Document Visualization via Semantic Map Projection
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Solution Overview
Problem
Existing document visualization techniques, such as document classification and clustering, fail to effectively represent semantic relatedness between documents and can arbitrarily place documents with multiple topics, leading to under-representation of concepts scattered across multiple classes or clusters.
Innovation Solution
A method that projects N-dimensional compact representations of documents onto a K-dimensional map, maintaining relative distances and identifying regions associated with concepts, with labels generated for each region to represent main concepts, allowing for semantic information search and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If document classification or clustering is applied to reduce dimensionality, then the number of dimensions is reduced, but semantic relatedness between documents is not clearly represented
Solution Approach 1:
The patent transforms the document representation from high-dimensional term space to a two-dimensional map through a novel projection technique. Instead of using traditional classification or clustering approaches that lose semantic information, the invention creates a visual map where documents are positioned based on their conceptual relationships, maintaining semantic relatedness while reducing dimensionality for visualization purposes.
Solution Approach 2:
The patent introduces an intermediate procedure that bridges the gap between high-dimensional document space and two-dimensional visualization. This intermediary process involves identifying main concepts in documents and using them as mediators to position documents on the map, thereby preserving semantic relationships that would otherwise be lost in direct dimensionality reduction.
2Device complexity
If document classification is applied to reduce dimensions, then computation is simplified, but the choice of cluster or class appears arbitrary when documents include multiple topics
Solution Approach 1:
The patent applies local quality by allowing different regions of the two-dimensional map to represent different concepts or topics. Instead of forcing each document into a single global class, the system enables documents to be associated with multiple local regions based on their various topics, making the classification non-arbitrary and context-dependent.
Solution Approach 2:
By transitioning to a two-dimensional visual map, the patent resolves the arbitrariness of single-class classification. Documents with multiple topics can be positioned in regions that reflect their conceptual relationships, allowing them to be associated with multiple concepts simultaneously rather than being forced into a single arbitrary category.
3Measurement precision
If labels are placed based on local document distribution, then local concepts are well-represented, but concepts scattered across multiple classes are under-represented
Solution Approach 1:
The patent makes the labeling system universal by enabling labels to represent concepts that span multiple document regions on the map. Instead of creating separate labels for each local cluster, the system allows a single label to encompass concepts that appear across different areas, thereby capturing both local and global concept distributions simultaneously.
Solution Approach 2:
The system incorporates feedback by considering the global distribution of concepts when placing labels on the map. The label placement process takes into account how concepts are distributed across the entire document set, ensuring that concepts appearing in multiple regions are appropriately represented rather than being overlooked in favor of locally dominant but globally rare concepts.
Data Source
AI summary
Method and system for visualizing documents. N-dimensional compact representations are obtained for a set of documents. A plurality of documents are then retrieved with the corresponding N-dimensional compact representations. Each of the retrieved documents is associated with at least one concept. Each of the retrieved documents is projected to a point on a K-dimensional map based on its N-dimensional compact representation so that projected document points in the K-dimensional map maintain the relative distances among the retrieved documents in the N-dimensional space. Regions in the K-dimensional map associated with a concept are identified. A label is generated for each concept in each identified region. Then generated labels are rendered on the K-dimensional map in a corresponding region identified.