Mattia Atzeni and Maurizio Atzori.
Abstract: We present an unsupervised approach to process natural language questions that cannot be answered by factual question answering nor advanced data querying, requiring ad-hoc code generation instead.
To address this challenging task, our system, AskCO, performs language-to-code translation by interpreting the natural language question and generating a SPARQL query that is run against CodeOntology, a large RDF repository containing millions of triples representing Java code constructs. The SPARQL query will result in a number of candidate Java source code snippets and methods, ranked by AskCO on both syntactic and semantic features, to find the best candidate, that is then executed to get the correct answer. The evaluation of the system is based on a dataset extracted from StackOverflow and experimental results show that our approach is comparable with other state-of-the-art proprietary systems, such as the closed-source WolframAlpha computational knowledge engine.
Keywords: Question Answering over Linked Data; Natural Language Programming; Semantic Parsing; Machine Reading; Language-to-Code