Improving similarity measures of relatedness proximity: Toward augmented concept maps

Sasson, Elan; Ravid, Gilad; Pliskin, Nava

doi:10.1016/j.joi.2015.06.003

Cited by 10 publications

(3 citation statements)

References 27 publications

(30 reference statements)

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“…The current paper which focuses on temporal proximity measurement, and the previous one (Sasson et al, 2015) which has focused on contextual proximity measurement, supplement one another. Further research is required to examine the transformative process of combining the relatedness proximity measurement and the PTA to yield augmented concept maps with these two components joined together.…”

Section: Model Validation and Conclusionmentioning

confidence: 91%

“…We have already coped with the first challenge of contextual proximity (Sasson et al, 2015) by developing, demonstrating and validating a research model for augmenting concept maps contextually. This novel method of co-word analysis helps to determine the contextual relatedness of concepts on the map by using webometrics Web counts to improve similarity measures of relatedness proximity.…”

Section: Introductionmentioning

confidence: 99%

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Creation of knowledge-added concept maps: time augmention via pairwise temporal analysis

Sasson

Ravid

Pliskin

2017

JKM

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Purpose Although acknowledged as a principal dimension in the context of text mining, time has yet to be formally incorporated into the process of visually representing the relationships between keywords in a knowledge domain. This paper aims to develop and validate the feasibility of adding temporal knowledge to a concept map via pair-wise temporal analysis (PTA). Design/methodology/approach The paper presents a temporal trend detection algorithm – vector space model – designed to use objective quantitative pair-wise temporal operators to automatically detect co-occurring hot concepts. This PTA approach is demonstrated and validated without loss of generality for a spectrum of information technologies. Findings The rigorous validation study shows that the resulting temporal assessments are highly correlated with subjective assessments of experts (n = 136), exhibiting substantial reliability-of-agreement measures and average predictive validity above 85 per cent. Practical implications Using massive amounts of textual documents available on the Web to first generate a concept map and then add temporal knowledge, the contribution of this work is emphasized and magnified against the current growing attention to big data analytics. Originality/value This paper proposes a novel knowledge discovery method to improve a text-based concept map (i.e. semantic graph) via detection and representation of temporal relationships. The originality and value of the proposed method is highlighted in comparison to other knowledge discovery methods.

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Section: Model Validation and Conclusionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Creation of knowledge-added concept maps: time augmention via pairwise temporal analysis

Sasson

Ravid

Pliskin

2017

JKM

Self Cite

View full text Add to dashboard Cite

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“…The perception of the use of data mining techniques as a strategic management tool in the banking sector from an accounting and financial perspective was also explored (Al Chahadah, El Refae & Qasim, 2018). A co-word analysis framework model has been proposed that encompasses and synthesizes unstructured textual data from web sources to improve decision-making processes to gain and maintain competitive advantage (Sasson, Ravid & Pliskin, 2015).…”

Section: Techniques Applied In Big Datamentioning

confidence: 99%

Big data as a value generator in decision support systems: a literature review

Grander

Silva

González

2021

REGE

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PurposeThis paper aims to analyze how decision support systems manage Big data to obtain value.Design/methodology/approachA systematic literature review was performed with screening and analysis of 72 articles published between 2012 and 2019.FindingsThe findings reveal that techniques of big data analytics, machine learning algorithms and technologies predominantly related to computer science and cloud computing are used on decision support systems. Another finding was that the main areas that these techniques and technologies are been applied are logistic, traffic, health, business and market. This article also allows authors to understand the relationship in which descriptive, predictive and prescriptive analyses are used according to an inverse relationship of complexity in data analysis and the need for human decision-making.Originality/valueAs it is an emerging theme, this study seeks to present an overview of the techniques and technologies that are being discussed in the literature to solve problems in their respective areas, as a form of theoretical contribution. The authors also understand that there is a practical contribution to the maturity of the discussion and with reflections even presented as suggestions for future research, such as the ethical discussion. This study’s descriptive classification can also serve as a guide for new researchers who seek to understand the research involving decision support systems and big data to gain value in our society.

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Use of Special Cases of Ontologies for Big Data Analysis in Decision Making Systems

Rogushina

Gladun

2022

Handbook on Decision Making

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Improving similarity measures of relatedness proximity: Toward augmented concept maps

Cited by 10 publications

References 27 publications

Creation of knowledge-added concept maps: time augmention via pairwise temporal analysis

Creation of knowledge-added concept maps: time augmention via pairwise temporal analysis

Big data as a value generator in decision support systems: a literature review

Use of Special Cases of Ontologies for Big Data Analysis in Decision Making Systems

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