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Dissertation
Explaining and Visualizing Structural Knowledge in Bipartite Graphs
(2023)
Bipartite graphs are an important model for the representation and analysis of relationships between two different types of entities. Datasets in this form are commonly found in many fields, such as social networks, biology, and economics. Formal concept analysis is a research approach that allows for the analysis of such bipartite graphs by clustering the data into so-called concepts and ordering those in a lattice structure.
In this thesis we propose multiple approaches for the extraction and visualization of ...
Dissertation
Attribute Exploration with Multiple Experts
(2023)
Attribute exploration is a knowledge acquisition method from the realm of formal concept analysis that allows a domain expert to efficiently uncover the dependencies in a domain. It is based on a question-answering scheme where the exploration algorithm generates questions about dependencies in the domain that are then answered by a domain expert. Even though many variants and extension to this were developed, only few attempts to incorporate multiple experts were made. The overarching goal of this thesis is to extend ...
Dissertation
Investigation and Elimination of Substructures in Formal Concept Analysis focusing on Boolean Suborders and Subcontexts
(2023)
In the field of Formal Concept Analysis, data is mainly presented in so-called formal contexts, which assign to a set of objects their respective attributes. From those concept lattices can be generated, where the objects are grouped with respect to their common attributes to represent the relationships in the data in a way that enhances the understandability for humans. However, since a concept lattice can be of exponential size compared to its associated formal context, the presented relationships often become hard ...
Dissertation
Trajectory Mapping in Physical and Topical Spaces
(2023)
In this dissertation, two different types of trajectories are investigated. In the engfirst part of this work, we investigate methods for the analysis of physical trajectories. We focus on scenarios, in which signal strengths ofWiFi access points are recorded. In detail, they are recorded through smartphone devices by people moving through a building. By means of theWiFi signal strengths, (physical) trajectories of humans are reconstructed, which reflect the locations visited over time. Accurate localization based ...
Dissertation
Orometry, Intrinsic Dimensionality and Learning: Novel Insights into Network Data
(2023-11)
Today, networks are an integral part of our world. Let it be real-life friendship networks or social connections that are based on social media. In this thesis, we contribute to the understanding of networks by studying networks from three different perspectives. First, we adapt notions and concepts from orometry to metric data and networks to gain novel insights from a local perspective. Specifically, we study measures of local outstandingness and propose concepts to derive small hierarchies from larger networks. ...
Dissertation
Social Network Mining for Analysis of Social Phenomena
(2019)
In den letzten zehn Jahren wurde eine große Menge an Daten über menschliche Interaktionen verfügbar, die entweder aus sozialen Online-Netzwerken stammen oder von tragbaren Geräten erfasst wurden. Die klassischen Sozialwissenschaften bieten keine Instrumente für die Durchführung datengetriebener Forschung mit solchen Daten. Die computerorientierte Sozialwissenschaft (Computational Social Science) versucht, diese Lücke zu schließen, indem sie Methoden für Data-Mining und maschinelles Lernen zur Nutzung der Daten aus ...
Dissertation
Analyzing Given Names
(2020)
Computer science has evolved so much that it influences almost every part of human live. A particularly personal part of human live is the selection of a given name for a newborn baby. The name discovery service Nameling is our way to help expectant parents with this decision. It is a web service that helps parents to find a given name for their newborn baby. We want to further improve the user experience of Nameling by personalizing its name results for the current user. We deem this important, because the selection ...
Dissertation
Discovering Knowledge in Bipartite Graphs with Formal Concept Analysis
(2019)
Since the 1970s knowledge based approaches are a crucial part of artificial intelligence (AI) research. In this work we investigate data sets in the form of bipartite graphs, i.e., graphs where a bipartition of the vertex set respecting the edge set can be found, for knowledge. To this end we first relate those bipartite graphs to the structure formal context, as used in formal concept analysis (FCA). This link enables us to employ the whole tool-set of FCA to bipartite graphs and therefore, notably, to bipartite ...
Dissertation
Relatedness in Evidence Networks
(2014-10-10)
Einhergehend mit der Entwicklung und zunehmenden Verfügbarkeit des Internets hat sich die Art der Informationsbereitstellung und der Informationsbeschaffung deutlich geändert. Die einstmalige Trennung zwischen Publizist und Konsument wird durch kollaborative Anwendungen des sogenannten Web 2.0 aufgehoben, wo jeder Teilnehmer gleichsam Informationen bereitstellen und konsumieren kann. Zudem können Einträge anderer Teilnehmer erweitert, kommentiert oder diskutiert werden. Mit dem Social Web treten schließlich die ...
Dissertation
Capturing Emergent Semantics from Social Annotation Systems
(2013-02-26)
The ongoing growth of the World Wide Web, catalyzed by the increasing possibility of ubiquitous access via a variety of devices, continues to strengthen its role as our prevalent information and commmunication medium. However, although tools like search engines facilitate retrieval, the task of finally making sense of Web content is still often left to human interpretation. The vision of supporting both humans and machines in such knowledge-based activities led to the development of different systems which allow to ...