Clustering Document based on Semantic Similarity Using Graph Base Spectral Algorithm

Ibrahim, Rowaida Khalil; Zeebaree, Subhi R. M.; Jacksi, Karwan; Ahmed, Sarkar Hasan; Mohammed, Shapol M.; Zebari, Rizgar R.; Alkhayyat, Ahmed; Rashid, Zryan Najat

doi:10.1109/iiceta54559.2022.9888613

Cited by 5 publications

(5 citation statements)

References 11 publications

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“…Many researchers have used WordNet as an information resource since their work on semantic similarity calculation between words. More recently, some have used a web search engine [41].…”

Section: B Information Resourcesmentioning

confidence: 99%

“…In the process of identifying the degree to which two distinct concepts are semantically connected to one another, WordNet is only one of many standard ways that are utilized. Other methods that are often used include: The WordNet database only includes English-language dictionaries since that is the language in which it was built [39][40][41]. The dictionaries in question are not offered in any other language editions.…”

Section: Introductionmentioning

confidence: 99%

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Document Clustering in the Age of Big Data: Incorporating Semantic Information for Improved Results

Haji¹,

Al-Zebari²,

Şengür³

et al. 2023

JASTT

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There has been a meteoric rise in the total amount of digital texts as a direct result of the proliferation of internet access. As a direct result of this, document clustering has evolved into a crucial method that must be used in order to successfully extract relevant information from big document collections. When employing the document clustering approach, documents are automatically sorted into groups whose members have a high degree of similarity to one another. These groups are created by applying the document clustering technique. Because they do not take into account the semantic linkages that exist between the texts, traditional clustering approaches are unable to provide an acceptable description of a collection of texts. This is because traditional clustering techniques. Document clusters, in which texts are ordered according to their meaning rather than their use of keywords, have been extensively utilized as a means of overcoming these challenges as a result of the incorporation of semantic information. This has been possible as a result of the fact that document clusters can group together related texts. In this investigation, we looked at a total of 27 distinct papers that were published over the previous five years and categorized the documents based on the semantic similarities that existed between the various pieces. A detailed literature evaluation is included to each and every one of the publications that were selected for further consideration. Comparative research is carried out on a wide variety of evaluation strategies, including as algorithms, similarity metrics, instruments, and processes. Following that, there is a drawn-out discussion that analyzes the similarities and differences between the activities.

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“…Many researchers have used WordNet as an information resource since their work on semantic similarity calculation between words. More recently, some have used a web search engine [41].…”

Section: B Information Resourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Document Clustering in the Age of Big Data: Incorporating Semantic Information for Improved Results

Haji¹,

Al-Zebari²,

Şengür³

et al. 2023

JASTT

View full text Add to dashboard Cite

show abstract

“…Contemporary database design offers a wide variety of functionality in terms of data access and manipulation. So that a large amount of data can be stored, these databases use the key-value pair concept [17]. Integration of data is the most critical challenge of any database project.…”

Section: Challenges Of Integrating Big Datamentioning

confidence: 99%

Orchestrating Distributed Computing and Web Technology with Semantic Web and Big Data

Amanoul,

Abdulrahman,

Abdullah

et al. 2023

Journal of Smart Internet of Things

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Complex data systems are incapable of processing large data volumes, rendering the task of retrieving pertinent information unattainable. The advent of the Internet has amplified the significance of accessible and readily available information. Additionally, it receives support from the World Wide Web Consortium (W3C) and global organizations responsible for establishing web standards, such as Web Ontology, Inc. It expands the functionality of the website to facilitate the retrieval, integration, and transmission of information. In recent years, several major organizations have shown a strong inclination towards using semantic technologies for the purpose of collecting Big Data. Undoubtedly, there are other advantages of integrating this into the Creative. It enhances the ability of end-users to manage data from many repositories, focuses on changing the corporate environment and the user experience, and incorporates individual definitions and integrates several data sources. Furthermore, the market’s evolving expectations and contemporary organizational practices require an adaptable but all-encompassing information strategy. Integration of data warehousing may be achieved by the use of scattered corporate ontologies. This study explores the impact of the Semantic Web on enhancing the intelligence of Big Data. It analyses the obstacles and opportunities associated with the integration of Big Data with the Semantic Web.

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“…The contribution that has been made has been of great assistance in advancing the state of human civilization. One of the terms that is used to describe this concept is the "Web of Things" (WoT) [6]. In order to make it easier for people to share information with one another, an organisation known as the World of Things (WoT) connects various physical devices to a computer network.…”

Section: Web Of Thing (Wot)mentioning

confidence: 99%

Modular Platforms based on Clouded Web Technology and Distributed Deep Learning Systems

Abdullah,

Abdulrahman,

Abdulkareem

et al. 2023

Journal of Smart Internet of Things

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Utilising the dispersed resources that are accessible inside a cluster, the dispersed Deep Learning System (DDLS) is able to successfully complete the process of training complex neural network models. This is accomplished by utilising the resources to their full potential. As a consequence of this, the system is able to get insights about neural network models that are becoming more complex. Individuals who work as engineers for distributed deep learning systems are needed to make a variety of decisions in order to properly manage their specialised job within the environment of their choice. This is necessary in order to ensure that the job is efficiently managed. Achieving this is very necessary in order to guarantee that they will be able to carry out their obligations. Throughout the course of the last several years, deep learning programmes have uncovered significant applications in a broad range of different industries. A few examples of these domains include image recognition, natural language processing, semantic understanding, financial analysis, and aided healthcare. These are only few of the topics that fall under this category. All of these factors have led to a significant growth in the amount of information that is being used in each and every application. The continued penetration of information into a number of different sectors, as well as the increasing complexity of computations and the restrictions of models, have all contributed to this. Because of this, there has been a significant increase in the quantity of information that is being employed on a worldwide scale for various purposes.

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Clustering Document based on Semantic Similarity Using Graph Base Spectral Algorithm

Cited by 5 publications

References 11 publications

Document Clustering in the Age of Big Data: Incorporating Semantic Information for Improved Results

Document Clustering in the Age of Big Data: Incorporating Semantic Information for Improved Results

Orchestrating Distributed Computing and Web Technology with Semantic Web and Big Data

Modular Platforms based on Clouded Web Technology and Distributed Deep Learning Systems

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