Hypergraph Event Embedding for High-Dimensional Relationship Capture
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Solution Overview
Problem
Current graph representations of event data are limited in capturing high-dimensional relationships, leading to inadequate embedding learning and suboptimal analytical results due to their low dimensionality.
Innovation Solution
The approach involves representing events using hypergraphs with hyperedges to capture high-dimensional relationships and leveraging the word2vec framework to map event sequences to sentences, where each event is treated as a word, enabling effective learning of event embeddings through an artificial neural network with a self-attention mechanism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If graph representation with low-dimensional edges is used to represent events, then the representation is simple and easy to process, but the high-dimensional relationships and complex patterns in event data are lost
Solution Approach 1:
The patent transitions from low-dimensional graph edges to high-dimensional hyperedges that can represent multiple relationships simultaneously. Each hyperedge connects multiple nodes (entities) and can encode complex relationships such as temporal sequences, causal connections, and contextual associations that cannot be captured by traditional pairwise edges, thereby resolving the information loss problem while maintaining representation capability.
2Ease of manufacture
If traditional graph representation is used for event data, then the structure is simple, but the embedding learning quality is limited due to insufficient relationship capture
Solution Approach 1:
The patent employs composite hyperedge structures that integrate multiple relationship types and contextual information into a single unified representation. These hyperedges combine various features (temporal, spatial, causal, contextual) into a composite structure that enriches the embedding learning process, enabling the system to learn more precise and nuanced event representations without significantly increasing implementation complexity.
3Duration of action of moving object
If events are represented as sequences in time, then temporal relationships are captured, but other dimensional relationships such as entity interactions and contextual patterns are missed
Solution Approach 1:
The patent merges temporal sequencing with multi-dimensional relationship encoding by representing events as hyperedges that simultaneously capture temporal order, entity interactions, and contextual patterns. This unified hyperedge representation integrates multiple relationship dimensions into a single structure, allowing the system to learn embeddings that reflect both temporal dynamics and complex inter-entity relationships without information loss.
Data Source
AI summary
The present teaching relates to method, system, medium, and implementations for learning embeddings. Upon receiving raw event data recording information related to a plurality of events, at least one attribute associated with each of the plurality of events is identified from the raw event data, wherein the at least one attribute represent characteristics associated with the event. The plurality of events are grouped into one or more aggregated groups in accordance with an aggregation criterion, defined with respect to at least some of the attributes identified from the events. Each aggregated group includes some events that satisfies the aggregation criterion which are used to create an event sequence, which includes the events in the aggregated group and one or more gaps each of which separates a pair of adjacent events in the at least one event. The created event sequences are then provided to an artificial neural network (ANN) to learn event embeddings.