Multilingual Question Answering Over Linked Data Building on a Model of the Lexicon-Ontology Interface

Multilingual question answering (QA) over linked data can make structured knowledge accessible across languages. However, most state-of-the-art QA systems are limited to English and rely on machine learning methods that lack controllability and extensibility, making their governance and incremental improvement challenging, along with the effort needed to collect and provide training data. To address this, we follow a model-based approach to QA that builds on a declarative model of the lexicon–ontology interface, OntoLex-Lemon , which enables the specification of the meaning of lexical entries with respect to the vocabulary of a particular dataset. From such a lexicon, in our approach, a QA grammar is automatically generated that can be used to parse questions into SPARQL queries. We demonstrate the extensibility of the approach to different languages by adapting it to German, Italian, and Spanish. We evaluate the approach with respect to the QALD benchmarks and show that our approach outperforms the state-of-the-art on all these datasets in an incremental evaluation mode in which additional lexical entries for test data are added. For example, on QALD-9, our approach obtains F 1 scores of 0.85 (English), 0.82 (German), 0.65 (Italian), and 0.83 (Spanish) in an incremental evaluation mode in which lexical entries covering test data questions are added. So far there is no system described in the literature that works for at least four languages while reaching state-of-the-art performance on all of them. Finally, we demonstrate the low efforts necessary to port the system to a new dataset and vocabulary.