Knowledge Graph Path Traversal for Indirect Entity Connections
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Solution Overview
Problem
Existing applications are limited in their ability to illustrate indirect connections between entities, such as individuals or organizations, and lack mechanisms to view relationships with locations or areas of interest, requiring time-consuming manual research for prospecting new clients.
Innovation Solution
A system for real-time profile search and data aggregation that includes a memory for storing data records, a data aggregation service, an entity recognition service, and a search engine to consolidate data from multiple sources into a knowledge graph, enabling path traversal and visualization of connections between entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing applications only show direct relationships between profiles, then the application complexity remains low, but the ability to illustrate indirect connections between entities is limited
Solution Approach 1:
The system segments the connection illustration into direct relationships (first-degree connections) and indirect relationships (second-degree and beyond). The interface separately displays direct connections in the profile view and indirect connections through path traversal results, allowing complex indirect relationship data to be processed and displayed in a structured, manageable way that doesn't overwhelm the interface.
Solution Approach 2:
The system adds a temporal/distance dimension to relationship display by showing paths of different degrees (1st degree, 2nd degree, etc.). This transforms the flat direct-connection view into a multi-dimensional view where relationships are organized by their distance in the knowledge graph, enabling indirect connections to be visualized without complicating the core interface.
2Productivity
If manual research is used to identify new clients and profile targets, then data accuracy can be maintained, but the process becomes time-consuming and less productive
Solution Approach 1:
The system performs automatic prospect research by querying the knowledge graph and data sources to retrieve entity information, relationships, and connection paths. The web application autonomously gathers and consolidates data about potential clients without requiring manual research, significantly reducing the time needed for prospect identification and profiling while maintaining data accuracy through systematic data aggregation.
3Adaptability or versatility
If existing applications focus only on person-to-person relationships, then the data model remains simple, but the ability to view relationships with locations and institutions is limited
Solution Approach 1:
The knowledge graph is designed as a universal data model that handles multiple entity types (persons, organizations, locations) and relationship types through a unified structure. The same graph infrastructure and query mechanisms used for person-to-person relationships are extended to handle person-to-organization, person-to-location, and organization-to-organization relationships, allowing the system to accommodate diverse relationship types without requiring separate specialized systems.
Data Source
AI summary
Embodiments generally relate to real-time profile search interfaces and web services with the ability to real-time search for and view details on entities and visualizations of the network. The computer service enables real-time profile search of entities and performs a sequence of data aggregation heuristics to present a consolidated view of an individual.