QAmp uses unsupervised message passing over knowledge graphs to answer complex questions, achieving higher recall than a prior SPARQL-based baseline on LC-QuAD while showing that question interpretation, not graph reasoning, is the main source of errors.
Towards a Question Answering System over the Semantic Web
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abstract
Thanks to the development of the Semantic Web, a lot of new structured data has become available on the Web in the form of knowledge bases (KBs). Making this valuable data accessible and usable for end-users is one of the main goals of Question Answering (QA) over KBs. Most current QA systems query one KB, in one language (namely English). The existing approaches are not designed to be easily adaptable to new KBs and languages. We first introduce a new approach for translating natural language questions to SPARQL queries. It is able to query several KBs simultaneously, in different languages, and can easily be ported to other KBs and languages. In our evaluation, the impact of our approach is proven using 5 different well-known and large KBs: Wikidata, DBpedia, MusicBrainz, DBLP and Freebase as well as 5 different languages namely English, German, French, Italian and Spanish. Second, we show how we integrated our approach, to make it easily accessible by the research community and by end-users. To summarize, we provided a conceptional solution for multilingual, KB-agnostic Question Answering over the Semantic Web. The provided first approximation validates this concept.
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2019 1verdicts
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Message Passing for Complex Question Answering over Knowledge Graphs
QAmp uses unsupervised message passing over knowledge graphs to answer complex questions, achieving higher recall than a prior SPARQL-based baseline on LC-QuAD while showing that question interpretation, not graph reasoning, is the main source of errors.