Knowledge Graph Entity Linking for Dynamic Content Topic Analysis
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Solution Overview
Problem
Existing methods for determining the topic of user interactions in interactive computing environments are limited by static and inconsistent manual labels, failing to provide accurate information for customizing user interfaces effectively.
Innovation Solution
A content analysis system that extracts entity tags from input content, links them to a digital knowledge base, generates a knowledge graph, and identifies related entities based on graph embeddings to personalize user interfaces by presenting content matching user interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling methods are used to determine content topics, then the process is simple to implement, but the labels are static and inconsistent leading to inaccurate topic determination
Solution Approach 1:
The patent replaces manual labeling (mechanical process) with automated entity recognition and knowledge graph processing (computational process). The system automatically extracts entities from content, links them to knowledge base entities, and determines topics through computational analysis of entity relationships and graph embeddings, eliminating human subjectivity and inconsistency.
Solution Approach 2:
The patent introduces a knowledge base as an intermediary between content analysis and topic determination. The knowledge base provides standardized entity definitions and relationships that mediate between raw content and topic labels, enabling consistent and accurate topic determination without direct manual labeling.
2Adaptability or versatility
If static manual labels are used for content customization, then the implementation is straightforward, but the labels cannot adapt to different users or contexts
Solution Approach 1:
The patent transforms static manual labels into dynamic topic representations through knowledge graphs. Topics are no longer fixed labels but living structures that can adapt to different users based on their interaction patterns. The system dynamically builds user profiles by analyzing which entities and topics users engage with, enabling personalized content recommendations that evolve with user interests.
Solution Approach 2:
The patent implements feedback loops where user interactions with content are continuously analyzed to refine topic assignments and user profiles. The system monitors user behavior patterns and uses this feedback to adjust topic modeling and content recommendations, creating an adaptive system that learns from user responses and improves customization over time.
3Measurement precision
If automated entity extraction and knowledge graph processing is implemented, then topic determination accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing content to extract entities and pre-building knowledge graphs with standardized entity relationships. This preparation work is done in advance so that when actual topic determination is needed, the system can quickly query and match against the pre-processed data and knowledge graph structures, reducing real-time processing requirements.
Solution Approach 2:
The patent creates a simplified representation (copy) of complex content through entity extraction and knowledge graph abstraction. Instead of processing the full original content repeatedly, the system works with extracted entity representations and their relationships in the knowledge graph, which are lighter-weight data structures that enable faster processing while preserving essential semantic information.
Data Source
AI summary
In some embodiments, a content analysis system accesses input content associated with a user of an online platform. The content analysis system extracts entity tags for entities contained in the input content and links the identities to standard entities in a knowledge base to generate linked entities. The content analysis system further generates a knowledge graph to include the linked entities and other standard entities connected to the linked entities as nodes and edges connecting these nodes. Based on the knowledge graph, the content analysis system identifies related entities that are similar to the linked entities and cause the online platform to be modified based on the related entities.