Semantic Approach to Web-Based Discovery of Unknowns to Enhance Intelligence Gathering

2013 ◽  
Vol 3 (1) ◽  
pp. 27-42
Author(s):  
Natalia Danilova ◽  
David Stupples
Keyword(s):  
Language Processing ◽  
Web Search ◽  
Worked Examples ◽  
Search Method ◽  
Semantic Approach ◽  
Web Based ◽  
Intelligence Gathering ◽  
Dempster Shafer Theory ◽  
Shafer Theory

A semantic Web-based search method is introduced that automates the correlation of topic-related content for discovery of hitherto unknown intelligence from disparate and widely diverse Web-sources. This method is in contrast to traditional search methods that are constrained to specific or narrowly defined topics. The method is based on algorithms from Natural Language Processing combined with techniques adapted from grounded theory and Dempster-Shafer theory to significantly enhance the discovery of related Web-sourced intelligence. This paper describes the development of the method by showing the integration of the mathematical models used. Real-world worked examples demonstrate the effectiveness of the method with supporting performance analysis, showing that the quality of the extracted content is significantly enhanced comparing to the traditional Web-search approaches.

Semantic Approach to Web-Based Discovery of Unknowns to Enhance Intelligence Gathering

2017 ◽  
pp. 196-213
Author(s):  
Natalia Danilova ◽  
David Stupples
Keyword(s):  
Language Processing ◽  
Web Search ◽  
Worked Examples ◽  
Search Method ◽  
Semantic Approach ◽  
Web Based ◽  
Intelligence Gathering ◽  
Dempster Shafer Theory ◽  
Shafer Theory

A semantic Web-based search method is introduced that automates the correlation of topic-related content for discovery of hitherto unknown intelligence from disparate and widely diverse Web-sources. This method is in contrast to traditional search methods that are constrained to specific or narrowly defined topics. The method is based on algorithms from Natural Language Processing combined with techniques adapted from grounded theory and Dempster-Shafer theory to significantly enhance the discovery of related Web-sourced intelligence. This paper describes the development of the method by showing the integration of the mathematical models used. Real-world worked examples demonstrate the effectiveness of the method with supporting performance analysis, showing that the quality of the extracted content is significantly enhanced comparing to the traditional Web-search approaches.

Effective Intelligent Data Mining Using Dempster-Shafer Theory

2008 ◽  
pp. 2943-2963
Author(s):  
Malcolm J. Beynon
Keyword(s):  
Data Mining ◽  
Missing Values ◽  
Uncertain Reasoning ◽  
Worst Case ◽  
Data Mining Techniques ◽  
Imperfect Data ◽  
Dempster Shafer Theory ◽  
External Management ◽  
Shafer Theory

The efficacy of data mining lies in its ability to identify relationships amongst data. This chapter investigates that constraining this efficacy is the quality of the data analysed, including whether the data is imprecise or in the worst case incomplete. Through the description of Dempster-Shafer theory (DST), a general methodology based on uncertain reasoning, it argues that traditional data mining techniques are not structured to handle such imperfect data, instead requiring the external management of missing values, and so forth. One DST based technique is classification and ranking belief simplex (CaRBS), which allows intelligent data mining through the acceptance of missing values in the data analysed, considering them a factor of ignorance, and not requiring their external management. Results presented here, using CaRBS and a number of simplex plots, show the effect of managing and not managing of imperfect data.

Effective Intelligent Data Mining Using Dempster-Shafer Theory

2011 ◽  
pp. 203-223
Author(s):  
Malcolm J. Beynon
Keyword(s):  
Data Mining ◽  
Missing Values ◽  
Uncertain Reasoning ◽  
Worst Case ◽  
Data Mining Techniques ◽  
Imperfect Data ◽  
Dempster Shafer Theory ◽  
External Management ◽  
Shafer Theory

The efficacy of data mining lies in its ability to identify relationships amongst data. This chapter investigates that constraining this efficacy is the quality of the data analysed, including whether the data is imprecise or in the worst case incomplete. Through the description of Dempster-Shafer theory (DST), a general methodology based on uncertain reasoning, it argues that traditional data mining techniques are not structured to handle such imperfect data, instead requiring the external management of missing values, and so forth. One DST based technique is classification and ranking belief simplex (CaRBS), which allows intelligent data mining through the acceptance of missing values in the data analysed, considering them a factor of ignorance, and not requiring their external management. Results presented here, using CaRBS and a number of simplex plots, show the effect of managing and not managing of imperfect data.

