Natural Language Interfaces to Data

Natural language interfaces (NLIs) make it easy for non-technical users to query and interact with data. Recent advances in NLU and processing have led to a renewed interest in NLIs. Challenges include identifying entities, connecting them meaningfully, and generating structured queries. Rule-based and hybrid approaches are used, with conversational interfaces being the next step. This monograph reviews NLI technologies, approaches, and benchmarks, and discusses challenges for widespread adoption.

Format: Paperback / softback
Length: 108 pages
Publication date: 30 May 2022
Publisher: now publishers Inc

Natural language interfaces (NLIs) are a powerful tool that allows users to interact with data in a more intuitive and natural way. By leveraging advanced natural language understanding and processing techniques, NLIs enable non-technical users to explore and analyze data sets without the need for specialized knowledge or technical skills.

One of the key challenges in NLI is identifying the entities involved in the user's utterance and connecting them in a meaningful way to interpret the user's intent. This involves a complex process of entity recognition, relationship extraction, and intent mapping. Rule-based systems, which rely on semantic indices, ontologies, and knowledge graphs, are commonly used to address this challenge. These systems use predefined rules and algorithms to identify entities, understand their relationships, and generate structured queries.

However, rule-based systems have limitations, such as the difficulty in handling complex and ambiguous queries, and the reliance on predefined knowledge. To overcome these limitations, hybrid approaches that combine rule-based techniques with deep learning models have gained popularity. Deep learning models, such as neural networks, can learn from large amounts of data and can adapt to new and complex situations. By combining rule-based systems with deep learning models, NLIs can achieve better accuracy and scalability in handling natural language queries.

Conversational interfaces are the next logical step in NLI development. By leveraging the context of multiple turns of conversation, conversational interfaces can disambiguate ambiguous queries and provide more accurate and natural responses. This approach is particularly useful in scenarios where users engage in natural and conversational dialogue with the NLI.

In this monograph, the authors review the rule-based and hybrid technologies used in NLIs and survey the different approaches to natural language querying. They also describe conversational interfaces for data analytics and discuss several benchmarks used for natural language querying research and evaluation. The monograph concludes with a discussion on the challenges that need to be addressed before these systems can be widely adopted.

Overall, NLIs have the potential to revolutionize the way we interact with data and enable us to make more informed decisions based on the analysis of large and complex data sets. With ongoing advancements in natural language understanding and processing, we can expect to see more innovative and effective NLIs in the future.

Weight: 165g
Dimension: 234 x 156 (mm)
ISBN-13: 9781638280286