
Knowledge Graph Fact Prediction via KnowledgeEnriched Tensor Factorization
We present a family of novel methods for embedding knowledge graphs into...
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Tucker decompositionbased Temporal Knowledge Graph Completion
Knowledge graphs have been demonstrated to be an effective tool for nume...
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Variational Quantum Circuit Model for Knowledge Graphs Embedding
In this work, we propose the first quantum Ansätze for the statistical r...
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Embedding Models for Episodic Memory
In recent years a number of largescale tripleoriented knowledge graphs...
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Binarized Knowledge Graph Embeddings
Tensor factorization has become an increasingly popular approach to know...
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Linking ImageNet WordNet Synsets with Wikidata
The linkage of ImageNet WordNet synsets to Wikidata items will leverage ...
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Learning with Memory Embeddings
Embedding learning, a.k.a. representation learning, has been shown to be...
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Quantum Machine Learning Algorithm for Knowledge Graphs
Semantic knowledge graphs are largescale tripleoriented databases for knowledge representation and reasoning. Implicit knowledge can be inferred by modeling and reconstructing the tensor representations generated from knowledge graphs. However, as the sizes of knowledge graphs continue to grow, classical modeling becomes increasingly computational resource intensive. This paper investigates how quantum resources can be capitalized to accelerate the modeling of knowledge graphs. In particular, we propose the first quantum machine learning algorithm for making inference on tensorized data, e.g., on knowledge graphs. Since most tensor problems are NPhard, it is challenging to devise quantum algorithms to support that task. We simplify the problem by making a plausible assumption that the tensor representation of a knowledge graph can be approximated by its lowrank tensor singular value decomposition, which is verified by our experiments. The proposed samplingbased quantum algorithm achieves exponential speedup with a runtime that is polylogarithmic in the dimension of knowledge graph tensor.
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