Contextual Clustering for Hierarchical Taxonomy Generation
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Solution Overview
Problem
Current cluster analysis technologies struggle to automatically organize terms into complex and informative structures in a robust manner, often requiring human intervention for labeling and specifying the number of output clusters.
Innovation Solution
A computerized system and method that automatically cluster entities based on measuring degrees of similarity and relevance, allowing entities to belong to multiple clusters, and generate taxonomies that describe intricate semantic relationships between terms in multiple tiers or categories of semantic hierarchy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If current cluster analysis technology is used to group terms according to similarity measure, then terms can be grouped into clusters, but the technology cannot automatically organize terms into complex and informative structures without human intervention
Solution Approach 1:
The patent segments the organization process into multiple hierarchical levels (tiers), where terms are organized into clusters at different levels of granularity. This allows automatic organization into complex structures by breaking down the overall task into manageable segmentation steps, enabling the system to create multi-level taxonomies without requiring human intervention at each level.
Solution Approach 2:
The patent introduces a hierarchical dimension to traditional flat clustering, organizing terms across multiple tiers rather than single-level groups. This dimensional transformation enables the system to represent complex semantic relationships automatically, moving from simple similarity grouping to multi-dimensional organizational structures that capture intricate meanings.
2Extent of automation
If human users label groups or clusters, then clusters can be identified and named, but human intervention is required which reduces automation and increases time consumption
Solution Approach 1:
The system performs self-service by automatically generating labels for clusters based on the terms within them. The patent employs algorithms that analyze term characteristics, frequencies, and relationships to autonomously create meaningful cluster labels, eliminating the need for human users to manually label groups and significantly reducing time consumption while maintaining high automation levels.
3Device complexity
If terms are organized into simple single-level clusters, then the organization process is simple, but the structure cannot describe intricate semantic relationships between terms
Solution Approach 1:
The patent implements a nested hierarchical structure where clusters at lower levels are contained within clusters at higher levels, similar to nested dolls. This nesting approach allows the system to preserve intricate semantic relationships by organizing terms into nested tiers, where each level provides increasingly specific categorizations while maintaining the hierarchical context that captures semantic connections between terms at different levels.
Data Source
AI summary
A computerized system and method may provide automated clustering procedures where each clustered entity or node may be included in a plurality of clusters (e.g., more than a single cluster). Clustering procedures provided by some embodiments of the invention may involve measuring and/or quantifying degrees of relevance and/or generality for a plurality of entities or nodes. In some embodiments, a clustering procedure may be used, e.g., to generate a hierarchical, multi-tiered taxonomy of such entities. A computerized system comprising a processor, and a memory, may be used for ranking a plurality of nodes; select nodes based on the ranking; cluster selected nodes into intermediate clusters; calculate distances between unselected nodes and intermediate clusters; and cluster unselected nodes and intermediate clusters into final clusters based on the calculated distances. Some embodiments of the invention may allow routing interactions between remotely connected computer systems based on an automatically generated taxonomy.