Acronym String Mapping With Seq2Seq Contextual Tag Translation
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Solution Overview
Problem
Existing methods for mapping building points, such as sensors and actuators, using acronyms to standard tags face challenges due to many-to-many relationships, which dictionary-based methods struggle to handle effectively.
Innovation Solution
A sequence-to-sequence neural network, specifically a Long-Short Term Memory (LSTM) or Conditional Random Field (CRF) model, is employed to translate acronym strings into tag strings, utilizing contextual information and attention mechanisms to manage complex mappings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If dictionary based mapping method is used, then mapping process is simple, but it fails to handle many to many mapping relationship between acronyms and tags
Solution Approach 1:
The patent replaces the mechanical dictionary-based lookup system with a neural network-based semantic understanding system. The neural network analyzes contextual relationships between acronyms and tags, enabling accurate mapping in many-to-many scenarios where traditional dictionary methods fail.
Solution Approach 2:
The patent changes the mapping approach from direct key-value lookup to probabilistic semantic matching. By using neural network confidence scores and contextual analysis, the system dynamically adjusts mapping parameters based on the complexity and ambiguity of each mapping scenario.
2Reliability
If sequence to sequence neural network is used, then mapping accuracy improves for many to many relationships, but system complexity increases
Solution Approach 1:
The patent implements a universal neural network architecture that handles multiple mapping scenarios (one-to-one, one-to-many, many-to-one, many-to-many) through a single unified model. This eliminates the need for separate mapping systems for different relationship types, managing complexity through architectural universality.
Solution Approach 2:
The patent introduces contextual vectors and attention mechanisms as intermediaries between acronyms and tags. These intermediaries capture semantic relationships and enable accurate mapping without requiring complex rule-based systems, simplifying the overall architecture while maintaining high accuracy.
3Reliability
If contextual information is utilized in mapping, then mapping accuracy for ambiguous acronyms improves, but processing time increases
Solution Approach 1:
The patent performs preliminary contextual analysis by pre-processing input strings to identify potential acronyms and their contexts before the main mapping operation. This preliminary action reduces the computational burden during actual mapping, balancing accuracy with processing efficiency.
Solution Approach 2:
The patent dynamically adjusts the level of contextual analysis based on input characteristics. For unambiguous acronyms, minimal contextual processing is performed, while ambiguous cases receive full contextual analysis. This dynamic approach optimizes processing time while maintaining accuracy where needed.
Data Source
AI summary
A building system including one or more memory devices configured to store instructions that, when executed by one or more processors, cause the one or more processors to receive training data including acronym strings and tag strings, train a sequence to sequence neural network based on the training data, receive an acronym string for labeling, the acronym string comprising a particular plurality of acronyms, and generate a tag string for the acronym string with the sequence to sequence neural network, wherein the sequence to sequence neural network outputs a tag of the tag string for one acronym of the particular plurality of acronyms based on the one acronym and contextual information of the acronym string, wherein the contextual information includes other acronyms of the particular plurality of acronyms.