Document Graph for Content Reuse and Provenance Tracking
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Solution Overview
Problem
In productivity applications, tracing the history and origin of content as it is reused and modified across multiple documents by multiple users is cumbersome, and finding relevant information from various sources is challenging.
Innovation Solution
A document graph is generated and updated to track relationships between content, documents, users, and actions, allowing for the identification and reuse of relevant content components by normalizing user actions and classifying users and content, which facilitates content reuse and collaboration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content is reused and modified in multiple different documents by multiple users, then content productivity and collaboration are enhanced, but it becomes cumbersome to trace the history of the original content and determine who created, contributed, and changed the content
Solution Approach 1:
A document graph data structure serves as an intermediary between content components and users, automatically tracking and recording all relationships, actions, and modifications. This mediator captures creator information, contributor identities, and change histories without requiring manual intervention, thus maintaining content provenance while enabling efficient reuse and collaboration.
2Productivity
If content is extracted and stored independently from source documents, then content retrieval and reuse efficiency are improved, but the complexity of managing content relationships and metadata increases
Solution Approach 1:
Content components are copied from source documents and stored independently in a content repository, with each copy maintaining references to its origin through the document graph. This allows efficient retrieval and reuse of content while the graph structure automatically manages relationships, reducing the perceived complexity for users.
Solution Approach 2:
The system adds a new dimensional layer (the document graph) that stores relationship metadata and provenance information separately from the content itself. This separates content storage from relationship tracking, enabling efficient content retrieval while systematically managing complexity through structured data organization.
3Adaptability or versatility
If user actions are normalized and classified, then content classification and reuse identification are enhanced, but the processing time and computational resources required increase
Solution Approach 1:
User actions are normalized and classified as they occur during document creation and editing, with the document graph being updated in real-time. This preliminary processing ensures that content is pre-classified and relationships are pre-established before reuse is needed, reducing delays when content needs to be retrieved or classified later.
Solution Approach 2:
The system automatically performs normalization and classification of user actions without requiring manual intervention or review. The document graph self-updates with classified information as users work, enabling the system to serve itself in maintaining accurate content metadata and relationships, thus reducing overall processing time.
Data Source
AI summary
A system for reuse of content within documents can include storage media on which a document graph is stored, and indexing software that, when executed by a processing system, directs the processing system to at least: identify content component types within a source document; and represent content components in a document graph at least connected by ‘contains’ edge to source document. A search of the document graph for reusable content can then be directed to related content components including those of a particular content component type or relevancy.