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Beschreibung:
Nowadays, the Web provides an infrastructure to share all kinds of information which are easily accessible to humans around the world. Furthermore, the amount of information is growing rapidly and requires computing machines to process, comprehend, and extract useful information tailored for the end-users. The Semantic Web and semantic technologies play a prominent role to enable knowledge representation and reasoning for these computational processes. Semantic technologies such as ontologies and knowledge graphs are being used in various application domains, including data governance, knowledge management, chatbots, biology, etc., which aim at providing proper infrastructure to analyze the knowledge and reasoning for the computers. Semantic Question Answering systems are among the most desired platforms in recent years that facilitate access to information in knowledge graphs. They provide a natural language interface that permits the users to ask their questions posed in a natural language, without any understanding of the underlying technologies. We thus study question answering systems over knowledge graphs which aim to map an input question in natural language into a formal query, intending to retrieve a concise answer from the knowledge graph. This is a highly challenging task due to the intrinsic complexity of the natural language, such that the resulting query does not always accurately subserve the user intent, particularly, for more complex and less common questions. In this thesis, we explore semantic question answering systems in a modular manner in order to discover the bottlenecks and mitigate the challenges in each part independently. Therefore, we focus on the individual modules and propose two innovative models: First, a reinforcement learning-based approach to parse the input question using distant labels, and second, an algorithm that generates the candidate formal queries based on a set of linked entities and relations. The latter additionally uses a neural network based model to rank the ...