Entity Relationship Extraction from Q&A Webpages
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Solution Overview
Problem
Current technologies face challenges in accurately extracting and determining relationships between entities from question and answer websites, as they struggle to distinguish relevant information from advertisements, headers, and other text, leading to inaccuracies in identifying relationship types and entities.
Innovation Solution
A system is developed that identifies question and answer text on Q&A websites, determines relationship types by comparing text to templates, and aggregates scores to establish the most likely valid relationships between entities, using a combination of machine-learned classifiers and natural language processing techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If text from Q&A webpages is extracted to determine entity relationships, then relationship information can be obtained, but the text may include advertisements, headers, and other irrelevant content that reduces accuracy
Solution Approach 1:
The webpage text is divided into multiple segments including question text, answer text, advertisements, headers, and other elements. The system selectively processes only the question and answer segments while excluding irrelevant segments, thereby improving extraction accuracy without requiring complex processing of entire webpage content.
Solution Approach 2:
The system extracts and isolates the relevant question and answer text from the webpage, separating it from advertisements, headers, and other irrelevant content. This extraction focuses computational resources on the essential information needed for relationship determination while discarding distracting elements.
2Adaptability or versatility
If multiple candidate relationships are generated from Q&A webpages, then more relationship options are available, but determining the most accurate relationship becomes more difficult
Solution Approach 1:
The system generates multiple candidate relationships and then uses feedback mechanisms to evaluate and rank them. By analyzing the frequency of relationships across multiple Q&A webpages and comparing candidates against established patterns, the system iteratively refines its selection to identify the most accurate relationship while maintaining versatility in considering multiple possibilities.
3Reliability
If relationship scores are aggregated from multiple Q&A webpages, then relationship reliability improves, but processing time increases
Solution Approach 1:
The system performs preliminary processing of Q&A webpages to identify and extract potential relationships before full aggregation. By pre-processing and filtering content in advance, the system reduces the computational burden during the aggregation phase, allowing reliable relationship determination across multiple sources without excessive processing time.
Data Source
AI summary
Methods, systems, and apparatus for obtaining a resource, identifying a first portion of text of the resource that is characterized as a question, and a second part of text of the resource that is characterized as an answer to the question, identifying an entity that is referenced by one or more terms of the text that is characterized as the question, a relationship type that is referenced by one or more other terms of the text that is characterized as the question, and an entity that is referenced by the text that is characterized as the answer to the question, and adjusting a score for a relationship of the relationship type for the entity that is referenced by the one or more terms of the text that is characterized as the question and the entity that is referenced by the text that is characterized as the answer to the question.