Entity Graph Search for Electronic Notes
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Solution Overview
Problem
Existing electronic note management systems are limited in discovering connections and relationships between electronic notes and non-electronic note entities such as persons, places, things, and events, making it inefficient for users to locate notes without matching keywords or tags.
Innovation Solution
The implementation of a connected entity graph system that organizes electronic notes and related entities, allowing users to explore and search through relationships independent of keyword tags, using a visual interface to represent and navigate these connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If keyword search is used to locate electronic notes, then search speed is improved for known tags, but the ability to discover connections between notes and non-note entities deteriorates
Solution Approach 1:
The system segments information into two distinct access paths: traditional keyword/tag-based search for rapid retrieval of known notes, and visual entity graph exploration for discovering connections between notes and non-note entities. This segmentation allows users to choose the appropriate method based on their information needs, maintaining search speed for known targets while enabling connection discovery for unknown relationships.
Solution Approach 2:
The visual entity graph acts as an intermediary representation layer between the note database and the user. It transforms complex relational data into an intuitive visual format that reveals connections between electronic notes and non-note entities (persons, places, things, events) without requiring keyword matching, thus bridging the gap between fast keyword search and comprehensive connection discovery.
2Ease of operation
If traditional keyword-based organization is used, then ease of access to tagged notes is improved, but the complexity of representing relationships between notes and non-note entities increases
Solution Approach 1:
The system adds a visual spatial dimension to the traditional flat keyword-based organization. By representing entities as visual nodes and relationships as connecting edges in a graph layout, the system transforms abstract relational data into a spatially organized visual structure that is both comprehensive in representing complexity and intuitive in ease of navigation.
3Loss of information
If comprehensive relationship tracking is implemented, then connection discovery capability is improved, but system complexity and resource requirements increase
Solution Approach 1:
The system implements self-service through automated relationship extraction and graph construction. The background processes automatically track and update connections between electronic notes and non-note entities without requiring manual configuration or complex user intervention, thereby maintaining comprehensive connection discovery capability while minimizing the operational complexity users must manage.
Data Source
AI summary
A computer-implemented method is disclosed. The method comprises managing, by a processor, a connected entity graph of nodes and edges, each node of the nodes representing an entity having an entity type of a plurality of entity types, at least one node of the nodes having an entity type of an electronic note, each edge of the edges representing a relationship between two entities; receiving a request for a search specifying a beginning entity, a linking parameter, and a filter on the linking parameter, the linking parameter specifying a property of an intermediary entity linking a source entity and a target entity during the search, the filter constraining a value of the property; searching the connected entity graph based on the request; causing a display of a search result.