Sparse Distributed Representation Generation for Semantic Mapping
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Solution Overview
Problem
Conventional systems for data document clustering using self-organizing maps are limited in providing explicit semantic definitions for data items and do not effectively generate cross-lingual sparse distributed representations for semantic mapping and search functionalities.
Innovation Solution
A computer-implemented method that clusters data documents in a two-dimensional metric space, generates sparse distributed representations by determining occurrence information for data items, and reduces the number of set bits based on a maximum sparsity level to create a sparse distributed representation with a normative fillgrade, enabling semantic mapping and search functionalities across languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional self-organizing maps are used for data document clustering, then data documents can be clustered by type, but the system cannot provide explicit semantic definitions for data items or generate cross-lingual sparse distributed representations
Solution Approach 1:
The system segments the clustering process into multiple specialized modules: a reference map generator for clustering data documents, a parser for extracting data items, a representation generator for creating distributed representations, and a sparsifying module for generating sparse distributed representations. Each module handles a specific aspect of the semantic mapping process, enabling comprehensive functionality while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system creates a multi-functional framework that combines document clustering, semantic definition generation, and cross-lingual representation generation within a unified architecture. The reference map generator serves multiple purposes by providing both clustering functionality and the basis for semantic mapping, while the representation generator produces distributed representations that can be used for both semantic definitions and cross-lingual translations.
2Productivity
If distributed representations are generated with high sparsity, then search efficiency improves, but the number of set bits decreases which may reduce semantic information retention
Solution Approach 1:
The sparsifying module dynamically adjusts the sparsity parameter to optimize the balance between search efficiency and information retention. By controlling the maximum number of set bits in the sparse distributed representations, the system can adapt the representation density based on specific application requirements, achieving both efficient search and adequate semantic information preservation.
Solution Approach 2:
The system generates sparse distributed representations that retain only the most significant semantic features rather than attempting to preserve all possible information. This partial representation approach focuses on capturing essential semantic relationships while reducing dimensionality, achieving efficient search without complete information loss through strategic selection of representative features.
Data Source
AI summary
A method of mapping data items to sparse distributed representations (SDRs) includes clustering in a two-dimensional metric space, by a reference map generator, a set of data documents selected according to at least one criterion, generating a semantic map. The semantic map associates a coordinate pair with each of the set of data documents. A parser generates an enumeration of data items occurring in the set of data documents. A representation generator determines, for each data item in the enumeration, occurrence information. The representation generator generates a distributed representation using the occurrence information. A sparsifying module receives an identification of a maximum level of sparsity. The sparsifying module reduces a total number of set bits within the distributed representation based on the maximum level of sparsity to generate an SDR having a normative fillgrade.