Semantic Parsing Natural Language into SPARQL: Improving Target Language Representation with Neural Attention

Abstract

Semantic parsing is the process of mapping a natural language sentence into a formal representation of its meaning. In this work we use the neural network approach to transform natural language sentence into a query to an ontology database in the SPARQL language. This method does not rely on handcraft-rules, high-quality lexicons, manually-built templates or other handmade complex structures. Our approach is based on vector space model and neural networks. The proposed model is based in two learning steps. The first step generates a vector representation for the sentence in natural language and SPARQL query. The second step uses this vector representation as input to a neural network (LSTM with attention mechanism) to generate a model able to encode natural language and decode SPARQL.

References in corpus (3)

• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars
• Language to Logical Form with Neural Attention

Cited by in corpus (4)

• Neural Machine Translating from Natural Language to SPARQL
• Towards Natural Language Question Answering over Earth Observation Linked Data using Attention-based Neural Machine Translation
• Reducing the impact of out of vocabulary words in the translation of natural language questions into SPARQL queries
• Question Answering over Knowledge Graphs with Neural Machine Translation and Entity Linking