related Workmentioning

confidence: 85%

Probabilistic Evaluation of Process Model Matching Techniques

Kuss

Leopold

et al. 2016

Conceptual Modeling

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Abstract. Process model matching refers to the automatic identification of corresponding activities between two process models. It represents the basis for many advanced process model analysis techniques such as the identification of similar process parts or process model search. A central problem is how to evaluate the performance of process model matching techniques. Often, not even humans can agree on a set of correct correspondences. Current evaluation methods, however, require a binary gold standard, which clearly defines which correspondences are correct. The disadvantage of this evaluation method is that it does not take the true complexity of the matching problem into account and does not fairly assess the capabilities of a matching technique. In this paper, we propose a novel evaluation method for process model matching techniques. In particular, we build on the assessment of multiple annotators to define probabilistic notions of precision and recall. We use the dataset and the results of the Process Model Matching Contest 2015 to assess and compare our evaluation method. We find that our probabilistic evaluation method assigns different ranks to the matching techniques from the contest and allows to gain more detailed insights into their performance.

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Section: Related Workmentioning

confidence: 85%

Probabilistic Evaluation of Process Model Matching Techniques

Kuss

Leopold

et al. 2016

Conceptual Modeling

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“…At the same time, it will require corresponding changes (extensions) in front-end logic of a system (service, application, etc.) to perform appropriate reconfiguration of UI and make alignment [7][8] between system ontology (data model) and personal ontology of a user.…”

Section: Ui Adaptation Framework Driven By Semantic Visualizationmentioning

confidence: 99%

Semantic UI: Automated Creation of Semantically Personalized User Interface

Khriyenko

2015

GSTF J Comput

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Abstract-Information representation plays very important role for services and applications to be more attractive for end user. One design cannot fit varied preferences and be suitable for all users. User driven customization of user interface applied for particular application or service do not influence at overall satisfaction of a user and usability of other applications and services. Therefore, in the paper authors tackle challenges of UI personalization on semantic level; and present a framework for adaptive UI development, driven by personal semantic user profile and mashup of reusable adaptive visualization modules. Considering human as a powerful integrated part of IoT environment, UI adaptation and personalization framework has been extended with automated ontology-based UI creation (Semantic Scanner concept). Presented approach of on-the-fly semantically-driven adaptive UI creation facilitates a process of human integration into machine-oriented infrastructure.

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“…Some recent work (e.g. [15,8]) has defined challenges that need to be addressed when dealing with large ontologies. According to [8] interactivity, scalability, and reasoning-based error diagnosis are required to deal with large ontologies.…”

Section: Introductionmentioning

confidence: 99%

“…According to [8] interactivity, scalability, and reasoning-based error diagnosis are required to deal with large ontologies. [15] defines the following challenges related to aligning large ontologies. Regarding scalability [15] discusses efficiency of matching techniques.…”

Section: Introductionmentioning

confidence: 99%

“…[15] defines the following challenges related to aligning large ontologies. Regarding scalability [15] discusses efficiency of matching techniques. This is important as many participants in the Ontology Alignment Evaluation Initiative (OAEI, a yearly event that focuses on evaluating systems that automatically generate mapping suggestions) have perfomance problems when dealing with large ontologies.…”

Section: Introductionmentioning

confidence: 99%

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A Session-Based Approach for Aligning Large Ontologies

Lambrix

Kaliyaperumal

2013

The Semantic Web: Semantics and Big Data

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Abstract. There are a number of challenges that need to be addressed when aligning large ontologies. Previous work has pointed out scalability and efficiency of matching techniques, matching with background knowledge, support for matcher selection, combination and tuning, and user involvement as major requirements. In this paper we address these challenges. Our first contribution is an ontology alignment framework that enables solutions to each of the challenges. This is achieved by introducing different kinds of interruptable sessions. The framework allows partial computations for generating mapping suggestions, partial validations of mappings suggestions and use of validation decisions in (re)computation of mapping suggestions and the recommendation of alignment strategies to use. Further, we describe an implemented system providing solutions to each of the challenges and show through experiments the advantages of the session-based approach.

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Ontology Matching: State of the Art and Future Challenges

Cited by 873 publications

References 137 publications

Probabilistic Evaluation of Process Model Matching Techniques

Probabilistic Evaluation of Process Model Matching Techniques

Semantic UI: Automated Creation of Semantically Personalized User Interface

A Session-Based Approach for Aligning Large Ontologies

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