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Knowledge Association with Hyperbolic Knowledge Graph Embeddings [article]

Zequn Sun, Muhao Chen, Wei Hu, Chengming Wang, Jian Dai, Wei Zhang
2020 arXiv   pre-print
We propose a hyperbolic relational graph neural network for KG embedding and capture knowledge associations with a hyperbolic transformation.  ...  They also depend on high embedding dimensions to realize enough expressiveness. Differently, we explore with low-dimensional hyperbolic embeddings for knowledge association.  ...  To leverage such merit, HyperKA employs a hyperbolic relational graph neural network (GNN) for KG embedding and captures multi-granular knowledge associations with a hyperbolic transformation between embedding  ... 
arXiv:2010.02162v1 fatcat:lhah4lzokzdafib2judmzzzvhu

Knowledge Association with Hyperbolic Knowledge Graph Embeddings

Zequn Sun, Muhao Chen, Wei Hu, Chengming Wang, Jian Dai, Wei Zhang
2020 Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)   unpublished
We propose a hyperbolic relational graph neural network for KG embedding and capture knowledge associations with a hyperbolic transformation.  ...  They also depend on high embedding dimensions to realize enough expressiveness. Differently, we explore with low-dimensional hyperbolic embeddings for knowledge association.  ...  To leverage such merit, HyperKA employs a hyperbolic relational graph neural network (GNN) for KG embedding and captures multi-granular knowledge associations with a hyperbolic transformation between embedding  ... 
doi:10.18653/v1/2020.emnlp-main.460 fatcat:zacvssmle5btlebvnyo3g6wxcu

HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation [article]

Yuntao Du, Xinjun Zhu, Lu Chen, Baihua Zheng, Yunjun Gao
2022 arXiv   pre-print
Furthermore, we propose a dual item embeddings design to represent and propagate collaborative signals and knowledge associations separately, and leverage the gated aggregation to distill discriminative  ...  Knowledge graph (KG) plays an increasingly important role to improve the recommendation performance and interpretability.  ...  Early studies [1, 42, 53] directly integrate knowledge graph embeddings with items to enhance their representations.  ... 
arXiv:2204.04959v1 fatcat:xjj3a7e2z5dyxfaim24m24hnha

HAKG

Yuntao Du, Xinjun Zhu, Lu Chen, Baihua Zheng, Yunjun Gao
2022 Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval  
Furthermore, we propose the dual item embeddings design to represent and propagate collaborative signals and knowledge associations separately, and leverage the gated aggregation to distill discriminative  ...  Knowledge graph (KG) plays an increasingly important role to improve the recommendation performance and interpretability.  ...  Early studies [1, 44, 55] directly integrate knowledge graph embeddings with items to enhance their representations.  ... 
doi:10.1145/3477495.3531987 fatcat:drb4k3f3ufczdawlcys3pouiva

Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation [article]

Yankai Chen, Menglin Yang, Yingxue Zhang, Mengchen Zhao, Ziqiao Meng, Jianye Hao, Irwin King
2022 arXiv   pre-print
To address this issue and provide more accurate recommendation, we propose a knowledge-aware recommendation method with the hyperbolic geometry, namely Lorentzian Knowledge-enhanced Graph convolutional  ...  Via unifying the KG with user-item interactions into a tripartite graph, recent works explore the graph topologies to learn the low-dimensional representations of users and items with rich semantics.  ...  LKGR learns the hierarchical structures of scale-free graphs with the hyperbolic geometry, and coherently summarizes interactive signals and knowledge associations into the low-dimensional embeddings,  ... 
arXiv:2108.06468v3 fatcat:crvllwrugvhsvc7h2lg3skchdu

Hyperbolic Graph Neural Networks: A Review of Methods and Applications [article]

Menglin Yang, Min Zhou, Zhihao Li, Jiahong Liu, Lujia Pan, Hui Xiong, Irwin King
2022 arXiv   pre-print
Recently, hyperbolic space has gained increasing popularity in processing graph data with tree-like structure and power-law distribution, owing to its exponential growth property.  ...  with highly non-Euclidean latent anatomy.  ...  For HGNN-based knowledge graph associations, [Sun et al., 2020] developed a HyperKA model which employs an HGNN for KG embedding and utilizes a hyperbolic transformation across embedding spaces to capture  ... 
arXiv:2202.13852v1 fatcat:atsnqyg2mrap5bbdriqkmbbo7a

Multi-modal Entity Alignment in Hyperbolic Space [article]

Hao Guo, Jiuyang Tang, Weixin Zeng, Xiang Zhao, Li Liu
2021 arXiv   pre-print
It has been a new trend to merge multi-modal information into knowledge graph(KG), resulting in multi-modal knowledge graphs (MMKG).  ...  We first adopt the Hyperbolic Graph Convolutional Networks (HGCNs) to learn structural representations of entities.  ...  Then, we convert the images associated with entities into visual embeddings using the densenet model, which are also projected into the hyperbolic space.  ... 
arXiv:2106.03619v1 fatcat:wpu3tfhl4jbonmwq5nefjyiwli

Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones [article]

