Achieving Higher Ranking to Webpages Through Search Engine Optimization

Swapna, B.; Anuradha, T.

doi:10.1007/978-981-10-6319-0_9

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…System/service design should aim for user optimization by monitoring the interactions (behavioral patterns) with users and systems. It should also consider SEO to achieve higher ranking to Web pages and improve the ranking by detecting and understanding users’ behavioral intention to search information on the Web (Gaou and Bekkari, 2018; Swapna and Anuradha, 2018).…”

Section: Findings and Discussionmentioning

confidence: 99%

SEO for an open access scholarly information system to improve user experience

Park

2018

IDD

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Purpose This paper aims to introduce a case of search engine optimization (SEO), especially designed for a national scholarly open access information website in the field of STEM. Design/methodology/approach Korea Institute of Science and Technology Information (KISTI) collaborated with the Google Scholar team to open and share the research outcomes of STEM in Korea worldwide. KoreaScience is a reference-linking platform for open access scientific and technical journals in Korea, operated by KISTI. KISTI worked with the Google Scholar team to embed machine-readable bibliographic metadata into its journal pages and to create an XML Sitemap to help Google find pages on KoreaScience. Findings As a result of implementation of metadata and creation of an XML Sitemap, the KoreaScience Web pages have noticeably increased the relevance of a search results’ list on Google and Google Scholar. In addition to this, the KoreaScience platform has received an increasing amount of its traffic from around the world. Originality/value Not much research has sought to understand SEO in the aspect of users and how it may be facilitated in “visible” academic Web environments such as search systems and open access information systems. For this project, the motivation for investigating SEO comes from its association with positive outcomes that range from personal benefits to global rewards, e.g. increased satisfaction in search user experience and, further, academic progress and scientific development by sharing and accessing scientific knowledge in the fast-growing field of STEM.

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Section: Findings and Discussionmentioning

confidence: 99%

SEO for an open access scholarly information system to improve user experience

Park

2018

IDD

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“…Therefore, we handled this issue by employing topic modeling. We applied LDA [59], hierarchical Dirichlet process (HDP) [60], and latent semantic indexing (LSI) [61] to see the effect of the topic modeling on the results of the summarization process. LDA improved the results the most, so we have used it in our model.…”

Section: Representativenessmentioning

confidence: 99%

Grapharizer: A Graph-Based Technique for Extractive Multi-Document Summarization

et al. 2023

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In the age of big data, there is increasing growth of data on the Internet. It becomes frustrating for users to locate the desired data. Therefore, text summarization emerges as a solution to this problem. It summarizes and presents the users with the gist of the provided documents. However, summarizer systems face challenges, such as poor grammaticality, missing important information, and redundancy, particularly in multi-document summarization. This study involves the development of a graph-based extractive generic MDS technique, named Grapharizer (GRAPH-based summARIZER), focusing on resolving these challenges. Grapharizer addresses the grammaticality problems of the summary using lemmatization during pre-processing. Furthermore, synonym mapping, multi-word expression mapping, and anaphora and cataphora resolution, contribute positively to improving the grammaticality of the generated summary. Challenges, such as redundancy and proper coverage of all topics, are dealt with to achieve informativity and representativeness. Grapharizer is a novel approach which can also be used in combination with different machine learning models. The system was tested on DUC 2004 and Recent News Article datasets against various state-of-the-art techniques. Use of Grapharizer with machine learning increased accuracy by up to 23.05% compared with different baseline techniques on ROUGE scores. Expert evaluation of the proposed system indicated the accuracy to be more than 55%.

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