Open AccessMQALD: Evaluating the impact of modifiers in question answering over knowledge graphs
Author(s) -
Lucia Siciliani,
Pierpaolo Basile,
Pasquale Lops,
Giovanni Semeraro
Publication year - 2022
Publication title -
semantic web
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.862
H-Index - 45
eISSN - 2210-4968
pISSN - 1570-0844
DOI - 10.3233/sw-210440
Subject(s) - computer science , question answering , information retrieval , query language , process (computing) , field (mathematics) , knowledge graph , query expansion , aggregate (composite) , natural language processing , natural language , artificial intelligence , programming language , pure mathematics , materials science , mathematics , composite material
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.