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

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.

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Advanced Seminar toward Visual Analaytics in next Summer Semester at h_da/FBI (computer science)

In the upcoming summer semester 2020, our team provides an advanced seminar at the department of computer science at the Darmstadt University of Applied Sciences. The topic of the seminar will be toward “Visual Analytics for Smart Manufacturing and Trend Analysis”:

Due to the increasing digitization, analyzes are a constant necessity, which also applies to the identification of errors, problems or opportunities for improvement. With visual analytics, the combination of algorithmic and massive calculation opportunities of computer-based systems on the one side as well as the visual and cognitive skills of the users on the other side are used together. The seminar focuses on the use of visual analytics in the two current areas of smart manufacturing and trend analysis. On behalf of the given task, current approaches, methods and implementations should be researched and examined. The goal should be a scientific elaboration based on empirical and scientific standards.

Interested students are invited to register soon, since the number of places will be limited.

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Information Science: The new degree program at Darmstadt University of Applied Sciences

Digitization contributes to the accumulation of knowledge in immense amounts of data, but often unstructured and widely scattered. Information Scientists bring this knowledge together and make it usable for business and society. The new study program Information Science at the University of Applied Sciences Darmstadt (h_da) trains students to become experts in the professional handling of data, information and knowledge. The bachelor and master program starts for the first time in the winter semester 2019/20 and replaces the previous course Information Science. Applications are possible from the 15th of May.

“Since digitization processes and rapid technological changes have a major impact on companies, information scientists also contribute to future and innovation processes,” emphasizes Prof. Dr. med. Kawa Nazemi. He is in charge of the Bachelor’s degree program Information Science. His graduates also qualify for leadership positions in companies, institutions, media organizations, administrations or libraries as well as for research activities. The master’s degree also qualifies for a doctorate. There are perspectives at the Hochschule Darmstadt in the cross-university doctoral program “Applied Computer Science”, in which h_da is involved.

The full news article (in German) can be found at:
https://www.h-da.de/news-anzeigen/meldung-einzelansicht/news/information-science-neuer-studiengang-an-der-hochschule-darmstadt/

Information about the application and the admission requirements can be found at:
https://h-da.de/bewerben

Detailed information on the Information Science program can be found at:
https://iw.h-da.de/

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