Ontology-Based API Query Recommendation System

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Solution Overview

Problem

Current decision-making systems face challenges in recommending effective queries to locate application programming interfaces (APIs) due to the complexity of unstructured data and the lack of efficient methods for identifying semantically relevant terms within these APIs.

Innovation Solution

A system comprising a processor and memory that generates an ontology from unstructured API data and uses a reasoner component to identify semantically corresponding terms, employing algorithms like Path Ranking to derive hidden relationships and transform queries for improved recommendation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If unstructured data of API descriptions is used directly for query recommendation, then the system can handle diverse API formats, but the precision of semantic term identification deteriorates

Engineering Contradiction:

Improveability to handle diverse API formatsVSAvoidprecision of semantic term identification

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The system performs preliminary action by generating an ontology from unstructured API data before query recommendation. The ontology component creates a structured knowledge representation that organizes API concepts, relationships, and semantics in advance. This pre-processed ontology serves as a foundation for accurate semantic term identification during query recommendation, resolving the contradiction between handling diverse formats and maintaining precision.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The ontology acts as an intermediary between unstructured API data and query recommendation processes. Instead of directly processing unstructured data during queries, the system uses the pre-generated ontology as a mediator that captures semantic relationships and enables precise term identification. This intermediary structure preserves adaptability to diverse API formats while improving measurement precision in semantic term identification.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If traditional keyword matching is used for query recommendation, then the system operates quickly with simple algorithms, but the accuracy of locating relevant APIs deteriorates

Engineering Contradiction:

Improvespeed of query processingVSAvoidaccuracy of API location

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system replaces traditional mechanical keyword matching with a semantic reasoning approach based on ontologies. Instead of simple string comparison, the reasoner component uses semantic relationships and contextual understanding derived from the ontology to identify relevant terms. This substitution maintains productivity through efficient ontology-based reasoning while significantly improving the accuracy of API location through semantic understanding.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Loss of information

If comprehensive ontology generation from all unstructured API data is performed, then the semantic coverage is improved, but the system complexity and processing time increase

Engineering Contradiction:

Improvesemantic coverage of API conceptsVSAvoidcomplexity of ontology generation process

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The ontology generation process is segmented into distinct components and stages. The ontology component breaks down unstructured API data into structured elements representing concepts, relationships, and attributes. This segmentation allows the system to manage complexity by processing different aspects of API data separately while maintaining comprehensive semantic coverage through the organized ontology structure.

Inventive Principle:

Principle #1Segmentation

4Measurement precision

If semantic reasoning algorithms are applied to identify corresponding terms, then the relevance of query results is improved, but the computational resources and processing time increase

Engineering Contradiction:

Improverelevance of query resultsVSAvoidcomputational resources consumed

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

Semantic reasoning is performed in advance during ontology generation rather than during each query processing operation. The reasoner component pre-computes semantic relationships and term correspondences based on the ontology structure, storing these results for efficient retrieval during query recommendation. This preliminary action reduces computational resource consumption during actual queries while maintaining high relevance of results through pre-established semantic understanding.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11500914B2Query recommendation to locate an application programming interface

Publication Date: 2022.11.15 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Systems, computer-implemented methods, and computer program products to facilitate query recommendation are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an ontology component that can generate an ontology based on unstructured data of a description of an application programming interface. The computer executable components can further comprise a reasoner component that can identify one or more terms of the ontology that correspond semantically to a term of a query.