Bio-entity Relationship Extraction via NLP and Graph Algorithms
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Solution Overview
Problem
Current methods for extracting bio-entity relationships from literature are inefficient, error-prone, and costly due to reliance on manual annotation, with existing automated techniques achieving only partial success on small datasets and suffering from high false positive rates and low coverage.
Innovation Solution
A computer-implemented software application using natural language processing and graph theoretic algorithms to extract textual relationships by building a decision support tool with multiple levels of decision nodes, assigning probability values to patterns, and simplifying patterns with wildcards and synonyms to classify triplets as true or false.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to extract bio-entity relationships, then accuracy can be maintained, but time consumption and costs increase significantly
Solution Approach 1:
The patent introduces computational methods as an intermediary between raw literature and final extracted relationships. The system uses natural language processing, pattern matching, and machine learning algorithms to process text automatically, serving as a mediator that reduces the burden of manual annotation while maintaining acceptable accuracy through multiple processing stages and human-in-the-loop verification.
Solution Approach 2:
The patent applies preliminary action by pre-processing literature texts through tokenization, part-of-speech tagging, and pattern identification before extraction. Training data is pre-annotated to create templates and rules that guide subsequent automated extraction, reducing the need for real-time manual intervention during the actual extraction process.
2Productivity
If simple co-occurrence rules are used for PPI detection, then processing speed increases, but false positive rate becomes unacceptably high
Solution Approach 1:
The patent segments the extraction process into multiple independent stages: co-occurrence detection, pattern matching against predefined templates, grammatical relationship analysis, and machine learning classification. Each stage filters results independently, allowing the system to maintain high processing speed while progressively reducing false positives through layered validation.
Solution Approach 2:
The patent applies partial action by using co-occurrence rules only for initial candidate identification, then applying more rigorous pattern matching and grammatical analysis only to those candidates. This selective application of processing intensity maintains speed for obvious cases while ensuring accuracy for ambiguous cases.
3Reliability
If manually specified rules are used for extraction, then false positive rate decreases, but coverage of detectable relationships becomes limited
Solution Approach 1:
The patent merges multiple extraction approaches into a unified system that combines manually specified rules, computationally learned patterns, and machine learning classifiers. This integration allows the system to leverage the precision of manual rules for well-defined cases while using learned patterns and ML methods to capture diverse relationship types, achieving both high precision and broad coverage simultaneously.
Solution Approach 2:
The patent creates a universal extraction framework that can handle multiple types of bio-entity relationships through a single system. The pattern templates and machine learning models are designed to be domain-agnostic, allowing the same system to extract protein-protein interactions, gene-disease associations, and other biological relationships with consistent performance across different relationship types.
4Productivity
If computational methods are applied to large datasets, then processing efficiency improves, but accuracy decreases due to complexity
Solution Approach 1:
The patent adds dimensional layers to the extraction process by incorporating grammatical relationship analysis, dependency parsing, and contextual features beyond simple word co-occurrence. These additional dimensions provide richer information for distinguishing true relationships from false positives, maintaining accuracy even when processing large datasets through multi-dimensional feature evaluation.
Data Source
AI summary
Automated, standardized and accurate extraction of relationships within text. Automatic extraction of such relationships/information allows the information to be stored in structured form so that it can be easily and accurately retrieved when needed. Such information can be used to build online search engines for highly specific and accurate information retrieval. Generally, according to the current invention, extracting such information (i.e., relationships within text) from raw text can be accomplished using natural language processing (NLP) and graph theoretic algorithm. Examples of such textual relationships include, but are not limited to, biological relationships between biological terms such as proteins, genes, pathways, diseases and drugs. The current methodology is also able to recognize negative dependences in context, match patterns, and provide a shortest path between related words.