A novel approach for learning ontology from relational database: from the construction to the evaluation

Mahria, Bilal Ben; Chaker, Ilham; Zahi, Azeddine

doi:10.1186/s40537-021-00412-2

Cited by 19 publications

(11 citation statements)

References 38 publications

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“…A novel approach proposed by Bilal Ben Mahria, et al [ 15 ], introduces a new life cycle for ontology learning from relational databases based on software engineering requirements.…”

Section: Related Workmentioning

confidence: 99%

Semantic Integration of Heterogeneous Data Sources Using Ontology-Based Domain Knowledge Modeling for Early Detection of COVID-19

Thirumahal¹,

Sadasivam²,

Shruti³

2022

SN COMPUT. SCI.

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The enormous outbreak of biomedical knowledge, the aim of reducing computation and processing costs and the widespread availability of internet connection have created a profuse amount of electronic data. Such data are stored across the globe in various data sources that are semantically, structurally and syntactically different. This decentralized nature of biomedical data has made it difficult to obtain a unified view of the data. Data integration plays a crucial role in enhancing access to heterogeneous data making the retrieval easier and faster. A variety of ontology, machine learning, deep learning and fuzzy logic-based solutions are being developed for heterogeneous data integration. The proposed model concentrates on the automatic ontology-based data integration method that can be effectively deployed and used in the healthcare domain. The proposed model is divided into three phases. The first phase includes the automatic mapping of data and generation of local ontology across heterogeneous data sources, the second phase combines the local ontology models developed in the first phase to create a root global schema mapping and the third phase queries diverse databases to retrieve semantically analogous records. The model is created based on the medical records, chest X-ray details and COVID-19 symptom questionnaire data of various patients distributed across three data sources (SQL, mongodb and excel). Based on the data, the patients who have moderate/higher risk of developing serious illness from COVID-19 are retrieved.

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“…A novel approach proposed by Bilal Ben Mahria, et al [ 15 ], introduces a new life cycle for ontology learning from relational databases based on software engineering requirements.…”

Section: Related Workmentioning

confidence: 99%

Semantic Integration of Heterogeneous Data Sources Using Ontology-Based Domain Knowledge Modeling for Early Detection of COVID-19

Thirumahal¹,

Sadasivam²,

Shruti³

2022

SN COMPUT. SCI.

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“…Ontological frameworks have numerous advantages, such that it makes domain assumptions on data explicit and/or to share a common understanding of structural information across metrics. [84]. The ontologies can either be learned such as with the Mahria et.…”

Section: Ontologymentioning

confidence: 99%

“…The ontologies can either be learned such as with the Mahria et. al paper titled A novel approach for learning ontology from relational database: from the construction to the evaluation or formulaized by domain experts [84]. There are numerous ontological databases available for describing relationships between known domains.…”

Section: Ontologymentioning

confidence: 99%

A Novel Approach to Topological Graph Theory with R-K Diagrams and Gravitational Wave Analysis

