Master Thesis: Knowledge Extraction Solution with Natural Language Processing and Knowledge Graphs
Type: Master thesis
Student: Kiara Marnitt Ascencion Arevalo
Supervisor: Prof. Dr. Andreas Harth, Andreas Belger
Abstract: Organizations have a wide variety of available data coming from different sources. To generate value, it is essential for companies to exploit all this information and convert it into knowledge. However, the different data sets are rarely interoperable as the data varies greatly between sources in terms of type, scope, and structure. For example, an organization’s context information is often provided as unstructured texts like analyst reports, public tenders, or press mentions. This thesis aims to create a solution to extract information from unstructured texts with the support of state-of-the-art NLP methods and represent it in a structured form through the application of Knowledge Graphs and Ontologies.