18301514. GENERATING A QUESTION ANSWERING SYSTEM FOR FLOWCHARTS simplified abstract (International Business Machines Corporation)
Contents
GENERATING A QUESTION ANSWERING SYSTEM FOR FLOWCHARTS
Organization Name
International Business Machines Corporation
Inventor(s)
Joseph Shtok of Binyamina (IL)
LEONID Karlinsky of Acton MA (US)
Simon Magnus Tannert of Stuttgart (DE)
Jasmina Bogojeska of Adliswil (CH)
Marcelo Gabriel Feighelstein of Zychron Yaakov (IL)
GENERATING A QUESTION ANSWERING SYSTEM FOR FLOWCHARTS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18301514 titled 'GENERATING A QUESTION ANSWERING SYSTEM FOR FLOWCHARTS
The abstract describes a method for generating semantically meaningful question-answer pairs for graph-like charts, such as flowcharts, using a Question Answering (QA) system.
- Generating synthetic dataset of graph-like chart images
- Rendering graph-like chart images from associated graph data
- Generating question-answer pairs for each chart image
- Calculating ground truth annotations for question-answer pairs and chart images
- Training a vision-language architecture on the synthetic dataset to answer questions about the chart images
Potential Applications: - Enhancing question-answering systems for graph-like charts - Improving understanding and analysis of complex data structures - Enhancing educational tools for visual learning
Problems Solved: - Generating meaningful question-answer pairs for graph-like charts - Improving accuracy and efficiency of QA systems for visual data - Enhancing communication and interpretation of complex information
Benefits: - Increased efficiency in analyzing and interpreting graph-like charts - Improved accuracy in answering questions related to visual data - Enhanced educational and training tools for visual learning
Commercial Applications: Title: "Enhancing Visual Data Analysis with Semantically Meaningful QA Pairs" This technology can be used in industries such as education, data analysis, and information technology to improve the understanding and interpretation of complex visual data structures.
Prior Art: Researchers can explore existing literature on question-answering systems for visual data and semantic analysis of graph-like charts to understand the background of this technology.
Frequently Updated Research: Researchers are continuously exploring new methods and algorithms to improve the accuracy and efficiency of question-answering systems for visual data, including graph-like charts.
Questions about Semantically Meaningful QA Pairs: 1. How does this technology improve the accuracy of question-answering systems for visual data? 2. What are the potential applications of this technology in educational settings?
Original Abstract Submitted
Aspects of the disclosure include methods, systems, and computer program products for generating semantically meaningful question-answer pairs for graph-like charts, such as flowcharts. In one example, a method of implementing a Question Answering (QA) system may comprise generating a synthetic dataset of graph-like chart images. The generating may comprise rendering a plurality of graph-like chart images from a plurality of associated graph data, generating a plurality of question-answer pairs for each of the graph-like chart images, and calculating a plurality of ground truth annotations for each of the plurality of question-answer pairs and associated graph-like chart images from the plurality of associated graph data. The method of implementing the QA system may further comprise training a vision-language architecture on the synthetic dataset to answer questions about the graph-like chart images.
