Semantic Graph for Enterprise Information Asset Management
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Solution Overview
Problem
Large enterprises face challenges in managing and understanding relationships between numerous information assets, leading to ambiguity and confusion when navigating large result sets, as existing systems fail to provide visibility into related assets without explicit secondary searches.
Innovation Solution
An information asset management tool that monitors user interactions with search results to generate a weighted semantic graph, capturing relationships between assets based on user behavior, and presents related assets to users, thereby enhancing search results and identifying important or underutilized assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users navigate through large result sets manually, then they can access information assets, but the system fails to provide visibility into related assets without explicit secondary searches
Solution Approach 1:
The system monitors user navigation behavior through search results and uses this feedback to automatically learn and infer relationships between information assets. This feedback loop enables the system to improve its understanding of asset relationships without requiring explicit user input or configuration.
Solution Approach 2:
The system performs self-learning by automatically analyzing user navigation patterns to discover asset relationships. This self-service mechanism eliminates the need for manual relationship definition or explicit user configuration, allowing the system to autonomously build its own knowledge model of information asset connections.
2Loss of information
If the system monitors user interactions to generate semantic graphs, then relationships between assets are identified, but user privacy and data collection requirements increase
Solution Approach 1:
The system extracts only the essential navigation path information from user interactions to build relationship models. By taking out only the necessary data elements (sequence of asset accesses) rather than collecting complete user behavior datasets, the system minimizes data quantity while still capturing meaningful relationship patterns.
Solution Approach 2:
The system applies partial monitoring by focusing on specific navigation events (access sequences) rather than comprehensive user behavior tracking. This partial action approach collects only the minimum necessary data to infer relationships, avoiding excessive data collection while still achieving the desired relationship identification.
Data Source
AI summary
Embodiments of the invention provide an approach for creating, evolving and using a weighted semantic graph to manage and potentially identify certain information assets within an enterprise. The semantic graph may be generated by monitoring users navigating through search results which provide a set of information assets responsive to a search query. By recording the navigation path taken by many users, relationships between information assets may be identified. Further, once generated, the semantic graph may be used to present users with in indication of related information assets as part of the search results. Further still, the semantic graph may also be used to identify information assert “hubs” as well as information assets that may provide low utility to individuals within the enterprise.