For bachelor students we offer German lectures on database systems in addition with 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, search engines and information retrieval enhanced by specialized seminars, master projects and advised 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 data sets and source code.
AbstractIn today's social media, news often spread faster than in mainstream media, along with additional context and aspects about the current affairs. Consequently, users in social networks are up-to-date with the details of real-world events and the involved individuals. Examples include crime scenes and potential perpetrator descriptions, public gatherings with rumors about celebrities among the guests, rallies by prominent politicians, concerts by musicians, etc. We are interested in the problem of tracking persons mentioned in social media, namely detecting the locations of individuals by leveraging the online discussions about them. Existing literature focuses on the well-known and more convenient problem of user location detection in social media, mainly as the location discovery of the user profiles and their messages. In contrast, we track individuals with text mining techniques, regardless whether they hold a social network account or not. We observe what the community shares about them and estimate their locations. Our approach consists of two steps: firstly, we introduce a noise filter that prunes irrelevant posts using a recursive partitioning technique. Secondly, we build a model that reasons over the set of messages about an individual and determines his/her locations. In our experiments, we successfully trace the last U.S. presidential candidates through millions of tweets published from November 2015 until January 2017. Our results outperform previously introduced techniques and various baselines.
What was Hillary Clinton doing in Katy, Texas?. Gruetze, Toni; Krestel, Ralf; Lazaridou, Konstantina; Naumann, Felix (2017).
AbstractDuring the last presidential election in the United States of America, Twitter drew a lot of attention. This is because many leading persons and organizations, such as U.S. president Donald J. Trump, showed a strong affection to this medium. In this work we neglect the political contents and opinions shared on Twitter and focus on the question: Can we determine and track the physical location of the presidential candidates based on posts in the Twittersphere?
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.
Topic Shifts in StackOverflow: Ask it like Socrates. Gruetze, Toni; Krestel, Ralf; Naumann, Felix (2016). (Vol. 9612) 213–221.
AbstractCommunity based question-and-answer (Q&A) sites rely on well posed and appropriately tagged questions. However, most platforms have only limited capabilities to support their users in finding the right tags. In this paper, we propose a temporal recommendation model to support users in tagging new questions and thus improve their acceptance in the community. To underline the necessity of temporal awareness of such a model, we first investigate the changes in tag usage and show different types of collective attention in StackOverflow, a community-driven Q&A website for computer programming topics. Furthermore, we examine the changes over time in the correlation between question terms and topics. Our results show that temporal awareness is indeed important for recommending tags in Q&A communities.
AbstractSocial networking services, such as Facebook, Google+, and Twitter are commonly used to share relevant Web documents with a peer group. By sharing a document with her peers, a user recommends the content for others and annotates it with a short description text. This short description yield many chances for text summarization and categorization. Because today’s social networking platforms are real-time media, the sharing behaviour is subject to many temporal effects, i.e., current events, breaking news, and trending topics. In this paper, we focus on time-dependent hashtag usage of the Twitter community to annotate shared Web-text documents. We introduce a framework for time-dependent hashtag recommendation models and introduce two content-based models. Finally, we evaluate the introduced models with respect to recommendation quality based on a Twitter-dataset consisting of links to Web documents that were aligned with hashtags.
Bootstrapping Wikipedia to Answer Ambiguous Person Name Queries. Gruetze, Toni; Kasneci, Gjergji; Zuo, Zhe; Naumann, Felix (2014).