Inaugural lecture at Heinrich-Heine-University Düsseldorf on 28 May 2019.
When searching the Web for information, human knowledge and artificial intelligence are in constant interplay. On the one hand, human online interactions such as click streams, crowd-sourced knowledge graphs, semi-structured web markup or distributional semantic models built from billions of Web documents are informing machine learning and information retrieval models, for instance, as part of the Google search engine. On the other hand, the very same search engines help users in finding relevant documents, facts, or data for particular information needs, thereby helping users to gain knowledge. This talk will give an overview of recent work in both of the aforementioned areas. This includes 1) research on mining structured knowledge graphs of factual knowledge, claims and opinions from heterogeneous Web documents as well as 2) recent work in the field of interactive information retrieval, where supervised models are trained to predict the knowledge (gain) of users during Web search sessions in order to personalise rankings. Both streams of research are converging as part of online platforms and applications to facilitate access to data(sets), information and knowledge.