Querying knowledge graphs in natural language

Abstract

Knowledge graphs are a powerful concept for querying large amounts of data. These knowledge graphs are typically enormous and are often not easily accessible to end-users because they require specialized knowledge in query languages such as SPARQL. Moreover, end-users need a deep understanding of the structure of the underlying data models often based on the Resource Description Framework (RDF). This drawback has led to the development of Question-Answering (QA) systems that enable end-users to express their information needs in natural language. While existing systems simplify user access, there is still room for improvement in the accuracy of these systems. In this paper we propose a new QA system for translating natural language questions into SPARQL queries. The key idea is to break up the translation process into 5 smaller, more manageable sub-tasks and use ensemble machine learning methods as well as Tree-LSTM-based neural network models to automatically learn and translate a natural language question into a SPARQL query. The performance of our proposed QA system is empirically evaluated using the two renowned benchmarks-the 7th Question Answering over Linked Data Challenge (QALD-7) and the Large-Scale Complex Question Answering Dataset (LC-QuAD). Experimental results show that our QA system outperforms the state-of-art systems by 15% on the QALD-7 dataset and by 48% on the LC-QuAD dataset, respectively. In addition, we make our source code available.

Leveraging Abstract Meaning Representation for Knowledge Base Question Answering

“…Baselines: We evaluate NSQA against four systems: GAnswer (Zou et al, 2014), QAmp (Vakulenko et al, 2019, WDAqua-core1 (Diefenbach et al, 2020), and a recent approach by (Liang et al, 2021). GAnswer is a graph data-driven approach and is the state-of-the-art on the QALD dataset.…”

“…WDAqua-core1 is knowledge base agnostic approach that, to the best of our knowledge, is the only technique that has been evaluated on both QALD-9 and LC-QuAD 1.0 on different versions of DBpedia. Lastly, Liang et al (Liang et al, 2021) is a recent approach AMR3.0 QALD-9 LC-QuAD 1.0 stack-Transformer 80.00 87.91 84.03 that uses an ensemble of entity and relation linking modules and train a Tree-LSTM model for query ranking.…”

“…Furthermore, NSQA's performance on LC-QuAD 1.0 significantly outperforms QAmp by 11.45 percentage points on F1. Due to difference in evaluation setup in Liang et al (2021), we reevaluated their system on the same setup and metrics as the above systems. Given the test set and the evaluation, (Liang et al, 2021)'s F1 score reduces to 29.2% 5 .…”

“…Due to difference in evaluation setup in Liang et al (2021), we reevaluated their system on the same setup and metrics as the above systems. Given the test set and the evaluation, (Liang et al, 2021)'s F1 score reduces to 29.2% 5 . We exclude this work from our comparison due to lack of standard evaluation.…”

User Context Ontology for Adaptive Mobile-Phone Interfaces

“…Pellet Reasoner is used to validate our ontology model. Previously researchers and ontology engineers use reasoner to validate their work [58]- [60]. It can be used in unification with Jena and OWL APIs.Reasoner were used to verify the rules, relations and constraints to detect the inconsistency among concepts definition and relations in the built ontology.…”