Yushi Bai, Rex Ying, Hongyu Ren, Jure Leskovec
2021 arXiv   pre-print
Here we present ConE (Cone Embedding), a KG embedding model that is able to simultaneously model multiple hierarchical as well as non-hierarchical relations in a knowledge graph.  ...  Hierarchical relations are prevalent and indispensable for organizing human knowledge captured by a knowledge graph (KG).  ...  Here we propose a novel hyperbolic knowledge graph embedding model ConE.  ... 
arXiv:2110.14923v2 fatcat:y4v6zawwa5dpfn3brcg34wbwoi

Learning Representations of Entities and Relations [article]

Ivana Balažević
2022 arXiv   pre-print
The third contribution is MuRP, first multi-relational graph representation model embedded in hyperbolic space.  ...  The focus of this thesis is on (i) improving knowledge graph representation with the aim of tackling the link prediction task; and (ii) devising a theory on how semantics can be captured in the geometry  ...  Based on this intuition, we focus on embedding multi-relational knowledge graph data in hyperbolic space.  ... 
arXiv:2201.13073v1 fatcat:mtozmiptsjeehbb6wnlrc7vrqu

Low-Dimensional Hyperbolic Knowledge Graph Embeddings [article]

Ines Chami, Adva Wolf, Da-Cheng Juan, Frederic Sala, Sujith Ravi, Christopher Ré
2020 arXiv   pre-print
Knowledge graph (KG) embeddings learn low-dimensional representations of entities and relations to predict missing facts.  ...  Our approach combines hyperbolic reflections and rotations with attention to model complex relational patterns.  ...  ., 2019 ) models use graph attention networks for knowledge graph embeddings.  ... 
arXiv:2005.00545v1 fatcat:mm3ej3ptirh7xmrytkcuiygr6y

Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path [article]

Lili Wang, Chongyang Gao, Chenghan Huang, Ruibo Liu, Weicheng Ma, Soroush Vosoughi
2021 arXiv   pre-print
knowledge for meta-path selection.  ...  In this paper, we propose a novel self-guided random walk method that does not require meta-path for embedding heterogeneous networks into hyperbolic space.  ...  to embed attributed graphs and Ganea et al. (2018) use hyperbolic cones as a heuristic for embedding directed acyclic graphs.  ... 
arXiv:2106.09923v1 fatcat:w2hae4qsljcmbfatzsd2y6jjau

HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning [article]

Mingyu Derek Ma, Muhao Chen, Te-Lin Wu, Nanyun Peng
2021 arXiv   pre-print
their relations with a Hyperbolic Graph Neural Network (HGNN).  ...  with the geometric properties of a hyperbolic space.  ...  , hierarchical classification (López and Strube, 2020; , knowledge association , knowledge graph completion (Wang et al., 2021a; Balazevic et al., 2019) and event prediction (Surís et al., 2021) .  ... 
arXiv:2109.10500v1 fatcat:7uupuxk3ozd4bkjrqndnai7zoq

Application and evaluation of knowledge graph embeddings in biomedical data

Mona Alshahrani, Maha A. Thafar, Magbubah Essack
2021 PeerJ Computer Science  
Such knowledge graph embedding methods provide a practical approach to data analytics and increase chances of building machine learning models with high prediction accuracy that can enhance decision support  ...  The reason being, entities and relations in the knowledge graph can be represented as embedding vectors in semantic space, and these embedding vectors have been used to predict relationships between entities  ...  Knowledge graph embedding methods employ various methodological techniques and model different relational patterns and properties of the knowledge graphs and scale differently with the knowledge graphs  ... 
doi:10.7717/peerj-cs.341 pmid:33816992 pmcid:PMC7959619 fatcat:3f5kmbwalzbq7l6xcixwdxa6fa

Hyperbolic Hierarchical Knowledge Graph Embeddings for Link Prediction in Low Dimensions [article]

Wenjie Zheng, Wenxue Wang, Fulan Qian, Shu Zhao, Yanping Zhang
2022 arXiv   pre-print
Knowledge graph embeddings (KGE) have been validated as powerful methods for inferring missing links in knowledge graphs (KGs) since they map entities into Euclidean space and treat relations as transformations  ...  For hierarchical data, instead of traditional Euclidean space, hyperbolic space as an embedding space has shown the promise of high fidelity and low memory consumption; however, existing hyperbolic KGE  ...  To address the challenges above, we propose the Hyperbolic Hierarchical Knowledge Graph Embeddings (HypHKGE) approach, which models the semantic hierarchies of KGs in hyperbolic space.  ... 
arXiv:2204.13704v1 fatcat:2o7gb7alsvbnteduuiffi2yawy

A Literature Review of Recent Graph Embedding Techniques for Biomedical Data [article]

Yankai Chen and Yaozu Wu and Shicheng Ma and Irwin King
2021 arXiv   pre-print
Recently, graph embedding methods provide an effective and efficient way to address the above issues.  ...  It converts graph-based data into a low dimensional vector space where the graph structural properties and knowledge information are well preserved.  ...  There may exist many issues associated with the biomedical data that may bring challenges to biomedical graph embedding tasks.  ... 
arXiv:2101.06569v2 fatcat:vqfosu4o6neklfffpvpmmdor2q
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