Relational Graph for Multi-Source User Skill Querying
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Solution Overview
Problem
Users face inefficiencies and inaccuracies when trying to identify characteristics such as domains of knowledge associated with individuals, as existing methods require piecemeal interrogation of separate information repositories, leading to cumbersome and time-consuming processes with isolated silos of information.
Innovation Solution
A computer-implemented technique creates and interrogates a relational data structure, such as a graph, by extracting user data items from application sources and knowledge data items from knowledge sources, generating objects and links to represent relationships, and using keyphrases to map to associated skills, allowing for efficient querying and ranking of relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users separately interrogate multiple information repositories to identify individual characteristics, then comprehensive information can be obtained, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The patent merges multiple separate information repositories into a unified graph data structure that integrates data from application sources (user data items) and knowledge sources (knowledge data items). This consolidation allows users to query all information simultaneously through a single interface rather than separately interrogating multiple repositories, thereby obtaining comprehensive information while significantly reducing the time and effort required.
Solution Approach 2:
The graph data structure serves as an intermediary layer between the original data sources and the user. It extracts and normalizes data from multiple sources, creating a unified representation with objects and relationships that can be efficiently queried. This intermediary structure enables comprehensive information retrieval without requiring users to interact with multiple separate systems.
2Adaptability or versatility
If separate services are used to gather information, then diverse data sources can be accessed, but information silos with separate terminology create assessment difficulties
Solution Approach 1:
The graph data structure implements a universal schema that can represent data from diverse sources using common object types and relationship patterns. By normalizing different data formats into a unified structure with standard object classes (e.g., Person, Skill, Document) and relationship types, the system maintains adaptability to access diverse data sources while enabling consistent and accurate information assessment across all sources.
Solution Approach 2:
The system applies local quality by allowing each object in the graph to have source-specific attributes while maintaining a consistent overall structure. Individual objects can incorporate terminology and characteristics from their original sources through source-specific fields, while the global graph structure provides a standardized framework for comparison and assessment, thus preserving data source diversity without sacrificing assessment precision.
3Productivity
If multiple data sources are integrated into a unified structure, then efficient querying is enabled, but the complexity of creating and maintaining the relational structure increases
Solution Approach 1:
The patent segments the complex integration task into distinct phases: (1) data extraction from multiple sources, (2) object creation from extracted data, (3) relationship establishment between objects, and (4) graph structure assembly. This segmentation allows the system to manage complexity by handling each aspect separately while maintaining overall query efficiency through the unified graph structure that results from the segmented process.
Data Source
AI summary
A computer-implemented technique is described herein for creating a relational data structure by extracting user data items from a collection of one or more applications sources. These data items evince interests exhibited by the users, and may include messages, documents, tasks, meetings, etc. The technique also collects knowledge data items from one or more knowledge sources. In one implementation, these data items may include terms used to describe skills possessed by the users. The technique constructs the data structure by providing objects associated with respective data items, and links between respective pairs of objects. In its real-time phase of operation, the technique allows a user to interrogate the relational data structure, e.g., to identify skills possessed by a particular user, to find users associated with a specified skill, etc.