Abstract
<div data-language="eng" data-ev-field="abstract">DBpedia is one of the most resourceful link databases today, and to access information in DBpedia databases, we need to use query syntax (e.g., SPARQL). However, not all users know SPARQL, so we must use a natural language query system to translate the user’s query into the corresponding query syntax. It is costly and time-consuming for the query system to generate query syntax. Therefore, this paper proposes generating query syntax from natural language. Two multi-label learning methods are used for question transformation: Binary Relevance (BR) and Classifier Chains (CC). To predict all the labels that match the query intentions, we use Recurrent Neural Networks (RNNs) to build a multilabel classifier for generating RDF triples. To better consider the relationship between RDF triples, the Binary Relevance is integrated into an ensemble learning approach to propose an Ensemble BR. The experiments perform better than the other research to improve the query accuracy.<br/></div> © 2023, CC BY.
| Original language | American English |
|---|---|
| Journal | Research Square |
| DOIs | |
| State | Published - 2023 |
Keywords
- Classification (of information)
- Learning systems
- Natural language processing systems
- Resource Description Framework (RDF)
- Syntactics
- Binary relevance
- Binary relevances
- Classifier chain
- Classifier chains
- Dbpedia
- LC-QuAD
- Multi-label classifier
- Multi-labels
- QALD
- Question answering systems
- Recurrent neural network
- SPARQL