Research

Coordinator: Gerhard Weikum

The proliferation of knowledge-sharing communities such as Wikipedia and the progress in scalable information extraction from Web and text sources has enabled the automatic construction of very large knowledge bases. Recent endeavors of this kind include academic research projects such as DBpedia, KnowItAll, ReadTheWeb, and our YAGO-NAGA project, as well as industrial ones such as Freebase and Trueknowledge. These projects provide automatically constructed knowledge bases of facts about named entities, their semantic classes, and their mutual relationships. Such world knowledge in turn enables cognitive applications and knowledge-centric services like disambiguating natural-language text, deep question answering, and semantic search for entities and relations in Web and enterprise data. Highlights of our ongoing work include the YAGO2 knowledge base, the AIDA tool for named entity disambiguation, and the RDF search engine RDF-3X. This research is supported by a Google Focused Research Award.

Coordinator: Klaus Berberich

We focus on developing efficient and effective methods to search and analyze natural language texts that come with associated temporal information. This includes temporal expressions, which convey time periods a text refers to, as well as publication timestamps, which indicate when a text was published. Data of interest to us includes web archives, newspaper corpora, and other collections of born-digital or now-digital documents. Search, as one direction, targets situations when the user's information need is precise and can be satisfied by some piece of our data, typically a document. Analytics, as another direction, targets situations when the user's information need is vague or can be satisfied with some derived data, typically aggregated statistics. Implementing our methods in systems and experimentally evaluating them on real-world data is integral to our approach. Our recent and ongoing efforts include time-travel text search, algorithms to compute n-gram statistics at large scale, as well as redundancy-aware retrieval models.

Coordinator: Simon Razniewski

Structured representations of encyclopedic and common-sense world knowledge are essential for applications such as search, question answering or dialogue, and especially important as interpretable and reusable components across tasks. We aim to construct knowledge bases that go beyond traditional entity-relationship assertions by adding information about expected numbers of objects, and by information about knowledge base completeness. Where traditional approaches to KB construction usually focus on correctness, we investigate methods towards measuring the extent to which a KB contains all facts on a selected topic, using techniques ranging from crowdsourcing to rule mining and text extraction. Such completeness information can then help both to target KB construction efforts, and to gain confidence about the truth of statements currently not contained in knowledge bases.

Coordinator: Andrew Yates

In contrast to authoritative information sources, like encyclopedias, news articles, and academic papers, much of the information available on the Web is contained in informal text that requires different strategies to interpret. Our group aims to develop methods for searching, mining, and learning with such text so that it may be integrated with other knowledge. This goal spans both information retrieval and natural language processing tasks, such as mining health-related claims from social media, extracting information from dialogue, and learning to identify relevant spans of text. On the information retrieval side, we are particularly interested in leveraging recent advances in deep learning to develop more powerful retrieval models and to learn fine-grained types of relevance, including task-specific and passage-level relevance.

Coordinator: Rishiraj Saha Roy

Research on question answering (QA) aims to provide direct answers to natural language utterances over curated knowledge graphs, structured databases, unstructured Web text, or a combination of the above. In our group, we have tried to push the state-of-the-art in QA along multiple dimensions. The key driving criteria have been handling diversity in question formulations, complexity in information needs, and providing unsupervised, interpretable, and robust solutions that are not constrained to specific settings and benchmarks. Specific contributions include a method for automated template generation for QA, a continuous learning framework that extends these learnt templates with a layer of semantic similarity and user feedback, using predicted answer types for improving efficiency for complex questions, a resuable module that enables any QA system to answer temporal questions, and a graph-based method to answer complex questions by joining evidence from multiple documents on-the-fly. Current focus areas include bridging the structured and unstructured paradigms using powerful graph algorithms, and handling multi-turn question answering for information-seeking conversations using smart methods for context resolution. Detailed information on this research group can be found here.