MQALD: Evaluating the impact of modifiers in question answering over knowledge graphs

Semantic web

article

Lucia Siciliani¹  Pierpaolo Basile¹  Pasquale Lops¹  Giovanni Semeraro¹ 

[1] Department of Computer Science, University of Bari Aldo Moro

关键词: Question answering;    knowledge graphs;    dataset;    benchmark;    SPARQL modifiers;   

DOI  :  10.3233/SW-210440

来源: IOS Press

【 摘 要 】

Question Answering (QA) over Knowledge Graphs (KG) aims to develop a system that is capable of answering users’ questions using the information coming from one or multiple Knowledge Graphs, like DBpedia, Wikidata, and so on. Question Answering systems need to translate the user’s question, written using natural language, into a query formulated through a specific data query language that is compliant with the underlying KG. This translation process is already non-trivial when trying to answer simple questions that involve a single triple pattern. It becomes even more troublesome when trying to cope with questions that require modifiers in the final query, i.e., aggregate functions, query forms, and so on. The attention over this last aspect is growing but has never been thoroughly addressed by the existing literature. Starting from the latest advances in this field, we want to further step in this direction. This work aims to provide a publicly available dataset designed for evaluating the performance of a QA system in translating articulated questions into a specific data query language. This dataset has also been used to evaluate three QA systems available at the state of the art.

【 授权许可】

Unknown