Unveiling Interesting Insights: Monte Carlo Tree Search for Knowledge Discovery

2025-10-01

AI Summary

To address the growing disparity between rapidly expanding data volumes and lagging knowledge comprehension capabilities, this paper proposes an automated knowledge discovery framework based on Monte Carlo Tree Search (MCTS). It is the first work to adapt MCTS to knowledge discovery, jointly optimizing data transformations and model selection under subjective, goal-directed guidance to efficiently explore high-dimensional, heterogeneous organizational process data and uncover latent relationships. The framework supports flexible integration of domain knowledge and multimodal pattern extraction strategies, ensuring strong scalability. Extensive experiments on both real-world and synthetic datasets demonstrate that the method significantly improves insight generation efficiency, accurately identifies the optimal transformation–model combination for revealing interesting data patterns, and effectively bridges the gap between data acquisition and actionable knowledge derivation.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Search and Optimization: Mixed Discrete/Continuous Search
• Knowledge Representation and Reasoning: Knowledge Acquisition

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Web Mining and Content Analysis: Topic discovery and tracking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Organizations are increasingly focused on leveraging data from their processes to gain insights and drive decision-making. However, converting this data into actionable knowledge remains a difficult and time-consuming task. There is often a gap between the volume of data collected and the ability to process and understand it, which automated knowledge discovery aims to fill. Automated knowledge discovery involves complex open problems, including effectively navigating data, building models to extract implicit relationships, and considering subjective goals and knowledge. In this paper, we introduce a novel method for Automated Insights and Data Exploration (AIDE), that serves as a robust foundation for tackling these challenges through the use of Monte Carlo Tree Search (MCTS). We evaluate AIDE using both real-world and synthetic data, demonstrating its effectiveness in identifying data transformations and models that uncover interesting data patterns. Among its strengths, AIDE's MCTS-based framework offers significant extensibility, allowing for future integration of additional pattern extraction strategies and domain knowledge. This makes AIDE a valuable step towards developing a comprehensive solution for automated knowledge discovery.

Problem

• Automated knowledge discovery from process data remains difficult
• Bridging the gap between data volume and understanding capability
• Navigating data and extracting implicit relationships with subjective goals

Innovation

• Monte Carlo Tree Search for automated knowledge discovery
• Framework enables extensible pattern extraction strategies
• Automated data transformations uncover interesting patterns