Data Provenance System Using Context Images for Source Identification
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Solution Overview
Problem
The rapid expansion and digital nature of data on the Internet make it increasingly difficult to determine the origins of data and the ideas embodied in it, leading to challenges in identifying plagiarism, intellectual property infringement, and misappropriation of digital works, as existing tools lack effective methods for tracing data provenance and ensuring proper attribution.
Innovation Solution
A data provenance system that processes digital content to identify concepts, generates similarity scores, and determines the source of content by searching a corpus of digital works, using context images and natural language processing to establish relationships between artifacts and provide automated attribution and citation suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing tools are used to detect plagiarism and intellectual property infringement, then the detection process becomes simpler, but the ability to effectively trace data provenance and identify content sources is insufficient
Solution Approach 1:
The system segments digital works into discrete artifacts and further into contextual units (statements, phrases, words) that can be independently analyzed. Each artifact is broken down into context images representing semantic relationships, enabling precise tracking of content origins without requiring analysis of entire documents at once.
Solution Approach 2:
Context images serve as intermediary representations between the original digital content and the provenance analysis system. These graphical models translate diverse media types (text, images, audio, video) into a common format that captures semantic relationships, facilitating accurate comparison and source identification across different artifact types.
2Reliability
If the corpus of digital works to be searched is expanded to cover more Internet content, then the comprehensiveness of provenance tracing is improved, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary indexing and context image generation for artifacts in the corpus before actual provenance analysis is needed. By pre-processing and organizing digital works into structured context images with extracted semantic relationships, the system reduces the computational burden during query execution, enabling faster comparison even across large corpora.
Solution Approach 2:
The system replaces traditional text-based comparison methods with visual pattern recognition using context images. By converting semantic relationships into graphical representations and using image similarity algorithms, the system achieves faster and more accurate content matching compared to conventional string-based plagiarism detection methods.
3Measurement precision
If traditional text-based comparison methods are used, then the implementation is simpler, but the ability to detect conceptual similarity and paraphrasing is insufficient
Solution Approach 1:
Context images serve as intermediary representations between the original digital content and the provenance analysis system. These graphical models translate diverse media types (text, images, audio, video) into a common format that captures semantic relationships, facilitating accurate comparison and source identification across different artifact types.
Solution Approach 2:
The system transforms the comparison parameter from literal text matching to semantic relationship matching. By extracting entities, attributes, and relationships from content and representing them as contextual graphs, the system detects similarity based on meaning and structure rather than exact word matches, enabling identification of paraphrasing and conceptual plagiarism.
Data Source
AI summary
An electronic artifact is accessed which includes content of a particular type of media. Text is determined corresponding to the content and natural language processing is performed on the text to identify at least a subset of words in a statement within the text and determine meanings of each word in the subset of words. A context image is generated for the electronic artifact based on the natural language processing, where the context image includes a graph including nodes corresponding to the subset of words and the context image defines relationships between the subset of words.