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Midhad Blazevic defended his Master Thesis on Visual Search and Exploration for Scientific Publications through Similarity

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In his thesis, Midhad Blazevic alyzes exploratory search systems which use sophisticated features, visualizations and similarity-based algorithms to enhance exploratory searches. Examining how similarity algorithms are currently used in combination with elements from information retrieval, natural language processing and visualizations, but also examine what exploratory search is, what the requirements are and what makes it so special in modern times. Furthermore, the user himself or herself will be analyzed as user behavior during exploratory searches is a key factor that has to be taken into consideration when looking to optimize the exploratory search process overall. Based on these aspects, means of improvement will be developed and showcased, which will be used to determine if there is an improvement in comparison to other well-known systems. The outcome of this thesis will present a prototype of an exploratory search system along with a practical use case.

Thesis Presentation: User-Centered Scientific Publication Research and Exploration in Digital Libraries

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When: 10/12/2018 15:30 Where: Frauhofer IGD, Fraunhoferstr. 5, Room 220 Who: Namitha Chandrashekara (Author), Dipl.-Inf. Dirk Burkhardt (Advisor/Co-Supervisor), Prof. Dr. Arjan Kuijper (Supervisor) What: Master Thesis – “User-Centered Scientific Publication Research and Exploration in Digital Libraries” Abstract: Scientific research is the basis for innovations. Surveying the research papers is an essential step in the process of research. It is vital to elaborate the intended writing of state of the art. Due to the rapid growth in scientific and technical discoveries, there is an increasing availability of publications. The traditional method of publishing the research papers includes physical libraries and books. These become hard to document with the rise in the number of publications produced. Due to the above mentioned problem, online archives for scientific publications have become more prominent in the scientific community. The availability of the search engines and digital libraries help the researchers in identifying the scientific publications. However, they provide limited search capabilities and visual interface. Most of the search engines have a single field to search and provides basic filtering of the data. Therefore, even with popular search engines, it is hard for the user to survey the research papers as it limits the user to search based on simple keywords. The relationships across multiple fields of the publications are also not considered such as to find the related papers and papers based on the citations or references. The main aim of the thesis is to develop a visual access to the digital libraries based on the scientific research and exploration. It helps the user in writing scientific papers. A scientific research and exploration model is developed based on the previous information visualization model for visual trend analysis with digital libraries, and with consideration of the research process. The principles from Visual Seeking Mantra are incorporated to have an interactive user interface that enhances the user experience. In the scope of this work, a research on Human Computer Interaction, particularly considering the aspects of user interface design are done. An overview of the scientific research, its types and various aspects of data analysis are researched. Different research models, existing approaches and tools that help the researchers in literature survey are also researched. The architecture and the implementation details of scientific research and exploration that provides visual access to digital libraries are presented.

Events

Thesis Presentation: Named-Entity Recognition on Publications and Raw-Text for Meticulous Insight at Visual Trend Analytics

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Where: TU Darmstadt / GRIS, Fraunhoferstr. 5 (Darmstadt), Room tba

!!!!! Due to the Corona crisis and the accompanying restrictions at the TU Darmstadt, the exam will be non-public! !!!!!

Who: Ubaid Rana (Author), Prof. Dr. Arjan Kuijper (Supervisor), Dipl.-Inf. Dirk Burkhardt (Advisor/Co-Supervisor)
What: Master Thesis – “Named-Entity Recognition on Publications and Raw-Text for Meticulous Insight at Visual Trend Analytics”

Abstract:

In the modern data-driven era, a massive amount of research documents are available from publicly accessible digital libraries in the form of academic papers, journals and publications. This plethora of data does not lead to new insights or knowledge. Therefore, suitable analysis techniques and graphical tools are needed to derive knowledge in order to get insight of this big data. To address this issue, researchers have developed visual analytical systems along with machine learning methods, e.g text mining with interactive data visualization, which leads to gain new insights of current and upcoming technology trends. These trends are significant for researchers, business analysts, and decision-makers for innovation, technology management and to make strategic decisions.
Nearly every existing search portal uses the traditional meta-information e.g only about the author and title to find the documents that match a search request and overlook the opportunity of extracting content-related information. It limits the possibility of discovering most relevant publications, moreover it lacks the knowledge required for trend analysis. To collect this very concrete information, named entity recognition must be used to be able to better identify the results and trends. The state-of-the-art systems use static approach for named entity recognition which means that upcoming technologies remain undetected. Modern techniques like distant supervision methods leverage big existing community-maintained data sources, such as Wikipedia, to extract entities dynamically. Nonetheless, these methods are still unstable and have never been tried on complex scenarios such as trend analysis before.
The aim of this thesis is to enable entity recognition on both static tables and dynamic community updated data sources like Wikipedia & DBpedia for trend analysis. To accomplish this goal, a model is suggested which enabled entity extraction on DBpedia and translated the extracted entities into interactive visualizations. The analysts can use these visualizations to gain trend insights, evaluate research trends or to analyze prevailing market moods and industry trends.

Thesis Presentation: Visual Trend Analysis on Condensed Expert Data beside Research Library Data for Enhanced Insights

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Where: TU Darmstadt / GRIS, Fraunhoferstr. 5, Room 073 Who: Muhammad Ali Riaz (Author), Prof. Dr. Arjan Kuijper (Supervisor), Dipl.-Inf. Dirk Burkhardt (Advisor/Co-Supervisor) What: Master Thesis – “Visual Trend Analysis on Condensed Expert Data beside Research Library Data for Enhanced Insights” Abstract: In the present age of information, we live amidst seas of digital text documents including academic publications, white papers, news articles, patents, newspapers. To tackle the issue of the ever-increasing amount of text documents, researchers from the field of text mining and information visualization have developed tools and techniques to facilitate text analysis. In the context of visual trend analysis on text data, the use of well-structured patent data and public digital libraries are quite established. However, both sources of information have their limitations. For instance, the registration process for patents takes at least one year, which makes the extracted insights not suitable to research on present scenarios. In contrast to patent data, the digital libraries are up-to-date but provide high-level insights, only limited to broader research domains, and the data usage is almost restricted on meta information, such as title, author names and abstract, and they do not provide full text. For a certain type of detailed analysis such as competitor analysis or portfolio analysis, data from digital libraries is not enough, it would also make sense to analyze the full-text. Even more, it can be beneficial to analyze only a limited dataset that is filtered by an expert towards a very specific field, such as additive printing or smart wearables for medical observations. Sometimes also a mixture of both digital library data and manually collected documents is relevant to be able to validate a certain trend, where one gives a big picture and other gives a very condensed overview of the present scenario. The thesis aims, therefore, to focus on such manually collected documents by experts that can be defined as condensed data. So, the major goal of this thesis is to conceptualize and implement a solution that enables the creation and analysis of such a condensed data set and compensate therewith the limitations of digital library data analysis. As a result, a visual trend analysis system for analyzing text documents is presented, it utilizes the best of both state-of-the-art text analytics and information visualization techniques. In a nutshell, the presented trend analysis system does two things. Firstly, it is capable of extracting raw data from text documents in the form of unstructured text and meta-data, convert it into structured and analyzable formats, extract trends from it and present it with appropriate visualizations. Secondly, the system is also capable of performing gap-analysis tasks between two data sources, which in this case is digital library data and data from manually collected text documents (Condensed Expert Data). The proposed visual trend analysis system can be used by researchers for analyzing the research trends, organizations to identify current market buzz and industry trends, and many other use-cases where text data is the primary source of valuable information.

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