Ricardo Usbeck

TL;DR: GERBIL aims to become a focal point for the state of the art, driving the research agenda of the community by presenting comparable objective evaluation results.

TL;DR: This survey analyzes 62 different SQA systems, which are systematically and manually selected using predefined inclusion and exclusion criteria, leading to 72 selected publications out of 1960 candidates, and identifies common challenges, structure solutions, and provide recommendations for future systems.

TL;DR: This work presents AGDISTIS, a novel knowledge-base-agnostic approach for named entity disambiguation that combines the Hypertext-Induced Topic Search (HITS) algorithm with label expansion strategies and string similarity measures and can efficiently detect the correct URIs for a given set of named entities within an input text.

TL;DR: It is suggested that Frankenstein precisely solves the QA optimisation problem but also enables the automatic composition of optimised QA pipelines, which outperform the static Baseline QA pipeline.

TL;DR: The past years have seen a growing amount of research on question answering (QA) over Semantic Web data, shaping an interaction paradigm that allows end users to profit from the expressive power ofSemantic Web standards while, at the same time, hiding their complexity behind an intuitive and easy-to-use interface.