ANEMONE: An environment for modular ontology development

Özacar, Tuğba; Öztürk, Övünç; ínalır, Murat Osman

doi:10.1016/j.datak.2011.02.005

Cited by 27 publications

(10 citation statements)

References 14 publications

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“…To perform reasoning on a subontology with a smaller scale, the notion of a module, i.e., a property-preserving subontology, has been proposed. Module extraction has received much attention in recent years [9]- [11], [25]- [27]. Originally motivated by ontology reuse [28], [29], ontology modularity has been widely used in different areas, such as ontology matching [30] and debugging [31], forgetting [32], [33], or to improve reasoning [34].…”

Section: Introductionmentioning

confidence: 99%

Extracting Satisfiability-Preserving Modules From the OWL RL Ontology for Efficient Reasoning

Zhao

Yang

2021

IEEE Access

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Reasoning on large ontologies has been identified as an important challenge. Concept satisfiability is one of the core reasoning tasks in ontology engineering. To improve the efficiency of concept satisfiability checking for OWL RL ontologies, we propose an approach for extracting modules from the OWL RL ontology. Our approach is based on the idea that for a given concept being checked, not all of the ontology elements are related to the checking process. By transforming the OWL RL axioms into first-order constraints and analyzing the dependencies among the constraints, we give a strategy for deleting the irrelevant elements; thus, a simplified ontology that preserves the original checking result can be obtained. The experimental results obtained for a set of real-world ontologies show that our method can effectively reduce the reasoning time, and the efficiency is increased by 1.15 times to 9.08 times.

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Section: Introductionmentioning

confidence: 99%

Extracting Satisfiability-Preserving Modules From the OWL RL Ontology for Efficient Reasoning

Zhao

Yang

2021

IEEE Access

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“…As such, some useful information or knowledge could be lost from the data set. Moreover, the data missing will also lead to the nonresponse bias of samples which could be a serious concern for the data-driven-based studies [6][7][8][9][10]. In the literatures, most of the existing methods for such problem were mainly based on the statistical techniques.…”

Section: Introductionmentioning

confidence: 99%

An Improved Generalized-Trend-Diffusion-Based Data Imputation for Steel Industry

Liu

Wang

2013

Mathematical Problems in Engineering

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Integrality and validity of industrial data are the fundamental factors in the domain of data-driven modeling. Aiming at the data missing problem of gas flow in steel industry, an improved Generalized-Trend-Diffusion (iGTD) algorithm is proposed in this study, where in particular it considers the sort of problem with data properties of consecutively missing and small samples. And, the imputation accuracy can be greatly increased by the proposed Gaussian membership-based GTD which expands the useful knowledge of data samples. In addition, the imputation order is further discussed to enhance the sequential forecasting accuracy of gas flow. To verify the effectiveness of the proposed method, a series of experiments that consists of three categories of data features in the gas system is presented, and the results indicate that this method is comprehensively better for the imputation of the periodical-like data and the time-series-like data.

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“…Although existing approaches can be divided into several categories [7]- [9], here we chose to focus on module extraction and ontology partitioning techniques.…”

Section: Related Workmentioning

confidence: 99%

Partitioning of ontologies driven by a structure-based approach

Amato

Santo

Moscato

et al. 2015

Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)

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Abstract-In this paper, we propose a novel structure-based partitioning algorithm able to break a large ontology into different modules related to specific topics for the domain of interest. In particular, we leverage the topological properties of the ontology graph and exploit several techniques derived from Network Analysis to produce an efl'ective partitioning without considering any information about semantics of ontology relationships. An automated partitioning tool has been developed and several preliminary experiments have been conducted to validate the efl'ectiveness of our approach with respect to other techniques.

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ANEMONE: An environment for modular ontology development

Cited by 27 publications

References 14 publications

Extracting Satisfiability-Preserving Modules From the OWL RL Ontology for Efficient Reasoning

Extracting Satisfiability-Preserving Modules From the OWL RL Ontology for Efficient Reasoning

An Improved Generalized-Trend-Diffusion-Based Data Imputation for Steel Industry

Partitioning of ontologies driven by a structure-based approach

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