Building Acronym Tag Mapping Using Context-Aware Seq2Seq Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for mapping building points, such as sensors and controllers, struggle with many-to-many relationships between acronyms and tags, leading to poor performance in translating user-created acronyms into standard names.
Innovation Solution
A sequence-to-sequence neural network, specifically a long-short term memory (LSTM) model, is trained on acronym strings and tag strings to generate accurate tag translations, handling many-to-many mappings by considering contextual information within the acronym strings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a dictionary based mapping method is used, then the mapping process is simple and fast, but it fails to handle many-to-many mapping relationships between acronyms and tags
Solution Approach 1:
The patent replaces the mechanical dictionary lookup system with a neural network-based semantic understanding system. The neural network analyzes the contextual meaning of acronyms in sentences and maps them to appropriate tags, enabling accurate handling of many-to-many relationships where context determines the correct mapping.
Solution Approach 2:
The patent changes the mapping approach from direct keyword matching to contextual semantic analysis. By considering the surrounding text and usage context of acronyms, the system dynamically determines the appropriate tag mappings, transforming a static dictionary lookup into a dynamic context-aware process.
2Reliability
If contextual information is considered for accurate mapping, then mapping accuracy improves, but system complexity increases
Solution Approach 1:
The patent employs a universal neural network model that can handle multiple mapping scenarios (one-to-one, one-to-many, many-to-one, many-to-many) with a single system. This multi-functional approach eliminates the need for separate handling mechanisms for different mapping types, managing complexity through unification.
Solution Approach 2:
The neural network model trains itself on building data to learn contextual patterns and mapping relationships automatically. This self-training capability reduces the need for manual configuration and complex rule-based systems, allowing the model to adapt to specific building vocabularies and acronym usage patterns autonomously.
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.