Minimal Dictionary Language for Closed-Domain Data Retrieval
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Solution Overview
Problem
Existing methods fail to provide efficient and accurate access to data in closed-domain data sets, which are characterized by specialized vocabularies, hindering rapid and precise data retrieval.
Innovation Solution
The development of a minimal dictionary language (MDL) system that allows for the creation of domain-specific grammar templates and applications, enabling users to access closed-domain data sets through an MDL application editor, which provides customized vocabulary and grammar for efficient data retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a minimal dictionary language (MDL) system is developed to provide efficient and accurate access to closed-domain data sets, then data retrieval accuracy and speed are improved, but system complexity and development effort increase
Solution Approach 1:
The system segments the complex task of data retrieval into distinct components: the MDL application editor for creating applications, the vocabulary definition module for specifying domain terms, the grammar template engine for structuring queries, and the data access module for executing queries. Each component handles a specific aspect of the retrieval process, improving accuracy while making the overall system complexity manageable through modular design.
Solution Approach 2:
The patent introduces an intermediary layer (the MDL system with its grammar templates and vocabulary mappings) between the user's natural language queries and the closed-domain data set. This intermediary translates and structures queries according to domain-specific grammars, significantly improving retrieval accuracy without requiring the end user to directly handle the complexity of the underlying data access mechanisms.
2Loss of information
If domain-specific vocabulary and grammar templates are created for closed-domain data sets, then information loss is reduced, but the time and resources required for system setup increase
Solution Approach 1:
The system performs preliminary actions by pre-defining vocabulary terms, their meanings, and grammar templates specific to the closed-domain data set before actual data retrieval operations. The MDL application editor allows users to specify the domain vocabulary and grammar structures in advance, so that when queries are executed, the system already has the frameworks in place to accurately interpret and process them, reducing information loss without requiring setup at the moment of query execution.
3Productivity
If automated methods are implemented for accessing closed-domain data sets, then productivity is improved, but ease of operation decreases due to specialized vocabulary requirements
Solution Approach 1:
The MDL application editor enables users to create their own customized applications and query systems for accessing closed-domain data sets. Users can define their own vocabulary and grammar templates tailored to their specific needs, allowing them to self-configure the system to match their domain knowledge. This self-service capability improves productivity by enabling automated access while maintaining ease of operation through user-friendly customization interfaces.
Data Source
AI summary
How to correctly understand natural language query and catch the useful information from it is a long-standing research question in the chatbot field. However, in many cases only a small set of words are sufficient for closed-domain tasks. In this paper, we propose Minimal Dictionary Language (MDL): a simple language architecture which allows users to express their needs through certain basic syntax. For the pilot study, we selected Enron email open dataset[2] for our experimental use and apply MDL as the language for email retrieval task. We also built a front-end search interface by Python Flask and Vue.js for test and evaluation purposes.