Natural Language Interface to Web API via Semantic Mapping
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Solution Overview
Problem
Current Web APIs are complex and require programming expertise to use, limiting user accessibility and scalability, as most users cannot directly interact with them due to their service-oriented architecture and complex nature.
Innovation Solution
A natural language interface (NLI) to Web APIs (NL2API) is developed, allowing users to interact with Web services using natural language queries and commands, which maps natural language inputs into API calls through a framework that utilizes crowd-sourced training data and a hierarchical probabilistic model to generate a scalable and cost-effective interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Web APIs are exposed via service-oriented architecture, then service accessibility and scalability are improved, but user accessibility deteriorates due to programming expertise requirements
Solution Approach 1:
The patent introduces a natural language processing system as an intermediary between users and Web APIs. This intermediary translates natural language queries into API calls, eliminating the need for users to directly interact with complex API interfaces. The NLP system acts as a mediator that bridges the gap between simple user queries and complex service-oriented architecture.
Solution Approach 2:
The patent replaces the mechanical interaction model (programming APIs directly) with a semantic interaction model (natural language processing). Instead of requiring users to write code and understand API syntax, the system uses NLP to interpret semantic meaning and automatically generate appropriate API calls, substituting mechanical programming with intelligent translation.
2Manufacturing precision
If traditional API interface methods are used, then programming precision is maintained, but device complexity increases for users
Solution Approach 1:
The patent extracts the complexity of API interaction from the user interface. By separating the NLP translation layer from the API execution layer, the system removes complex programming requirements from the user-facing interface while maintaining precise API call generation in the backend. This extraction allows simple user interfaces to achieve complex functionality.
Solution Approach 2:
The patent changes the interaction parameters from programming-oriented (syntax, protocols, data formats) to natural language parameters (semantics, intent, context). This parameter transformation maintains the precision needed for accurate API calls while dramatically reducing interface complexity for end users.
3Ease of manufacture
If crowd-sourced training data is used, then cost of training data collection is reduced, but data quality may deteriorate
Solution Approach 1:
The patent implements a self-service quality control mechanism where the system automatically evaluates and filters crowd-sourced training data. The NLP system performs self-validation on the quality and relevance of contributed data, automatically filtering out low-quality entries while maintaining the cost advantages of crowd-sourcing. This self-service approach eliminates the need for expensive manual verification.
Solution Approach 2:
The patent incorporates feedback loops where the system continuously monitors and evaluates the quality of training data from crowd sources. Quality metrics and validation mechanisms provide feedback to both the data contributors and the system, enabling iterative improvement of data quality while maintaining cost-effectiveness through distributed contribution.
Data Source
AI summary
Subject matter involves using natural language to Web application program interfaces (API), which map natural language commands into API calls, or API commands. This mapping enables an average user with little or no programming expertise to access Web services that use API calls using natural language. An API schema is accessed and using a specialized grammar, with the help of application programmers, canonical commands associated with the API calls are generated. A hierarchical probabilistic distribution may be applied to a semantic mesh associated with the canonical commands to identify elements of the commands that require labeling. The identified elements may be sent to annotators, for labeling with NL phrases. Labeled elements may be applied to the semantic mesh and probabilities, or weights updated. Labeled elements may be mapped to the canonical commands with machine learning to generate a natural language to API interface. Other embodiments are described and claimed.