Semantic Analysis of Wikipedia documents using Ontology

Banik, Prachi; Gaikwad, Sonali; Awate, Anagha; Shaikh, Shahabaj; Gunjgur, Prathmesh; Padiya, Puja

doi:10.1109/icscan.2018.8541162

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…Assigning a score or weight to each individual user query and taking into account the semantic connection that exists between the different queries that are entered by users is the method that Banik et al 2018, [128] developed in order to improve the quality of search results. This method can be found on page 37.…”

Section: Related Workmentioning

confidence: 99%

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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Section: Related Workmentioning

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

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Systematic Review for Selecting Methods of Document Clustering on Semantic Similarity of Online Laboratories Repository

Haji

Jacksi

Salah³

2022

Advances in Intelligent Systems and Computing

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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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Semantic Analysis of Wikipedia documents using Ontology

Cited by 3 publications

References 2 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

Systematic Review for Selecting Methods of Document Clustering on Semantic Similarity of Online Laboratories Repository

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

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