Semantic Network Generation for Unstructured Document Processing
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Solution Overview
Problem
The current online reading experience is inefficient due to the overwhelming, unstructured, and contradictory nature of electronic documents, which challenges human understanding and requires better tools for processing and interacting with information.
Innovation Solution
A method and system that builds a semantic network of personal knowledge from unstructured data, allowing interactive human-machine collaboration to extract and represent knowledge in a structured form using concept-relation-concept triples, enabling efficient information processing and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If unstructured electronic documents are used to store information, then information can be stored in flexible formats, but the information becomes overwhelming and difficult to process
Solution Approach 1:
The patent segments unstructured documents into structured semantic networks by dividing text into sentences, extracting concepts and relations, and organizing them into concept-relation-concept triples. This segmentation transforms the overwhelming unstructured information into manageable, organized knowledge units that are easier to process and navigate.
Solution Approach 2:
The patent introduces semantic networks as an intermediary layer between unstructured documents and human users. This intermediary automatically processes and reorganizes the raw information into structured representations, acting as a mediator that converts difficult-to-process text into easily navigable knowledge graphs without requiring users to manually structure the information.
2Ease of operation
If semantic networks are created from unstructured data, then information becomes structured and manageable, but the process requires complex processing
Solution Approach 1:
The patent implements self-service through automated semantic network generation, where the system independently processes unstructured documents, extracts concepts and relations, and builds semantic networks without requiring manual intervention. The automated extraction and organization processes eliminate the need for users to manually structure information, reducing operational complexity despite the inherent complexity of the processing system.
Solution Approach 2:
The patent changes the structural parameters of information from unstructured text to structured semantic networks by transforming documents into concept-relation-concept triples. This parameter transformation reorganizes information into a different format that is easier to manage and query, making the complexity of the processing system transparent to users while providing ease of operation through the transformed data structure.
3Extent of automation
If machines process unstructured information, then information can be automatically extracted, but the extracted information lacks meaningful structure
Solution Approach 1:
The patent applies preliminary action by performing automated sentence-level processing and concept extraction as a first step, then refining the results by organizing extracted relations into coherent semantic networks. This preliminary automated extraction captures the bulk of information structure automatically, while subsequent organization steps refine the precision of the final structured representation without requiring complete manual processing.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system processes unstructured information, generates semantic networks, and allows users to review and refine the results. This feedback loop enables automated extraction to work iteratively with human input, improving the precision of the extracted structure while maintaining automation. The system learns from user interactions to improve future extractions, balancing automated processing with structural precision.
Data Source
AI summary
A method, system and computer program product for developing a semantic network are disclosed. The system includes a server and a client computer, and the method involves: creating a semantic network containing at least one root concept; performing a set of instructions associated with the semantic network, the set of instructions comprising: transmitting information about the semantic network from the client computer to a server; providing information about at least one resource to the server, the at least one resource containing concepts and relations associated with the at least one root concept; receiving information about a modified semantic network from the server; presenting the information about the modified semantic network to a user; receiving a response from the user; based on the response, further modifying the semantic network. The system interactively modifies semantic networks in response to user feedback, and produces personal semantic networks and document use histories.