A Survey on Ontology Mapping Techniques

Hooi, Yew Kwang; Hassan, Mohd Fadzil; Shariff, Azmi Mohd

doi:10.1007/978-3-642-41674-3_118

Cited by 15 publications

(8 citation statements)

References 7 publications

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“…This technique is based on obtaining the correspondences between the entities with ignoring the structure of the ontology, this technique is divided into string-based, language-based and linguistic resource as in [14].…”

Section: B Ontology Matching Techniquesmentioning

confidence: 99%

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Understanding the Impact of Using Ontology Matching Tools for Validating Feature Models with Domain Knowledge

Mahmoud¹,

Hamza²,

Kamal³

2017

International Conferences on Software Engineering and Knowledge Engineering

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Feature models (FM) are a way for modeling and describing the product family of specific domain. They are widely used for describing the requirements in the domain engineering as it describes the commonalities and the differences of related products in specific domain. Currently the research in the feature model analysis and the validation of it focus on capturing the inconsistencies of the feature configurations of software systems. However the semantic web had been used for representing the feature models as ontology using OWL DL to use the Description Logic (DL) reasoners in validating the consistency of the feature model configurations, detecting the semantic contradictions or semantic mappings with certain domain is missed. The aim of this research is to detect the semantic mappings between a feature model and a specific domain ontology using the ontology marching tools. In the paper we used the Wireless Sensor Actuator Network (WSAN) feature model for analysis, and the well-known Semantic Sensor Network ontology (SSN) for validation. Two ontology-matching tools are used to map the feature model and the domain ontology, and the results have been compared.

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Section: B Ontology Matching Techniquesmentioning

confidence: 99%

“…• Structure-Level technique This technique focuses on obtaining the correspondences based on the relations between the entities within the structure of the ontology, this technique is divided into taxonomy mapping, and tree-based mapping as in [14].…”

Section: B Ontology Matching Techniquesmentioning

confidence: 99%

Understanding the Impact of Using Ontology Matching Tools for Validating Feature Models with Domain Knowledge

Mahmoud¹,

Hamza²,

Kamal³

2017

International Conferences on Software Engineering and Knowledge Engineering

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“…An essential element when performing the mapping is the matcher. The matcher tries to discover correspondences between different ontology entities from narrow perspectives [42]. It builds correspondences between different ontologies by first selecting an appropriate feature (e.g., entity label, structural description of concepts, and range for relations, instantiated attributes or extensional descriptions) from the ontologies [40].…”

Section: Ontology Integrationmentioning

confidence: 99%

“…It builds correspondences between different ontologies by first selecting an appropriate feature (e.g., entity label, structural description of concepts, and range for relations, instantiated attributes or extensional descriptions) from the ontologies [40]. Common mapping approaches link candidate ontologies to a common ontology using anchors [43], [40], [42]. Anchors are entities which are declared to be equivalent.…”

Section: Ontology Integrationmentioning

confidence: 99%

Using Ontologies for Enterprise Architecture Integration and Analysis

Antunes¹,

Bakhshandeh²,

Mayer

et al. 2014

Complex Systems Informatics and Modeling Quarterly

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Enterprise architecture facilitates the alignment between different domains, such as business, applications and information technology. These domains must be described with description languages that best address the concerns of its stakeholders. However, current model-based enterprise architecture techniques are unable to integrate multiple descriptions languages either due to the lack of suitable extension mechanisms or because they lack the means to maintain the coherence, consistency and traceability between the representations of the multiple domains of the enterprise. On the other hand, enterprise architecture models are often designed and used for communication and not for automated analysis of its contents. Model analysis is a valuable tool for assessing the qualities of a model, such as conformance and completeness, and also for supporting decision making. This paper addresses these two issues found in model-based enterprise architecture: (1) the integration of domain description languages, and (2) the automated analysis of models. This proposal uses ontology engineering techniques to specify and integrate the different domains and reasoning and querying as a means to analyse the models. The utility of the proposal is shown through an evaluation scenario that involve the analysis of an enterprise architecture model that spans multiple domains.

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“…Ontology mapping refers to the process of mapping the entity of the source ontology to the target ontology entity through a semantic association between the two ontologies. Ontology mapping enables heterogeneous ontologies to achieve a consistent understanding of the same thing and determine how different ontologies are mapped or related [5]. Ontology mapping solves the problem of complex information exchange in the Semantic Web.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method to Associate Sensor Data with Domain Ontology

Liu¹,

Yang²,

Shang³

2019

IJDSA

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With the development of the Internet of Things, sensor ontologies have been applied to a variety of fields. Most sensor ontologies are currently built for applications in specific domains, and these ontologies are usually heterogeneous, making it difficult to share or reuse knowledge and concepts. The ontology association methods can be used to construct the semantic mapping between heterogeneous ontologies, so as to effectively determine the similarity between concepts in the ontologies. However, most of the contemporary methods do not make full use of the information that is stored in ontologies and are insufficient for the effective association. This paper proposes a novel association method based on comprehensive similarity. In our proposed method, we first use How-Net to obtain concept representation and calculate the semantic similarity of ontology concepts through sememe Tree and sememe Hierarchy. Then we calculate the structural similarity by the internal structure and the hierarchical relationship between the ontologies and remove the conceptual pairs with low relevance. Finally, we combine the semantic similarity and structural similarity to calculate the similarity matrix between ontology concepts to achieve association. The experimental results on real data show that our method can effectively associate sensor data with domain ontology by combining two different similarity calculation methods.

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A Survey on Ontology Mapping Techniques

Cited by 15 publications

References 7 publications

Understanding the Impact of Using Ontology Matching Tools for Validating Feature Models with Domain Knowledge

Understanding the Impact of Using Ontology Matching Tools for Validating Feature Models with Domain Knowledge

Using Ontologies for Enterprise Architecture Integration and Analysis

A Novel Method to Associate Sensor Data with Domain Ontology

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