Wednesday 19 March 2025
A new system for questioning knowledge graphs (KGs) has been developed, making it easier for people to interact with complex semantic data. This achievement is significant because KGs are becoming increasingly important in various fields, such as Industry 5.0 and IoT-based industries.
The system begins by constructing a KG using a flexible ETL pipeline that ensures compatibility with the SemIoE ontology. Then, a hybrid SPARQL query generation approach is used to translate natural language questions into accurate and interpretable SPARQL queries. This approach combines the adaptability of large language models (LLMs) with the reliability of predefined templates, mitigating issues such as query hallucination and semantic misalignment.
The system also features a dynamic dashboard that facilitates intuitive and interactive visualization of query results. The dashboard automatically selects the most suitable visualization type for each set of data, ensuring that users can easily explore complex information. For example, if a query returns numerical trends or relationships, the system will generate a plot to help users understand the data.
The system’s performance was evaluated using a carefully designed dataset and well-defined metrics. The results show that the system is highly accurate in generating SPARQL queries and visualizing query results. The accuracy of query generation was measured by calculating the percentage of successfully executed queries, which was found to be high.
In addition, the system’s ability to select the correct visualization type was evaluated using a confusion matrix. This analysis revealed that the system accurately selected the most suitable visualization type in 70% of cases. While there is room for improvement, this result demonstrates the system’s potential to effectively communicate complex information to users.
The development of this system has several implications for various fields. For instance, it can be used to improve data integration and querying capabilities across different domains. This can lead to more accurate decision-making and better insight into complex systems.
Moreover, the system’s ability to generate visualizations based on query results can help users to quickly identify trends and patterns in large datasets. This can be particularly useful in fields such as finance, healthcare, and environmental monitoring, where timely analysis of data is crucial.
In summary, this new system has the potential to revolutionize the way people interact with knowledge graphs by making it easier to ask questions, generate queries, and visualize results. Its high accuracy and ability to adapt to different types of data make it a valuable tool for various fields and applications.
Cite this article: “Enabling Efficient Interaction with Knowledge Graphs”, The Science Archive, 2025.
Knowledge Graphs, Semantic Data, Industry 5.0, Iot-Based Industries, Etl Pipeline, Semioe Ontology, Sparql Queries, Natural Language Processing, Visualization Dashboard, Data Analysis
