Schema-Inferred Parse Tree Compression for Semantic Payload Reduction
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Solution Overview
Problem
Existing compression techniques obscure semantic content, leading to decreased accuracy and increased latency in machine learning models due to structural dissimilarity and redundancy in webpages, which are not effectively handled by current methods.
Innovation Solution
A novel schema inference method that merges parse trees to generate a global schema tree, removing redundant information while preserving semantic content, suitable for natural language processing and machine learning applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If generic compression (e.g., zipping) is applied to structured data formats like JSON, then data volume is significantly reduced (compression ratio of 50-95%), but semantic content becomes obscured and unavailable for inspection and analysis
Solution Approach 1:
The patent extracts and removes redundant syntactic structures (whitespace, punctuation, structural delimiters) from structured data formats while preserving the underlying semantic content. This extraction process condenses the data by eliminating non-essential elements that do not contribute to the meaning or functionality of the data, thereby reducing data volume without obscuring semantics.
Solution Approach 2:
The patent applies different treatment to different parts of the structured data: semantic content is preserved in its essential form while syntactic artifacts are removed. This local differentiation allows the system to maintain high compression ratios for redundant structural elements while preserving the integrity and inspectability of meaningful content for downstream analysis.
2Loss of information
If structured data formats (e.g., JSON, XML, HTML) are used to ensure clarity and organization, then semantic content is preserved, but data volume increases due to repetitive syntactic artifacts
Solution Approach 1:
The patent systematically extracts and removes repetitive syntactic artifacts such as whitespace, punctuation, and structural delimiters from structured data formats. This extraction eliminates redundant information that increases data volume without contributing to semantic meaning, thereby condensing the data while maintaining clarity and organization of the underlying content.
Solution Approach 2:
The patent discards redundant syntactic structures that are necessary for format compliance but do not contribute to semantic content. By removing these artifacts, the system recovers storage space and reduces data volume while the essential semantic structure remains intact and recoverable for analysis purposes.
3Adaptability or versatility
If individual webpages are processed with different structures, then each page maintains its unique format and semantics, but pattern recognition and heuristic analytics are interfered with due to structural dissimilarity
Solution Approach 1:
The patent applies a standardized processing approach that removes format-specific syntactic artifacts and extracts semantic content in a uniform manner across different webpage structures. This homogenization of the output format enables consistent pattern recognition and heuristic analytics while the underlying semantic diversity of individual pages is preserved through careful extraction of meaningful content.
Data Source
AI summary
Here is message compression using schema inference for condensing semantic content by removal of syntactic structure in multiple kinds of content. Topological structures of different trees are generalized to generate a merged tree. Because compression discards redundant content and often only semantic content is retained, the signal-to-noise ratio is increased, which increases accuracy of downstream semantic analytics such as machine learning. Compression based on the merged tree removes redundant information from new messages that, without obscuring semantic content, decreases the data volume for downstream analytics or archiving. This compression extracts semantic values that can be assembled into a sequence of lexical tokens that is suitable for natural language processing (NLP), and the sequence of lexical tokens does not contain tokens that represent syntax or structure. Thus, compression provides fewer tokens to be processed by a downstream language model, which is suitable for efficient processing of a live data stream.