Graph Neural Networks for Semantic and Co-engagement Learning
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Solution Overview
Problem
Conventional machine learning models struggle to effectively learn both relationships between entities and semantic concepts, as well as co-engagement relationships, which are crucial for search engines and recommendation systems. Additionally, these models often require frequent re-training to accommodate new entities, making them computationally expensive.
Innovation Solution
The proposed solution involves training a graph neural network (GNN) to learn user co-engagement with entities and semantic concept relationships. This is achieved by generating a semantic knowledge graph from semantic information and user engagement data, and then using a knowledge graph embedding technique to generate feature vectors for concept nodes. The GNN is trained on these graphs, allowing it to capture both semantic and co-engagement aspects without the need for frequent re-training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional machine learning models are used to learn relationships between entities and semantic concepts, then the models can be trained using traditional methods, but the models require frequent re-training to accommodate new entities which makes them computationally expensive
Solution Approach 1:
The patent segments the learning process into two distinct components: (1) learning semantic relationships between entities and concepts using knowledge graphs, and (2) learning co-engagement relationships between entity pairs using graph neural networks. This segmentation allows each component to be optimized independently, with the GNN component being able to adapt to new entities without requiring complete re-training of the entire system.
Solution Approach 2:
The patent implements a dynamic architecture where the graph neural network can incrementally update its representations when new entities are added to the knowledge graph. Instead of static models that require full re-training, the system dynamically adapts by propagating information through the graph structure, allowing efficient incorporation of new entities while maintaining learned relationships.
2Reliability
If conventional machine learning models are trained to learn both relationships between entities and semantic concepts and co-engagement relationships, then the models can provide comprehensive results, but the models have difficulty learning both types of relationships simultaneously
Solution Approach 1:
The patent merges two powerful approaches into a unified framework: knowledge graphs for representing semantic relationships and graph neural networks for learning co-engagement patterns. By combining these approaches, the system leverages the structured semantic information from knowledge graphs while simultaneously capturing complex interaction patterns through GNNs, achieving comprehensive relationship learning without the limitations of conventional models.
Solution Approach 2:
The patent introduces knowledge graphs as an intermediary structure that bridges semantic relationships and co-engagement patterns. The knowledge graph serves as a mediator that organizes entities and concepts with their semantic relationships, which then feeds into the GNN architecture. This intermediary structure allows the system to process and integrate both types of relationships in a coordinated manner, improving learning reliability.
3Measurement precision
If conventional machine learning models are re-trained frequently to learn about new entities, then the models stay up-to-date with current data, but the frequent re-training consumes significant computational resources and time
Solution Approach 1:
The patent performs preliminary action by pre-processing and organizing data into a knowledge graph structure with entities, concepts, and semantic relationships before feeding it to the graph neural network. This preliminary structuring of data allows the model to efficiently adapt to new entities through graph-based information propagation rather than requiring time-consuming re-training processes, maintaining information currency with minimal computational overhead.
Data Source
AI summary
One embodiment of a method for training a machine learning model includes generating a graph based on one or more semantic concepts associated with a plurality of entities and user engagement with the plurality of entities, and performing one or more operations to train an untrained machine learning model based on the graph to generate a trained machine learning model.