The Use of Ontology in Retrieval: A Study on Textual, Multilingual, and Multimedia Retrieval

Asim, Muhammad Nabeel; Wasim, Muhammad; Khan, Muhammad Usman Ghani; Mahmood, Nasir; Mahmood, Waqar

doi:10.1109/access.2019.2897849

Cited by 54 publications

(30 citation statements)

References 153 publications

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“…we already introduced Semantic Graph Codes (SGCs), which contain additional semantic structures by an annotation with semantic systems such as RDF, RDFS, ontologies, or knowledge organization systems [35][36][37]. This semantic information can be utilized to bridge the semantic gap between the technical representation of MMIR features and their human-understandable meaning.…”

Section: Graph Codesmentioning

confidence: 99%

Explainable Multimedia Feature Fusion for Medical Applications

Wagenpfeil

Kevitt²,

Cheddad

et al. 2022

J. Imaging

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Due to the exponential growth of medical information in the form of, e.g., text, images, Electrocardiograms (ECGs), X-ray, multimedia, etc., the management of a patient’s data has become a huge challenge. Particularly, the extraction of features from various different formats and their representation in a homogeneous way are areas of particular interest in medical applications. Multimedia Information Retrieval (MMIR) frameworks, like the Generic Multimedia Analysis Framework (GMAF), can contribute to solving this problem, when adapted to special requirements and modalities of medical applications. In this paper, we demonstrate how typical multimedia processing techniques can be extended and adapted to medical applications and how these applications benefit from employing a Multimedia Feature Graph (MMFG) and specialized, highly efficient indexing structures in the form of Graph Codes. These Graph Codes are transformed to feature relevant Graph Codes by employing a modified Term Frequency Inverse Document Frequency (TFIDF) algorithm, which further supports value ranges and Boolean operations required in the medical context. On this basis, various metrics for the calculation of similarity, recommendations, and automated inferencing and reasoning can be applied supporting the field of diagnostics. Finally, the presentation of these new facilities in the form of explainability is introduced and demonstrated. Thus, in this paper, we show how Graph Codes contribute new querying options for diagnosis and how Explainable Graph Codes can help to quickly understand medical multimedia formats.

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Section: Graph Codesmentioning

confidence: 99%

Explainable Multimedia Feature Fusion for Medical Applications

Wagenpfeil

Kevitt²,

Cheddad

et al. 2022

J. Imaging

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“…It consists of one master machine and six slaves' machines. The integration between HBase and Hadoop archives many advantages as follows [11]:…”

Section: Knowledge Source Phasementioning

confidence: 99%

“…The subject term is about the thing we're talking about. The predicate is a term used to describe or modify some aspect of the subject [3] [10] [11].…”

Section: Introductionmentioning

confidence: 99%

Intelligent System for Ranking Big Data in Search Engine

El¹,

EL-Hasnony²

2021

JISIoT

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The spread of Internet sources has increased the volume of big data that is difficult to handle in traditional ways. So, most users need modern search systems to facilitate the search and retrieval of information in the presence of big data. However, the main challenge in the first and second conventional generations of search engines are linking different web data based on the syntax of keywords not on the semantic meaning and without a knowledge base. This manuscript proposes a framework based on modern technologies such as ETI processes, ontology graphs, and indexing RDF using wide column NoSQL technique. The main contribution of our work is introducing a mathematical model that is used to calculate the similarity score between a query and stored RDF documents based on semantic relations. Various operations were carried out to measure the proposed model's efficiency using data sources such as DBpedia, YAGO dataset. According to experimental results, the proposed model is achieving high precision compared to other related systems.

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“…An ontologybased approach used to represent the vocabularies and relationships between semantic entities. Ontology describes the elements that exist in any field or area to represent semantic relation [10]- [14]. The contributions in this paper are divided into four main categories and can be summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

Enhanced Search Engine Using Proposed Framework and Ranking Algorithm Based on Semantic Relations

et al. 2019

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Today, most users need search engines to facilitate search and information retrieval processes. Unfortunately, traditional search engines have a significant challenge that they should retrieve high-precision results for a specific unclear query at a minimum response time. Also, a traditional search engine cannot expand a small, ambiguous query based on the meaning of each keyword and their semantic relationship. Therefore, this paper proposes a comprehensive search engine framework that combines the benefits of both a keyword-based and a semantic ontology-based search engine. The main contributions of this work are developing an algorithm for ranking results based on fuzzy membership value and a mathematical model of exploring a semantic relationship between different keywords. In the conducting experiments, eight different test cases were implemented to evaluate the proposed system. Executed test cases have achieved a precision rate of 97% with appropriate response time compared to the relevant systems.

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The Use of Ontology in Retrieval: A Study on Textual, Multilingual, and Multimedia Retrieval

Cited by 54 publications

References 153 publications

Explainable Multimedia Feature Fusion for Medical Applications

Explainable Multimedia Feature Fusion for Medical Applications

Intelligent System for Ranking Big Data in Search Engine

Enhanced Search Engine Using Proposed Framework and Ranking Algorithm Based on Semantic Relations

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