Knowledge Graph Canonicalization Using Joint Entity-Relation Clustering
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Solution Overview
Problem
Existing knowledge graphs struggle with adapting to new domains and suffer from redundant and ambiguous triples due to non-canonicalized noun and relation phrases, leading to suboptimal query results and inefficient memory usage.
Innovation Solution
The introduction of Canonicalizing Using Variational AutoEncoders (CUVA), which employs a neural architecture for joint learning of entity and relation clusters using variational autoencoders, to group noun and relation phrases into unique clusters, reducing redundancy and improving search performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If canonicalization is applied to noun phrases and relation phrases in knowledge graphs, then redundancy is reduced and memory efficiency is improved, but the complexity of the system increases due to the need for clustering algorithms and embedding learning
Solution Approach 1:
The patent replaces traditional mechanical clustering approaches with a neural network-based variational autoencoder system that learns embeddings and cluster assignments jointly. This substitution enables automatic canonicalization through learned representations rather than manual or rule-based methods, reducing redundancy while managing complexity through end-to-end learning.
Solution Approach 2:
The patent changes the parameter space by learning continuous embedding vectors for entities and relations, along with discrete cluster assignments. This parameter transformation allows the system to represent canonicalization in a continuous latent space, enabling smooth optimization and reducing redundancy through learned groupings rather than fixed categorical assignments.
2Measurement precision
If joint learning of entity and relation clusters is performed, then canonicalization accuracy is improved, but the training time and computational resources increase
Solution Approach 1:
The patent merges the learning processes for entity clusters and relation clusters into a single joint training objective. By combining both clustering tasks into one unified variational autoencoder model, the system achieves improved canonicalization accuracy through shared representations and joint optimization, while managing training time through efficient gradient-based learning rather than separate sequential processes.
Solution Approach 2:
The variational autoencoder model serves multiple functions simultaneously: it learns entity embeddings, learns relation embeddings, performs entity clustering, and performs relation clustering. This multi-functionality allows the system to achieve high canonicalization accuracy across both entities and relations while avoiding the need for multiple separate training processes.
3Reliability
If variational autoencoders are used for canonicalization, then unique embeddings and cluster assignments are learned effectively, but the device complexity and computational requirements increase
Solution Approach 1:
The patent replaces traditional deterministic clustering algorithms with a probabilistic variational autoencoder framework. This substitution allows the system to learn uncertain and ambiguous cluster assignments through probabilistic inference, improving canonicalization effectiveness for entities and relations that may belong to multiple categories, while managing computational complexity through stochastic gradient descent optimization.
Data Source
AI summary
Embodiments of the present invention provide computer-implemented methods, computer program products and computer systems. Embodiments of the present invention can, in response to receiving information, learn entity representations and cluster assignments of respective entity representations in a joint manner for both entities and relations of respective entities.