Visual Analytics and Similarity Search - Interest-based Similarity Search in Scientific Data

Blazevic, Midhad; Sina, Lennart B.; Burkhardt, Dirk; Siegel, Melanie; Nazemi, Kawa

doi:10.1109/iv53921.2021.00041

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…Figure 4 shows an alternative version of the UI focusing on visual analytics. The calculations and backend are the same; however, the frontend that is based on our previous works [8,28] grants the user the ability to explore the results better. The user has various filtering options seen in (2) while at the same time having a listed view seen in ( 1) that shows more information than Figure 3.…”

Section: Results Presentationmentioning

confidence: 99%

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Recommendation of Scientific Publications—A Real-Time Text Analysis and Publication Recommendation System

et al. 2023

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Studies have shown that although having more information improves the quality of decision-making, information overload causes adverse effects on decision quality. Visual analytics and recommendation systems counter this adverse effect on decision-making. Accurately identifying relevant information can reduce the noise during exploration and improve decision-making. These countermeasures also help scientists make correct decisions during research. We present a novel and intuitive approach that supports real-time collaboration. In this paper, we instantiate our approach to scientific writing and propose a system that supports scientists. The proposed system analyzes text as it is being written and recommends similar publications based on the written text through similarity algorithms. By analyzing text as it is being written, it is possible to provide targeted real-time recommendations to improve decision-making during research by finding relevant publications that might not have been otherwise found in the initial research phase. This approach allows the recommendations to evolve throughout the writing process, as recommendations begin on a paragraph-based level and progress throughout the entire written text. This approach yields various possible use cases discussed in our work. Furthermore, the recommendations are presented in a visual analytics system to further improve scientists’ decision-making capabilities.

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Section: Results Presentationmentioning

confidence: 99%

“…In our previous work, we examined how to identify relevant topics based on the interests of researchers and how to present the found relevant publications in a manner that allows a fast annotation during exploration [8]. In our current work, we develop our idea further by analyzing publications or text as they are being written.…”

Section: Introductionmentioning

confidence: 99%

Recommendation of Scientific Publications—A Real-Time Text Analysis and Publication Recommendation System

et al. 2023

Self Cite

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“…Additionally, comparative experiments were conducted between this study and related works [4,25,31], which performed comprehensive similarity measurement based on topic models on the MSRP dataset. They also conducted experiments comparing different numbers of topics and concluded that the models produced similar results when the number of topics was set to 20, 40, 100 compared to higher values of 200, 300.…”

Section: Comparison Experimentsmentioning

confidence: 99%

A Semantic Similarity Analysis Method for English Texts Based on Prior Information

Deng,

Zhou,

Wang

et al. 2024

Preprint

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With the rapid development of Internet technology, a massive amount of English text has emerged, creating an urgent need to evaluate the semantic similarity of these texts across different domains. Existing research on semantic similarity faces the challenge of insufficient prior knowledge. Therefore, a new method has been proposed, called Knowledge Prior Topic Model (KPTM), for analyzing text semantic similarity. This method incorporates semantic relationship data from the large-scale English knowledge base, Probase, into the topic model and combines it with an effective keyword serialization approach to calculate the hybrid measurement of concepts and keywords for text similarity. Experimental results demonstrate that the proposed method contributes to improving semantic similarity and outperforms other models in comparative experiments.

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“…The table view analysis uses the PGDU_LM algorithm to calculate the propensity scores for the five graduation destinations based on students' course grades, denoted as Lscore_ij. The comparative analysis of the Lscore_ij scores predicted the students' graduation destination (Blazevic et al, 2021) with seven columns in the table, each row representing one student, the first column representing the student's name, the second column the major, and the remaining five columns the propensity scores for employment, master's degree, abroad, freelance, and unemployed. As shown in Figure 4(B 2 ), the bubble chart shows more visually the propensity score of each student in each graduation destination and thus analyzes the predicted results of graduation destinations (Yu, 2021).…”

Section: The Graduate Destination Prediction Viewmentioning

confidence: 99%

A Prediction and Visual Analysis Method for Graduation Destination of Undergraduates Based on LambdaMART Model

Chen

Sun

Wei

et al. 2022

International Journal of Information and Communication Technology Education

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Predicting graduation destination can help students determine their learning goals in advance, help faculty optimize curriculum and provide career guidance for students. In this paper, the authors first propose a prediction algorithm for graduation destination of undergraduates based on LambdaMART, called PGDU_LM, which uses Spearman correlation coefficient to analyze the correlation between subjects and graduate destinations and extract characteristic subjects, and uses LambdaMART ranking model to calculate students' propensity scores in different graduate destinations. Second, a visual analysis method for students' course grades and graduation destinations is designed to support users to analyze student data from multiple dimensions. Finally, a prediction and visual analysis system for graduation destination of undergraduates, PGDUvis, is designed and implemented. A case study and user evaluation on this system was conducted using the academic data of students from five majors who graduated from a university during 2016-2020, and the results illustrate the effectiveness of this method.

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Visual Analytics and Similarity Search - Interest-based Similarity Search in Scientific Data

Cited by 8 publications

References 22 publications

Recommendation of Scientific Publications—A Real-Time Text Analysis and Publication Recommendation System

Recommendation of Scientific Publications—A Real-Time Text Analysis and Publication Recommendation System

A Semantic Similarity Analysis Method for English Texts Based on Prior Information

A Prediction and Visual Analysis Method for Graduation Destination of Undergraduates Based on LambdaMART Model

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