Bio-SODA: Enabling Natural Language Question Answering over Knowledge Graphs without Training Data
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by
Ana Claudia Sima, Tarcisio Mendes de Farias, Maria Anisimova, Christophe Dessimoz, Marc Robinson-Rechavi, Erich Zbinden, Kurt Stockinger
2021
Abstract
The problem of natural language processing over structured data has become a
growing research field, both within the relational database and the Semantic
Web community, with significant efforts involved in question answering over
knowledge graphs (KGQA). However, many of these approaches are either
specifically targeted at open-domain question answering using DBpedia, or
require large training datasets to translate a natural language question to
SPARQL in order to query the knowledge graph. Hence, these approaches often
cannot be applied directly to complex scientific datasets where no prior
training data is available.
In this paper, we focus on the challenges of natural language processing over
knowledge graphs of scientific datasets. In particular, we introduce Bio-SODA,
a natural language processing engine that does not require training data in the
form of question-answer pairs for generating SPARQL queries. Bio-SODA uses a
generic graph-based approach for translating user questions to a ranked list of
SPARQL candidate queries. Furthermore, Bio-SODA uses a novel ranking algorithm
that includes node centrality as a measure of relevance for selecting the best
SPARQL candidate query. Our experiments with real-world datasets across several
scientific domains, including the official bioinformatics Question Answering
over Linked Data (QALD) challenge, show that Bio-SODA outperforms publicly
available KGQA systems by an F1-score of least 20% and by an even higher factor
on more complex bioinformatics datasets.
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