NLINQ: A natural language interface for querying network performance

NLINQ: A natural language interface for querying network performance

연구 분야: Databases

학회: Applied Intelligence

Artificial Intelligence is finding increased applications in communication networks. In particular, the field of text-to-Structured Query Language (SQL) translation has great potential to improve customer experience by allowing the querying of network performance databases using natural language. Such adoption, however, is challenging, in general. On one hand, live production systems may have databases with non-semantic table and column names, which makes natural language parsing and text-to-SQL translation difficult. On the other hand, noisy input texts may lead to the generation of incorrect queries. Moreover, inaccurate transcription of speech input into text may further aggravate the problem. Motivated by these aspects, we investigate the problem of natural language-based querying of network performance databases used by Wireless Mesh Networks (WMNs). In particular, we fine-tune a state-of-the-art model to translate natural language questions into appropriate SQL queries. In order to mitigate the problem of non-semantic names, we generate database views with semantic column names, based on the existing tables. In addition, we make domain-specific corrections in the text in order to help generate accurate queries. We also design the Natural Language Interface for Network Query (NLINQ) prototype for a real-life industrial WMN solution. The results of the performance evaluation indicate that natural language text can be translated into SQL queries with an accuracy of 89.021–92.663%, on average. Moreover, the average turnaround time of NLINQ ranges between 1.263–2.013 seconds. The results indicate that NLINQ is suitable for real-time, interactive querying of network performance databases.