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The qanary ecosystem: Getting new insights by composing question answering pipelines

: Diefenbach, D.; Singh, K.; Both, A.; Cherix, D.; Lange, C.; Auer, S.


Cabot, J.:
Web Engineering. 17th International Conference, ICWE 2017 : Rome, Italy, June 5-8, 2017, Proceedings
Cham: Springer International Publishing, 2017 (Lecture Notes in Computer Science 10360)
ISBN: 978-3-319-60130-4 (Print)
ISBN: 978-3-319-60131-1 (Online)
International Conference on Web Engineering (ICWE) <17, 2017, Rome>
Conference Paper
Fraunhofer IAIS ()

The field of Question Answering (QA) is very multidisciplinary as it requires expertise from a large number of areas such as natural language processing (NLP), artificial intelligence, machine learning, information retrieval, speech recognition and semantic technologies. In the past years a large number of QA systems were proposed using approaches from different fields and focusing on particular tasks in the QA process. Unfortunately, most of these systems cannot be easily reused, extended, and results cannot be easily reproduced since the systems are mostly implemented in a monolithic fashion, lack standardized interfaces and are often not open source or available as Web services. To address these issues we developed the knowledge-based Qanary methodology for choreographing QA pipelines distributed over the Web. Qanary employs the qa vocabulary as an exchange format for typical QA components. As a result, QA systems can be built using the Qanary methodology in a simple r, more flexible and standardized way while becoming knowledge-driven instead of being process-oriented. This paper presents the components and services that are integrated using the qa vocabulary and the Qanary methodology within the Qanary ecosystem. Moreover, we show how the Qanary ecosystem can be used to analyse QA processes to detect weaknesses and research gaps. We illustrate this by focusing on the Entity Linking (EL) task w.r.t. textual natural language input, which is a fundamental step in most QA processes. Additionally, we contribute the first EL benchmark for QA, as open source. Our main goal is to show how the research community can use Qanary to gain new insights into QA processes.