Andreas Both


Knowledge Graph Question Answering Leaderboard: A Community Resource to Prevent a Replication Crisis
Aleksandr Perevalov | Xi Yan | Liubov Kovriguina | Longquan Jiang | Andreas Both | Ricardo Usbeck
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Data-driven systems need to be evaluated to establish trust in the scientific approach and its applicability. In particular, this is true for Knowledge Graph (KG) Question Answering (QA), where complex data structures are made accessible via natural-language interfaces. Evaluating the capabilities of these systems has been a driver for the community for more than ten years while establishing different KGQA benchmark datasets. However, comparing different approaches is cumbersome. The lack of existing and curated leaderboards leads to a missing global view over the research field and could inject mistrust into the results. In particular, the latest and most-used datasets in the KGQA community, LC-QuAD and QALD, miss providing central and up-to-date points of trust. In this paper, we survey and analyze a wide range of evaluation results with significant coverage of 100 publications and 98 systems from the last decade. We provide a new central and open leaderboard for any KGQA benchmark dataset as a focal point for the community - Our analysis highlights existing problems during the evaluation of KGQA systems. Thus, we will point to possible improvements for future evaluations.


NIF4OGGD - NLP Interchange Format for Open German Governmental Data
Mohamed Sherif | Sandro Coelho | Ricardo Usbeck | Sebastian Hellmann | Jens Lehmann | Martin Brümmer | Andreas Both
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In the last couple of years the amount of structured open government data has increased significantly. Already now, citizens are able to leverage the advantages of open data through increased transparency and better opportunities to take part in governmental decision making processes. Our approach increases the interoperability of existing but distributed open governmental datasets by converting them to the RDF-based NLP Interchange Format (NIF). Furthermore, we integrate the converted data into a geodata store and present a user interface for querying this data via a keyword-based search. The language resource generated in this project is publicly available for download and also via a dedicated SPARQL endpoint.

N³ - A Collection of Datasets for Named Entity Recognition and Disambiguation in the NLP Interchange Format
Michael Röder | Ricardo Usbeck | Sebastian Hellmann | Daniel Gerber | Andreas Both
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Extracting Linked Data following the Semantic Web principle from unstructured sources has become a key challenge for scientific research. Named Entity Recognition and Disambiguation are two basic operations in this extraction process. One step towards the realization of the Semantic Web vision and the development of highly accurate tools is the availability of data for validating the quality of processes for Named Entity Recognition and Disambiguation as well as for algorithm tuning. This article presents three novel, manually curated and annotated corpora (N3). All of them are based on a free license and stored in the NLP Interchange Format to leverage the Linked Data character of our datasets.