Semantic Association Visualization for Life Science Temporal Signals
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems are inadequate in visualizing semantic information and inferring temporal signals indicating salient associations between life science entities, particularly in unstructured and semi-structured data sources, due to limitations in predictive power, entity recognition, and temporal analytics.
Innovation Solution
A method that identifies semantic entities, generates word embeddings, and determines association strengths across time slices, using techniques like Word2vec, AdaGram, and Doc2vec, to detect statistically significant increases in association strengths and track salience over time, enabling visualization of temporal semantic associations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional visualization methods are used for unstructured data, then data can be displayed, but semantic reasoning capability is insufficient and structured information cannot be effectively extracted
Solution Approach 1:
The patent introduces semantic embeddings as an intermediary representation that bridges unstructured text data and structured visualizations. Word2vec-generated embeddings transform raw text into numerical vectors that capture semantic relationships, enabling downstream tasks like association strength calculation and temporal signal detection without requiring complex semantic reasoning at each processing stage
Solution Approach 2:
The patent replaces traditional keyword-based search and manual semantic analysis with automated machine learning models. Word2vec embeddings and association strength algorithms automatically extract structured information from unstructured data, eliminating the need for manual semantic tagging and reducing system complexity while improving information extraction capability
2Reliability
If citation-based methods are used to identify entity associations, then associations can be detected, but predictive power is minimal and long monitoring periods are required
Solution Approach 1:
The patent performs preliminary action by generating semantic embeddings and calculating association strengths continuously as new data arrives, rather than waiting for citation accumulation. This allows the system to detect nascent associations early based on semantic similarity in text corpora, providing predictive signals before formal publications or citations occur
Solution Approach 2:
The patent changes the detection parameter from citation count to semantic association strength based on word embeddings. This parameter transformation enables detection of associations based on contextual similarity in text, which occurs much earlier than citation-based methods and provides stronger predictive power for emerging relationships
3Measurement precision
If generic NLP methods are used for entity recognition, then processing can be performed, but industry-specific entity recognition accuracy is poor
Solution Approach 1:
The patent applies local quality by training Word2vec models on domain-specific corpora rather than using generic pre-trained models. This creates embeddings tailored to life sciences terminology and contexts, improving entity recognition accuracy for industry-specific entities while maintaining the versatility of the overall NLP framework across different applications
4Extent of automation
If simple synopsis responses are generated from single sources, then automation is easy, but semantic depth and synthesis capability are limited
Solution Approach 1:
The patent merges information from multiple disparate sources by calculating association strengths across different corpora and data types. The system integrates structured and unstructured information from multiple sources, synthesizing comprehensive responses that maintain high semantic depth while remaining fully automated through algorithmic processing
Data Source
AI summary
Disclosed systems, methods, and computer readable media can detect an association between semantic entities and generate semantic information between entities. For example, semantic entities and associated semantic collections present in knowledge bases can be identified. A time period can be determined and divided into time slices. For each time slice, word embeddings for the identified semantic entities can be generated; a first semantic association strength between a first semantic entity input and a second semantic entity input can be determined; and a second semantic association strength between the first semantic entity input and semantic entities associated with a semantic collection that is associated with the second semantic entity can be determined. An output can be provided based on the first and second semantic association strengths.