Abstract
Question Answering (QA) over Knowledge Graphs (KGs) has gained its momentum thanks to the spread of the Semantic Web. However, despite the abundance of methods proposed in this field, there are still many aspects that need to be fully covered. One of them is the generation of SPARQL queries with modifiers, i.e. queries that are made up not only by triple patterns but also other terms belonging to the SPARQL syntax, such as FILTER, LIMIT, COUNT, ORDER BY. This task results difficult to accomplish in a generic way since the matching with natural language is not straightforward. Few works try to address this complex issue. In this paper, we propose a new approach to handle and to generate queries containing modifiers. Our method is able to generate queries with multiple modifiers, it is easily extendable to cover new modifiers and new languages, and it is independent of the KG structure. Our approach represents an extension of an existing work called QAnswer.
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Siciliani, L., Diefenbach, D., Maret, P., Basile, P., Lops, P. (2019). Handling Modifiers in Question Answering over Knowledge Graphs. In: Alviano, M., Greco, G., Scarcello, F. (eds) AI*IA 2019 – Advances in Artificial Intelligence. AI*IA 2019. Lecture Notes in Computer Science(), vol 11946. Springer, Cham. https://doi.org/10.1007/978-3-030-35166-3_15
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DOI: https://doi.org/10.1007/978-3-030-35166-3_15
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