Roy¹,

Kesselman²

2022

Preprint

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Graph Theory and Topological Data Analytics (TDA), while powerful, have many drawbacks related to their sensitivity and consistency with TDA & Graph Network Analytics. In this paper, we aim to propose a novel approach for encoding vectorized associations between data points for the purpose of enabling smooth transitions between Graph and Topological Data Analytics. [66] [26] [41] We conclusively reveal effective ways of converting such vectorized associations to simplicial complexes representing micro-states [72] in a Phase-Space, resulting in filter specific, Homotopic Self-Expressive,event-driven unique topological signatures which we have referred as "Roy-Kesselman (R-K) Diagrams" with Persistent Homology, which emerge from filter-based encodings of "(R-K) Models". We finally aim to provide an efficient computational framework via the "(R-K) Pipeline" which can be used via the R-K Toolkit for filter based ML Driven models for unique topological signature identification and classification problems. This pipeline would be responsible for shape invariant encoding in strong correspondence with topological network divergence as global-macrostate properties of high-dimensional datasets.[60] The validity and impact of this approach were tested specifically on high-dimensional (parameter specific) raw and derived measures from the latest LIGO datasets published by the LIGO Open Science Centre [56] [102] along with testing a generalized approach for a non-scientific use-case, which has been demonstrated using the Tableau Superstore Sales dataset.[79] The results obtained verify the basis of this novel approach in the following ways: (1) Distinct topological structures were created from the data and we have done so both on a store sales data and LIGO data. (2) These topological structures have emergent properties that when evaluated and compared to, have the capacity to provide meaningful insights into the data that standard data analysis techniques would not identify. For example, topological differences were exposed in the analysis that could not be exposed over metric based analysis such as euclidean distance, 'Mahalanobis Distance', and other standard metric based distance measures. (3) The resulting structures provide an extensible representation, which can be applied different methods of analysis, such as classification, identification, segmentation, etc. In the store sales example, results were trained over a non-gradient, combinatorial approach. With respect to store sales data, unique topological structures were derived between distinct purchase events with a loss ( measured in similarity between a set of R-K Diagrams ) in the range of [0.78, 0.88]. In the case of the classification case study with LIGO data analysis, we recorded a high accuracy of compact binary classifications, and a average similarity measure of BH-BH Mergers falling between the range of [0.9, 0.96] and NS-NS Mergers between a range [0.85, 0.91]. Inter-class similarity measures between black holes, neutron stars & candidate PBH measures we...

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“…(2) Acquiring and representing domain knowledge, constructing domain ontology, domain ontology can be adopted to carry out semantic annotation for data resources, realize semantic understanding of data resources, and form ontology knowledge base; (3) Mining users' demand for knowledge services in response to public health emergencies; (4) Accessing, mapping, matching, reasoning and operation of ontology knowledge base are carried out to provide intelligent knowledge services for users in various forms. Ontology is the formal specification of Shared conceptual model of clear and conceptual Clarity formal sharing characteristics, and its basic composition elements mainly includes the concept relations and instance [10][11]. The purpose of semantic annotation process is to add corresponding semantic labels and semantic relations to multidimensional data, so as to have a deep understanding of the semantics of data [12].…”

Section: The Design Of S-dimensionmentioning

confidence: 99%

An Intelligent Service Model of Medical Library for Public Health Emergencies

Tan

2022

Computer Methods in Medicine and Health Care

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In the face of public health emergencies, medical libraries provide various types of intelligent knowledge services for relevant users based on the available resources and conditions, and take active actions, which is of great significance for medical libraries to fulfill their original mission and responsibilities with practical actions, expand their business scope and innovate service forms. The intelligent service of medical library for public health emergencies is a typical complex system engineering, which requires the cooperation of staff with different professional knowledge and skills. According to a series of systematic processes, intelligent service uses various types of hardware and software resources to realize intelligent knowledge service and decision support. Based on the WSR system methodology in the field of systems engineering, this paper constructs a medical library’s intelligent service model for public health emergencies. The model is a three-dimensional space structure composed of the physical (Wu in Chinese) dimension (W), the common sense (Shi in Chinese) dimension (S), and the human (Ren in Chinese) dimension (R). The article analyzes and discusses the functions and implementation paths of each dimension in detail.

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A novel approach for learning ontology from relational database: from the construction to the evaluation

Cited by 19 publications

References 38 publications

Semantic Integration of Heterogeneous Data Sources Using Ontology-Based Domain Knowledge Modeling for Early Detection of COVID-19

Semantic Integration of Heterogeneous Data Sources Using Ontology-Based Domain Knowledge Modeling for Early Detection of COVID-19

A Novel Approach to Topological Graph Theory with R-K Diagrams and Gravitational Wave Analysis

An Intelligent Service Model of Medical Library for Public Health Emergencies

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