Data Narration Using Hierarchical Fact Graph Ranking
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Solution Overview
Problem
Conventional data narration systems fail to effectively understand and process hierarchical relationships among data within a dataset, limiting the scope and effectiveness of generated data narratives.
Innovation Solution
A data narration system that segments datasets, uses a reinforcement learning model to extract facts from both the whole dataset and its segments, generates a graph based on these facts, and ranks them to create a hierarchical narrative.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional data narration systems process data without segmentation, then the system complexity is low, but the ability to understand hierarchical relationships and generate effective narratives is limited
Solution Approach 1:
The patent applies segmentation by dividing the dataset into multiple segments based on hierarchical relationships. The system identifies and separates data elements into different segments that represent different levels of the hierarchy, allowing the narration system to process and understand relationships at multiple granularities. This segmentation enables the system to capture hierarchical structures while managing complexity through organized data division.
2Measurement precision
If the system extracts facts from the entire dataset only, then the processing is simple, but the relevance and targeting of the narrative is reduced
Solution Approach 1:
The system segments the dataset into meaningful groups based on hierarchical relationships and extracts facts from both the full dataset and individual segments. This dual-approach segmentation allows the system to maintain high narrative relevance by capturing both overall patterns and segment-specific details, while managing processing efficiency through structured organization of extraction tasks.
Solution Approach 2:
The patent applies local quality by treating different segments of the dataset with different processing approaches. The system extracts facts specifically from segments that are relevant to particular narrative contexts, rather than uniformly processing all data. This allows the narration to be tailored to specific segments while maintaining overall dataset context, improving relevance without proportionally increasing processing burden.
3Adaptability or versatility
If the system generates a narrative without considering data interrelationships, then the generation process is fast, but the narrative effectiveness and comprehensiveness are limited
Solution Approach 1:
The system performs preliminary action by pre-identifying hierarchical relationships and segmenting the data before narrative generation. The hierarchical structure and segment assignments are established in advance, creating a ready-to-use framework that speeds up the actual narrative generation process. This preliminary organization allows the system to produce comprehensive narratives that leverage interrelationships without incurring excessive processing delays during generation.
Data Source
AI summary
Systems and methods for data narration are provided. One aspect of the systems and methods includes obtaining a dataset including a plurality of data elements, wherein each of the data elements includes a plurality of attributes; clustering the plurality of data elements to obtain a plurality of data segments; extracting a plurality of facts from the dataset based on the plurality of data segments; generating a graph including a plurality of nodes corresponding to the plurality of facts, respectively; computing an ordering of the plurality of facts based on the graph; and generating a description of the dataset based on the ordering of the plurality of facts.