We all turn towards Wikipedia with questions we want to know more about, but eventually find ourselves on the limit of its coverage. Instead of providing "ten blue links" as common in Web search, why not answer any web query with something that looks and feels like Wikipedia? This talk is about algorithms that automatically retrieve and identify relevant entities, relevant aspects of entities, and relevant relations. In combiantion, this does not only facilitate a semantic machine-understanding of text and entities, but enables predictive models of what is relevant information a user is looking for. While this contributes to many challenging applications, we detail these algorithms in the context of the TREC Complex Answer Retrieval shared task (http://trec-car.cs.unh.edu).
Laura Dietz is an Assistant Professor at the University of New Hampshire, where she teaches "Information Retrieval" and "Data Science for Knowledge Graphs and Text". She coordinates the TREC Complex Answer Retrieval Track and runs a tutorial/workshop series on Utilizing Knowledge Graphs in Text-centric Retrieval. Previously, she was a research scientist in the Data and Web Science group at Mannheim University, and a research scientist with Bruce Croft and Andrew McCallum at the Center for Intelligent Information Retrieval (CIIR) at UMass Amherst. She obtained her doctoral degree with a thesis on topic models for networked data from Max Planck Institute for Informatics, supervised by Tobias Scheffer and Gerhard Weikum.