A Realist Framework for Ontologies and the Semantic Web

Mishra, Sanju; Malik, Sonika; Jain, N. K.; Jain, Sarika

doi:10.1016/j.procs.2015.10.087

Cited by 16 publications

(6 citation statements)

References 12 publications

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“…On the other hand, we would like to formalize our approach using FCA (Formal Concept Analysis). Moreover, we propose to create a novel ontology by integrating our ontology (sowl) with existing vocabularies using the algorithms and rules provided in (Mishra & Jain, 2016;. To verify and validate the novel ontology, we propose also to use "QueryOnto" tool (Mishra & Jain, 2018).…”

Section: Discussionmentioning

confidence: 99%

Reusing Alignments for Discovering Instances Correspondences

Ghemmaz

Benchikha

Bouzid

2021

International Journal of Web-Based Learning and Teaching Technologies

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Recently, instance matching has become a key technology to achieve interoperability over datasets, especially in linked data. Due the rapid growth of published datasets, it attracts increasingly more research interest. In this context, several approaches have been proposed. However, they do not perform well since the problem of matching instances that possess different descriptions is not addressed. On the other hand, the usage of the identity link owl:sameAs is generally predominant in linking correspondences. Unfortunately, many existing identity links are misused. In this paper, the authors discuss these issues and propose an original instance matching approach aiming to match instances that hold diverse descriptions. Furthermore, a novel link named ViewSameAs is proposed. The key improvement compared to existing approaches is alignment reuse. Thus, two novel methods are introduced: ViewSameAs-based clustering and alignment reuse based on metadata. Experiments on datasets by considering those of OAEI show that the proposed approach achieves satisfying and highly accuracy results.

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Section: Discussionmentioning

confidence: 99%

Reusing Alignments for Discovering Instances Correspondences

Ghemmaz

Benchikha

Bouzid

2021

International Journal of Web-Based Learning and Teaching Technologies

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“…This feature presents several challenges on how to obtain the information, use the information and exchange data in understanding it and being able to process it. Interoperabilities [25] are classified into four categories:…”

Section: Interoperability Issues In Swotmentioning

confidence: 99%

“…For obtaining full interoperability, the semantics of information have to be the common for all systems. Ontology presents format as an explicit specification of a conceptualization [25]. Ontology can be managed by the devices and expresses the definitions of framed concepts and restrictions on possible interpretations among these terms to build a structure on the domain and how these concepts are connected to each other.…”

Section: Knowledge Basementioning

confidence: 99%

Secure Semantic Smart HealthCare (S3HC)

Tiwari

Jain

Abraham

et al. 2019

JWE

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Healthcare is a significant domain having a huge knowledge base, a significant part which comes from medical, diagnostic and imaging devices and sensors. The health status of patients may be monitored and managed remotely by performing reasoning over this knowledge base. Specialists in HealthCare facilities are required to handle large quantity of data generated and make decisions. However, the heterogeneous and complex nature and the huge amount of data generated; the way it is represented and presented; and the security challenges may overburden the core abilities of thinking and reasoning of even highly skilled and knowledgeable experts putting the lives of patients at risk. The situation may become even worse when data is coming from various healthcare devices and sensors which are themselves characterized by a number of representation and serialization formats. To address the various challenges in healthcare, this paper tries to represent and hence exchange the data collected by healthcare devices meaningfully and securely. This allows all healthcare devices to operate in conjunction with each other facilitating deeper insights and enabling generation of intelligent recommendations.To transfer the collected data from devices to the knowledge base and vice versa, a healthcare IoT ontology with sensors and actuators is developed. SPARQL queries and SWRL rules are composed to provide personalized services and alleviating the doctors' workload.

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“…This advisory system is supposed to generate recommendations using the mixed approach of rule based reasoning and case based reasoning to provide an automatic and effective response for a fast reaction to any emergency situation. The aim is to minimize the loss of life and property, thus the ontology serves as a knowledge base that stores information, recommendations and expert advice about emergency situations and the decisions taken for an effective and immediate emergency response [5], [20].…”

Section: Evaluation Of Emergency Situation Ontologymentioning

confidence: 99%

“…For providing prompt decisions during disaster management, the city personnel/planners need to focus upon the effective and efficient storage, analysis and processing of emergency information, safety plans and resources to generate the recommendations along with proper justification [3]. In recent years, ontology tools have been widely used for representation and reasoning in situations of emergency [4], [5]. It builds the knowledge base for an intelligent decision support system which enables an automatic and effective response to all kinds of disasters.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Refinement of Emergency Situation Ontology

Jain¹,

Meyer²

2018

IJIET

Self Cite

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Ontologies provide a machine-processable description of entities and their properties, relationships and constraints, thus they can depict the semantics of disaster situations and related emergency tasks and thereby help to create connections between them for an efficient emergency response. As an ontology acts as the basic structure and knowledge base of an application, evaluation and assessment of the ontology are a critical point of the development process. Through the evaluation, the quality and the content of the ontology is assessed and it ensures that the ontology is well built, structured and contains all important concepts and relationships between them for sufficient reasoning. In this paper, an evaluation framework is proposed to evaluate an emergency situation ontology for which existing evaluation methods have been combined into a single framework, dividing the methods used into two phases: verification and validation. The verification of the ontology ensures that the ontology is correctly built. It evaluates the structure, functionality and representation of the ontology. Different metrics and common pitfalls are used to detect errors. The validation of the ontology ensures that the right ontology for the given application is built. This is achieved by competency questions and expert interviews.

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A Realist Framework for Ontologies and the Semantic Web

Cited by 16 publications

References 12 publications

Reusing Alignments for Discovering Instances Correspondences

Reusing Alignments for Discovering Instances Correspondences

Secure Semantic Smart HealthCare (S3HC)

Evaluation and Refinement of Emergency Situation Ontology

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