Automatic Construction of Multiple Classification Dimensions for Managing Approaches in Scientific Papers

2025-05-29

AI Summary

Inefficient querying of scientific methods arises from the lack of multidimensional, structured knowledge management. Method: This paper proposes a four-layer linguistic pattern recognition framework targeting methodological steps to automatically extract procedural workflows. It defines five dimensions—semantic role, functional purpose, input, output, and constraints—to construct tree-structured step representations. A combined similarity measure—integrating tree-based and set-based metrics—is designed, and bottom-up hierarchical clustering generates interpretable “class-trees.” Contribution/Results: The work introduces, for the first time, a linguistic-pattern-driven method identification mechanism and a class-tree-driven multidimensional classification space, enabling semantically interpretable and highly relevant method organization and retrieval. Experiments demonstrate that the proposed five-dimensional class-tree space significantly improves query accuracy and convergence speed, empirically validating the efficacy of multidimensional structuring for scientific method knowledge management.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Natural Language Processing: Information Extraction
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• 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
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Approaches form the foundation for conducting scientific research. Querying approaches from a vast body of scientific papers is extremely time-consuming, and without a well-organized management framework, researchers may face significant challenges in querying and utilizing relevant approaches. Constructing multiple dimensions on approaches and managing them from these dimensions can provide an efficient solution. Firstly, this paper identifies approach patterns using a top-down way, refining the patterns through four distinct linguistic levels: semantic level, discourse level, syntactic level, and lexical level. Approaches in scientific papers are extracted based on approach patterns. Additionally, five dimensions for categorizing approaches are identified using these patterns. This paper proposes using tree structure to represent step and measuring the similarity between different steps with a tree-structure-based similarity measure that focuses on syntactic-level similarities. A collection similarity measure is proposed to compute the similarity between approaches. A bottom-up clustering algorithm is proposed to construct class trees for approach components within each dimension by merging each approach component or class with its most similar approach component or class in each iteration. The class labels generated during the clustering process indicate the common semantics of the step components within the approach components in each class and are used to manage the approaches within the class. The class trees of the five dimensions collectively form a multi-dimensional approach space. The application of approach queries on the multi-dimensional approach space demonstrates that querying within this space ensures strong relevance between user queries and results and rapidly reduces search space through a class-based query mechanism.

Problem

• Automatically classify scientific approaches using multi-dimensional patterns
• Extract and manage approach components via linguistic-level analysis
• Enable efficient approach queries through tree-based similarity clustering

Innovation

• Top-down approach pattern identification via linguistic levels
• Tree-structure-based similarity measure for steps
• Bottom-up clustering for multi-dimensional class trees