A Web Based Method for Measuring Semantic Relatedness between Words

2013 ◽  
Vol 347-350 ◽  
pp. 783-787
Author(s):  
Bo You ◽  
Ting Ting He ◽  
Fang Li
Keyword(s):  
Kernel Function ◽  
Language Processing ◽  
Web Search ◽  
Rank Correlation ◽  
Semantic Relatedness ◽  
Spearman Rank Correlation ◽  
Web Based ◽  
Web Search Engine ◽  
Nature Language Processing ◽  
Spearman Rank Correlation Coefficient

Semantic relatedness measures play important roles in many fields, such as information retrieval and Nature Language Processing. There are mainly two kinds of traditional methods to measure semantic relatedness: dictionary based and corpus based. However, with the development of information technology, web search engine is used to do this work. In this paper, we propose a method integrating page counts and web-based kernel function for measuring semantic relatedness between words. It gets a better result than using page counts and web-based kernel function alone. Experimental results show Spearman rank correlation coefficient can reach 0.63 and Correlation reach 0.724.

scholarly journals Risk Analysis with the Dempster–Shafer Theory for Smart City Planning: The Case of Qatar

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3080
Author(s):  
Reem Al Sharif ◽  
Shaligram Pokharel
Keyword(s):  
Quality Of Life ◽  
Risk Assessment ◽  
Smart City ◽  
City Planning ◽  
Smart Cities ◽  
Principal Component ◽  
Assessment Method ◽  
Dempster Shafer Theory ◽  
Shafer Theory

Smart cities support the enhancement of the quality of life of their residents, for which the use of a robust integrated platform of information and communication technology is required. However, not all cities have similar technology infrastructure and a similar understanding of the quality of life. Therefore, holistic planning, resource support, security, continuous updates, and dynamic operational enhancements should be considered while planning smart cities. However, a smart city could be vulnerable to security threats and a loss of personal or classified information due to the complexity of technology integration. Therefore, understanding and assessing different risks and embedding risk management mechanisms would be required to minimize vulnerability exposure in smart cities. This paper proposes a risk assessment method using the Dempster–Shafer theory for smart city planning. The Dempster–Shafer theory is used here to analyze the risks perceptions of experts. The principal component analysis method is used to analyze the data obtained from risk assessment. The application of this method is determined through a smart city test case in Qatar.

scholarly journals eRevise: Using Natural Language Processing to Provide Formative Feedback on Text Evidence Usage in Student Writing

2019 ◽  
Vol 33 ◽  
pp. 9619-9625 ◽  
Author(s):  
H. Zhang ◽  
A. Magooda ◽  
D. Litman ◽  
R. Correnti ◽  
E. Wang ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Formative Feedback ◽  
Student Writing ◽  
Web Based ◽  
Essay Scoring ◽  
Text Writing

Writing a good essay typically involves students revising an initial paper draft after receiving feedback. We present eRevise, a web-based writing and revising environment that uses natural language processing features generated for rubricbased essay scoring to trigger formative feedback messages regarding students’ use of evidence in response-to-text writing. By helping students understand the criteria for using text evidence during writing, eRevise empowers students to better revise their paper drafts. In a pilot deployment of eRevise in 7 classrooms spanning grades 5 and 6, the quality of text evidence usage in writing improved after students received formative feedback then engaged in paper revision.

Telehealth During COVID-19 for Remote Vision Rehabilitation of a Patient with mTBI

2020 ◽  
pp. 191-198
Keyword(s):  
Veterans Health ◽  
Health Administration ◽  
Web Based ◽  
Home Setting ◽  
Therapeutic Goals ◽  
Vision Rehabilitation ◽  
Novel Coronavirus ◽  
Performance Challenges ◽  
Perception Of Performance

Background: Binocular and accommodative vision problems are common after mild traumatic brain injury (mTBI). Traditionally, the management of visual dysfunctions following mTBI included in-office vision rehabilitation with a trained eye care provider. The concept of providing telehealth for remote vision rehabilitation in mTBI patients is a relatively novel practice that has not been widely utilized until the recent outbreak of the 2019 novel coronavirus (COVID-19) pandemic. Case Report: We describe the implementation of telehealth for remote vision rehabilitation during COVID-19 within the Veterans’ Health Administration (VHA) system in an adult patient with multiple confirmed histories of mTBI. Conclusion: Our telehealth remote vision rehabilitation was successfully implemented utilizing established VHA’s web-based videoconferencing tools. Therapeutic goals identified prior to COVID 19 were addressed without any challenges. The delivery of vision rehabilitation intervention via telehealth allowed for the continuance of services within the home setting that led to improvements in functional vision, decreased perception of performance challenges, and improved quality of life.

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