FoDoSu: Multi-document summarization exploiting semantic analysis based on social Folksonomy

Heu, J.; Qasim, Iqbal; Lee, Dong‐Ho

doi:10.1016/j.ipm.2014.06.003

Cited by 31 publications

(16 citation statements)

References 15 publications

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“…Based on the study aims and questions raised earlier, the authors employed the main source of qualitative data through interviews. Interview data are one of the important sources of data (Heu et al, 2015;Yang et al, 2015;Silverman, 1999). Silverman (1999) argued that words are important simply as a jumping-off point for the real analysis; he suggested that where texts are analysed, they usually are presented as 'official' or 'common sense' versions of social phenomena.…”

Section: The Study Approachmentioning

confidence: 99%

The employability skills of business graduates in Syria

Ayoubi

Alzarif²,

Khalifa

2017

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Purpose The purpose of this paper is to compare the desired employability skills of business graduates in Syria from the perspective of both higher education policymakers and employers in the private sector. Design/methodology/approach Interviews were conducted with 12 higher education policymakers and managers from the business sector. Content analysis was utilized to analyse the content of the interviews and the strategic priorities of the higher education sector in Syria. Findings Results revealed that although higher education policymakers focus more on societal, public and thinking skills for business graduates, the business sector focusses more on individual, private and practical skills. Accordingly, a comparative tool that aligns the two perspectives was developed in the study. The tool, based on the contradicting employability skills, identified four types of business graduates: leader, collective, technical and trainee. Research limitations/implications The study is limited by data collected before the current political instability in Syria in 2012. The data were collected only from official documents and interviews with policymakers and employers. Students were not part of the study. Practical implications The managerial tool developed at the end of the study will help both policymakers and the private sector to statistically allocate business graduates for better planning. The study provides recommendations to the different stakeholders in the higher education sector in Syria. Originality/value Although the majority of the previous literature raises the voices of the business sector, this study is one of the first studies that aligns the discrepant perspectives of the higher education and business sectors. The managerial tool developed in the study is original and usable by policymakers and the business sector, and it is subject to further development.

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Section: The Study Approachmentioning

confidence: 99%

The employability skills of business graduates in Syria

Ayoubi

Alzarif²,

Khalifa

2017

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“…In [36] the author describes an application in which word clouds are used to browse and synthesize Twitter results with the aim of showing a first opinion of the contents of the tweets. In [31] the authors propose a new multi-document summarization system called FoDoSu (Folksonomy-based Multi-Document Summarization) based on social folksonomies. The word frequency table is created for semantic analysis and the relevance of words is measured using HITS algorithms.…”

Section: Related Workmentioning

confidence: 99%

Using a multimedia semantic graph for web document visualization and summarization

Rinaldi

Russo

2020

Multimed Tools Appl

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The synthesis process of document content and its visualization play a basic role in the context of knowledge representation and retrieval. Existing methods for tag-clouds generations are mostly based on text content of documents, others also consider statistical or semantic information to enrich the document summary, while precious information deriving from multimedia content is often neglected. In this paper we present a document summarization and visualization technique based on both statistical and semantic analysis of textual and visual contents. The result of our framework is a Visual Semantic Tag Cloud based on the highlighting of relevant terms in a document using some features (font size, color, etc.) showing the importance of a term compared to other ones. The semantic information is derived from a knowledge base where concepts are represented through several multimedia items. The Visual Semantic Tag Cloud can be used not only to synthesize a document but also to represent a set of documents grouped by categories using a topic detection technique based on textual and visual analysis of multimedia features. Our work aims at demonstrating that with the help of semantic analysis and the combination of textual and visual features it is possible to improve the user knowledge acquisition by means of a synthesized visualization. The whole strategy has been evaluated by means of a ground truth and compared with similar approaches. Experimental results show the effectiveness of our approach, which outperforms state-of-art algorithms in topic detection combining both visual and semantic information.

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“…It is further narrated that various techniques have been utilized in this regard, including machine learning, deep learning, text and data mining, etc. It is concluded that machinelearning approaches are better in terms of classification accuracy and robustness [24][25][26].…”

Section: Literature Reviewmentioning

confidence: 99%

A Neuro-fuzzy approach for user behaviour classification and prediction

et al. 2019

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Big data and cloud computing technology appeared on the scene as new trends due to the rapid growth of social media usage over the last decade. Big data represent the immense volume of complex data that show more details about behaviours, activities, and events that occur around the world. As a result, big data analytics needs to access diverse types of resources within a decreased response time to produce accurate and stable business experimentation that could help make brilliant decisions for organizations in real-time. These developments have spurred a revolutionary transformation in research, inventions, and business marketing. User behaviour analysis for classification and prediction is one of the hottest topics in data science. This type of analysis is performed for several purposes, such as finding users' interests about a product (for marketing, e-commerce, etc.) or toward an event (elections, championships, etc.) and observing suspicious activities (security and privacy) based on their traits over the Internet. In this paper, a neuro-fuzzy approach for the classification and prediction of user behaviour is proposed. A dataset, composed of users' temporal logs containing three types of information, namely, local machine, network and web usage logs, is targeted. To complement the analysis, each user's 360-degree feedback is also utilized. Various rules have been implemented to address the company's policy for determining the precise behaviour of a user, which could be helpful in managerial decisions. For prediction, a Gaussian Radial Basis Function Neural Network (GRBF-NN) is trained based on the example set generated by a Fuzzy Rule Based System (FRBS) and the 360-degree feedback of the user. The results are obtained and compared with other state-of-the-art schemes in the literature, and the scheme is found to be promising in terms of classification as well as prediction accuracy.

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FoDoSu: Multi-document summarization exploiting semantic analysis based on social Folksonomy

Cited by 31 publications

References 15 publications

The employability skills of business graduates in Syria

The employability skills of business graduates in Syria

Using a multimedia semantic graph for web document visualization and summarization

A Neuro-fuzzy approach for user behaviour classification and prediction

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