Selected Ph.D. Thesis Abstracts

ions of the negotiation models to be implemented in the framework. Furthermore, a Domain Specific Language (DSL) has been developed and implemented to allow for easy configuration of negotiations. Experiments with simulations of different VPP scenarios have been conducted. These experiments indicate that the proposed approach is capable of integrating complex and heterogeneous DER in VPPs, while preserving the autonomous nature of the DER. Experiments are also conducted on instances of the 0/1 Knapsack problem. These experiments serve to illustrate the general applicability of the proposed solution.Future experiments will test the solution in real scenarios (http://aclausen.dk/documents/thesis.pdf). EFFICIENT KNOWLEDGE MANAGEMENT FOR NAMED ENTITIES FROM TEXT Sourav Dutta sourav.dutta@nokia.com Saarland University, Germany THE evolution of search from keywords to entities has necessitated the efficient harvesting and management of entity-centric information for constructing knowledge bases catering to various applications such as semantic search, question answering, and information retrieval. The vast amounts of natural language texts available across diverse domains on the Web provide rich sources for discovering facts about named entities such as people, places, and organizations. A key challenge, in this regard, entails the need for precise identification and disambiguation of entities across documents for extraction of attributes/relations and their proper representation in knowledge bases. Additionally, the applicability of such repositories not only involves the quality and accuracy of the stored information, but also storage management and query processing efficiency. This dissertation aims to tackle the above problems by presenting efficient approaches for entitycentric knowledge acquisition from texts and its representation in knowledge repositories. This dissertation presents a robust approach for identifying text phrases pertaining to the same named entity across huge corpora, and their disambiguation to canonical entities present in a knowledge base, by using enriched semantic contexts and link validation encapsulated in a hierarchical clustering framework. This work further presents language and consistency features for classification models to compute the credibility of obtained textual facts, ensuring quality of the extracted information. Finally, an encoding algorithm, using frequent term detection and improved data locality, to represent entities for enhanced knowledge base storage and query performance is presented (http://scidok.sulb.uni-saarland.de/volltexte/2017/