Ontology-Based Summarization for Query-Specific Answers
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Solution Overview
Problem
Existing automatic text summarization systems generate summaries that are too broad or too general, failing to provide specific information relevant to the researcher's needs, leading to inefficiencies in information retrieval and potential loss of critical information.
Innovation Solution
An ontology-based system that identifies relevant information sources and generates user-focused extractive text summarization using user queries, without requiring manual annotation, by constructing an ontology to standardize knowledge and employing natural language processing techniques to extract and summarize relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic text summarization systems generate summaries from large volumes of scientific literature, then information processing efficiency is improved, but the summaries become too broad or general, losing specific information relevant to user needs
Solution Approach 1:
The patent applies local quality by making different parts of the summarization system serve different functions: the ontology provides domain-specific structure and terminology for precise information extraction, while the bi-directional neural network performs user-specific query matching. This localized specialization ensures that summaries are both efficient and specifically relevant to user needs rather than broadly generic.
Solution Approach 2:
The patent introduces an intermediary ontology layer between the raw scientific literature and the user queries. This ontology acts as a mediator that structures domain knowledge and enables precise mapping between user needs and relevant information, preventing loss of specific information while maintaining processing efficiency.
2Measurement precision
If manual annotation is used to create domain-specific summaries, then summary precision and relevance to user needs is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent implements self-service by enabling the system to automatically construct domain-specific ontologies and generate precise summaries without manual annotation. The bi-directional neural network learns from the ontology structure and user queries to automatically produce relevant summaries, eliminating the need for time-consuming manual annotation while maintaining high precision.
Solution Approach 2:
The patent applies preliminary action by pre-constructing ontologies that encode domain knowledge and relationships before the summarization process begins. This preliminary structuring of knowledge enables the system to quickly generate precise summaries automatically, avoiding the need for manual annotation while maintaining high relevance to user needs.
3Device complexity
If traditional summarization methods are used without domain-specific ontologies, then system complexity is reduced, but adaptability to emerging topics with limited documentation deteriorates
Solution Approach 1:
The patent applies dynamics by making the ontology construction and summarization process adaptive rather than static. The bi-directional neural network dynamically adjusts its summarization based on user queries and the ontology structure, enabling the system to adapt to emerging topics and domains without requiring complete re-engineering, thus balancing complexity with versatility.
Solution Approach 2:
The patent implements universality by designing an ontology-based framework that can be applied across multiple domains and emerging topics. The ontology structure provides a universal language and knowledge representation that enables the system to adapt to new domains with limited documentation, making the system versatile without proportionally increasing complexity.
Data Source
AI summary
The present disclosure is directed to systems and methods of providing systems and methods of autonomously generating summary documents based, at least in part, on a plurality of queries provided by a system user. The systems and methods disclosed herein include processor circuitry to identify a plurality of information sources for a specific topic guided by an ontology with specific concepts and relations. The systems and methods disclosed herein also include processor circuitry to generate user-focused extractive text summarization from each of at least some of the plurality of identified information sources using a plurality of queries supplied by the user/researcher.