Our group includes PostDocs, PhD students, and student assistants, and is headed by Prof. Felix Naumann. If you are interested in joining our team, please contact Felix Naumann.
For bachelor students we offer German lectures on database systems in addition to paper- or project-oriented seminars. Within a one-year bachelor project, students finalize their studies in cooperation with external partners. For master students we offer courses on information integration, data profiling, and information retrieval enhanced by specialized seminars, master projects and we advise master theses.
Most of our research is conducted in the context of larger research projects, in collaboration across students, across groups, and across universities. We strive to make available most of our datasets and source code.
AbstractThis paper establishes a semi-supervised strategy for extracting various types of complex business relationships from textual data by using only a few manually provided company seed pairs that exemplify the target relationship. Additionally, we offer a solution for determining the direction of asymmetric relationships, such as “ownership of”. We improve the reliability of the extraction process by using a holistic pattern identification method that classifies the generated extraction patterns. Our experiments show that we can accurately and reliably extract new entity pairs occurring in the target relationship by using as few as five labeled seed pairs.
Improving Company Recognition from Unstructured Text by using Dictionaries. Loster, Michael; Zuo, Zhe; Naumann, Felix; Maspfuhl, Oliver; Thomas, Dirk (2017). 610–619.
AbstractWhile named entity recognition is a much addressed research topic, recognizing companies in text is of particular difficulty. Company names are extremely heterogeneous in structure, a given company can be referenced in many different ways, their names include person names, locations, acronyms, numbers, and other unusual tokens. Further, instead of using the official company name, quite different colloquial names are frequently used by the general public. We present a machine learning (CRF) system that reliably recognizes organizations in German texts. In particular, we construct and employ various dictionaries, regular expressions, text context, and other techniques to improve the results. In our experiments we achieved a precision of 91.11% and a recall of 78.82%, showing significant improvement over related work. Using our system we were able to extract 263,846 company mentions from a corpus of 141,970 newspaper articles.
CohEEL: Coherent and Efficient Named Entity Linking through Random Walks. Gruetze, Toni; Kasneci, Gjergji; Zuo, Zhe; Naumann, Felix in Web Semantics: Science, Services and Agents on the World Wide Web (2016). 37(C) 75–89.
AbstractIn recent years, the ever-growing amount of documents on the Web as well as in digital libraries led to a considerable increase of valuable textual information about entities. Harvesting entity knowledge from these large text collections is a major challenge. It requires the linkage of textual mentions within the documents with their real-world entities. This process is called entity linking. Solutions to this entity linking problem have typically aimed at balancing the rate of linking correctness (precision) and the linking coverage rate (recall). While entity links in texts could be used to improve various Information Retrieval tasks, such as text summarization, document classification, or topic-based clustering, the linking precision is the decisive factor. For example, for topic-based clustering a method that produces mostly correct links would be more desirable than a high-coverage method that leads to more but also more uncertain clusters. We propose an efficient linking method that uses a random walk strategy to combine a precision-oriented and a recall-oriented classifier in such a way that a high precision is maintained, while recall is elevated to the maximum possible level without affecting precision. An evaluation on three datasets with distinct characteristics demonstrates that our approach outperforms seminal work in the area and shows higher precision and time performance than the most closely related state-of-the-art methods.
Bootstrapping Wikipedia to Answer Ambiguous Person Name Queries. Gruetze, Toni; Kasneci, Gjergji; Zuo, Zhe; Naumann, Felix (2014).
