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Modern knowledge bases contain and organize knowledge from many different topic areas. Apart from specific entity information, they also store information about their relationships amongst each other. Combining this information results in a knowledge graph that can be particularly helpful in cases where relationships are of central importance. Among other applications, modern risk assessment in the financial sector can benefit from the inherent network structure of such knowledge graphs by assessing the consequences and risks of certain events, such as corporate insolvencies or fraudulent behavior, based on the underlying network structure. As public knowledge bases often do not contain the necessary information for the analysis of such scenarios, the need arises to create and maintain dedicated domain-specific knowledge bases.
This thesis investigates the process of creating domain-specific knowledge bases from structured and unstructured data sources. In particular, it addresses the topics of named entity recognition (NER), duplicate detection, and knowledge validation, which represent essential steps in the construction of knowledge bases.
As such, we present a novel method for duplicate detection based on a Siamese neural network that is able to learn a dataset-specific similarity measure which is used to identify duplicates. Using the specialized network architecture, we design and implement a knowledge transfer between two deduplication networks, which leads to significant performance improvements and a reduction of required training data.
Furthermore, we propose a named entity recognition approach that is able to identify company names by integrating external knowledge in the form of dictionaries into the training process of a conditional random field classifier. In this context, we study the effects of different dictionaries on the performance of the NER classifier. We show that both the inclusion of domain knowledge as well as the generation and use of alias names results in significant performance improvements.
For the validation of knowledge represented in a knowledge base, we introduce Colt, a framework for knowledge validation based on the interactive quality assessment of logical rules. In its most expressive implementation, we combine Gaussian processes with neural networks to create Colt-GP, an interactive algorithm for learning rule models. Unlike other approaches, Colt-GP uses knowledge graph embeddings and user feedback to cope with data quality issues of knowledge bases. The learned rule model can be used to conditionally apply a rule and assess its quality.
Finally, we present CurEx, a prototypical system for building domain-specific knowledge bases from structured and unstructured data sources. Its modular design is based on scalable technologies, which, in addition to processing large datasets, ensures that the modules can be easily exchanged or extended. CurEx offers multiple user interfaces, each tailored to the individual needs of a specific user group and is fully compatible with the Colt framework, which can be used as part of the system.
We conduct a wide range of experiments with different datasets to determine the strengths and weaknesses of the proposed methods. To ensure the validity of our results, we compare the proposed methods with competing approaches.
Das Rahmenkonzept der Universitätsschule Potsdam beschreibt die Wertegrundlage und das pädagogisch-didaktische sowie das wissenschaftliche Fundament einer zu gründenden Universitätsschule Potsdam. Wie andere Universitätsschulen soll sich auch diese Schule durch eine enge und institutionalisierte Beziehung zwischen Schule und Universität auszeichnen, die den ständigen Wissenstransfer zwischen Schulpraxis, Wissenschaft, Lehrkräftebildung und Schulverwaltung unterstützt. Das Rahmenkonzept legt die Grundlagen für eine inklusive Schule, deren Schüler:innen einen Querschnitt der Gesellschaft abbilden, und die in ungleichheitssensiblen Bildungsangeboten alle Bildungsabschlüsse des Landes Brandenburg anbietet. Die Universitätsschule soll den starken Segregationsprozessen in Potsdam entgegenwirken.
Im Leitbild werden die Grundwerte (Nachhaltigkeit, Inklusion und Bildungsgerechtigkeit, Menschenrechte und Demokratie, Gemeinschaft, Ganzheitlichkeit) und die Bildungsziele (Transferfähigkeit, kritisch-reflexives Denken und lebensbegleitendes Lernen, Diversitätsbewusstsein und Transkulturalität, Selbstkompetenz und Beziehungskompetenz, Kulturtechniken und digitale Kompetenz) der Universitätsschule dargestellt. Das Pädagogische Konzept veranschaulicht, wie Werte und Bildungsziele in den Bereichen Schulform, Schulkultur, Lernkultur sowie Lernorte und Lernumgebung ausgestaltet werden können. Schließlich wird die Universitätsschule als lernende und lehrende Institution beschrieben, die ein Ort des Transfers von Bildungsinnovationen ist. Dafür soll eine Transferwerkstatt in der Schule verankert werden, die den Wissensaustausch der schulrelevanten Akteur:innen unterstützt und gestaltet.