Taxonomy Tree Classification Using Link Prediction and Beam Search
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Solution Overview
Problem
Material classification in large corporations with numerous engineering departments is time-consuming and prone to errors due to the complexity of aligning purchasing requests for similar materials across different departments, leading to inefficiencies and increased costs.
Innovation Solution
A computer-implemented method using a link prediction model with a taxonomy tree structure, incorporating tree-weighted soft labels, negative sampling, and beam search to reduce computational effort and improve classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification methods are used to assign materials to taxonomy classes, then classification accuracy can be maintained through expert knowledge, but the classification process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical classification processes with an automated machine learning system. The link prediction model automatically assigns materials to taxonomy classes by learning from training data, substituting human expert manual classification with an automated computational system that maintains accuracy while dramatically reducing time consumption.
Solution Approach 2:
The patent implements preliminary action by pre-training the link prediction model on a comprehensive dataset of materials and taxonomy classes before actual classification tasks. This pre-training phase allows the system to learn patterns and relationships in advance, enabling rapid and accurate classification during operational use without requiring manual intervention for each new material.
2Measurement precision
If a detailed taxonomy tree with multiple hierarchical levels is implemented to improve material classification precision, then the complexity of the classification system increases, making it more difficult to manage and compute
Solution Approach 1:
The patent replaces complex manual taxonomy management with an automated link prediction model that handles the complexity of multi-level hierarchical taxonomies. The model automatically navigates the taxonomy structure, learns relationships between materials and classes at different hierarchical levels, and performs classification without requiring manual system management, thereby managing complexity through automation rather than simplification.
3Ease of manufacture
If traditional machine learning models are used for material classification, then implementation is straightforward, but computational efficiency decreases when handling large-scale taxonomy trees with many classes
Solution Approach 1:
The patent applies segmentation by dividing the classification task into distinct components: embedding generation for materials and taxonomy classes, link prediction modeling, and hierarchical navigation. This segmentation allows each component to be optimized independently, improving overall computational efficiency while maintaining implementation feasibility through modular design.
Solution Approach 2:
The patent implements parameter changes by transforming the classification problem into a link prediction framework with specific parameter optimizations including embedding dimensions, loss function parameters, and beam search width. These parameter adjustments enable the system to efficiently handle large-scale taxonomy trees while maintaining accuracy, improving computational productivity without sacrificing implementation ease.
Data Source
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AI summary
Computer implemented method for classification of an object having a certain set of properties into a class containing objects with at least one common property, said classes being arranged in a taxonomy tree being denoted by a class label and said classification being performed by using a trained link prediction model which provides, for a textual description of an object, a prediction of a class label of said object, wherein for the link prediction model the textual description of an object and the taxonomy tree (T ) is used as input and wherein the taxonomy tree comprises a set of nodes and a set of directed edges leading from a root node across nodes in at least one intermediate level, said nodes representing intermediate class labels to leaf nodes at a final level, said leaf nodes not having child nodes and denoting a class label and said taxonomy tree containing a structure information defined by a branching ratio at each node and a number of levels, wherein for the application of the link prediction model, structure information is used so that the number of computations to obtain a class label for a given textual descriptions reduced by pursuing only selected paths from the root node to leaf nodes.