Semantic Document Clustering using K-means algorithm and Ward's Method

Salih, Niyaz; Jacksi, Karwan

doi:10.1109/icoase51841.2020.9436588

Cited by 10 publications

(3 citation statements)

References 9 publications

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“…It has been transformed into a platform that manages data based on the evaluation of user data obtained from search engines and inferences made. The basis of the semantic network, inspired by XML , has been a novelty of Web 3.0 in making sense of OWL language data [36][37][38].…”

Section: Web 10mentioning

confidence: 99%

Evolution of the Web: from Web 1.0 to 4.0

Ibrahim

2021

QAJ

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The internet has become a vital component of the twenty-first century as technology has advanced. The number of new technologies emerging in tandem with the qualities supplied by the Internet is rapidly increasing. The World Wide Web (WWW), which is commonly referred to as the world's largest information environment, is a vital virtual environment in which internet users may trade, read, and publish information using a Web browser. Web 1.0, Web 2.0, and Web 3.0 technologies have all been seen and are still being observed in this review paper. However, there is no clear definition for Web 4.0, which is a 4th generation web technology, in the literature. Web 4.0 has multiple dimensions, as seen by the first examples that have appeared. Big data, augmented reality, machine-to-machine communication (M2M), cloud computing, and artificial intelligence (AI) technologies, as well as smart agents, will be able to integrate in the future years. Web 4.0 is a web technology revolution that includes a new internet of things (IoT) that interacts with a variety of models. The goal of this study is to clarify the notion of Web 4.0, which is viewed as an intelligent and symbiotic (human-machine interaction) network with massive interfaces and linkages, as well as to contribute to the literature by studying its many dimensions and investigating its links with new generation technologies.

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Section: Web 10mentioning

confidence: 99%

Evolution of the Web: from Web 1.0 to 4.0

Ibrahim

2021

QAJ

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“…The material on the web should be annotated with descriptions and relationships in the bottom-up or classic approach to the semantic web. However, because there are billions of unstructured HTML pages available without Meta data or annotations, annotating data is an extremely hard process that is still far from being solved [17][18][19].…”

Section: Semantic Webmentioning

confidence: 99%

Approaches to Web Service Composition for the Semantic Web

Ramadhan

2021

QAJ

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Abstract—Service composition is gaining popularity because a composite service can perform functions that an individual service cannot. There are multiple web services available on the web for different tasks. The semantic web is an advanced form of the current web in which all contents have well-defined meanings due to nature, allowing machines to process web contents automatically. A web service composition is a collection of web services that collaborate to achieve a common goal. They reveal the established methods for web service composition in both syntactic and semantic environments. In this study Initially, we identify the existing techniques used for the composition. We classified these approaches according to the processing of the service descriptions, which can be syntactic or semantic-based service processes. We have reviewed more than 14 articles in this domain and concluded the merits of the methodologies applied for the implementation of web service composition.

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“…This issue encompasses the delineation of communities or segments within the network, which can be achieved through different approaches, including topic segmentation or segmentation based on relevant semantics. Presently, various established methods are available, such as .1) clustering-based approaches use algorithms such as K-means, hierarchical clustering, and spectral clustering to cluster nodes in the semantic web based on their semantic features [6]. 2) Community discovery methods identify communities by detecting the underlying structure and assigning nodes with strong connections to the same community using algorithms such as Louvain, GN, and modularity maximization [7].…”

Section: Introductionmentioning

confidence: 99%

POS‐HC: A Part‐of‐Speech Hierarchical Clustering Approach for Normative Texts Partition

Li,

Liu,

Deng

et al. 2024

Preprint

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Chinese texts often feature a substantial volume of normative content, and their analysis has increasingly become a focal point in recent semantic analysis endeavors. Enhancing the effectiveness of text classification and clustering is pivotal in unveiling the intricate semantics within extensive textual data. Empirical research within the realm of computational social science has underscored the need for improved explanatory power when it comes to general text clustering methods applied to normative texts. This study introduces an innovative hierarchical clustering method that incorporates part-of-speech (POS) features and pioneers a novel semantic network model. It achieves this by amalgamating a POS-based feature weight calculation method with hierarchical clustering techniques. To assess its efficacy, the method is rigorously evaluated across three diverse text datasets, encompassing normative reports, news articles, and social media content. The experimental results unequivocally demonstrate the superior performance of the POS-HC method, surpassing the traditional TF and TF-IDF feature extraction methods in terms of clustering accuracy, and surpassing some existing clustering methods. Furthermore, its classification effectiveness exhibits notable advantages, particularly in the semantic classification of normative texts.

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Semantic Document Clustering using K-means algorithm and Ward's Method

Cited by 10 publications

References 9 publications

Evolution of the Web: from Web 1.0 to 4.0

Evolution of the Web: from Web 1.0 to 4.0

Approaches to Web Service Composition for the Semantic Web

POS‐HC: A Part‐of‐Speech Hierarchical Clustering Approach for Normative Texts Partition

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