datatype aware knowledge graph representation learning in hyperbolic space

DataType-Aware Knowledge Graph Representation Learning in Hyperbolic Space

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AbstractKnowledge graphs are typical multi-relational structures, which is consisted of many entities and relations. Nonetheless, existing knowledge graphs are still sparse and far from being complete. To refine the knowledge graphs, representation learning is utilized to embed entities and relations into low-dimensional spaces. Many existing knowledge graphs embedding models focus on learning latent features in close-world assumption but omit the changeable of each knowledge graph.In this paper, we propose a knowledge graph representation learning model, called Caps-OWKG, which leverages the capsule network to capture the both known and unknown triplets features in open-world knowledge graph. It combines the descriptive text and knowledge graph to get descriptive embedding and structural embedding, simultaneously. Then, the both above embeddings are used to calculate the probability of triplet authenticity. We verify the performance of Caps-OWKG on link prediction task with two common datasets FB15k-237-OWE and DBPedia50k. The experimental results are better than other baselines, and achieve the state-of-the-art performance.

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"DataType-Aware Knowledge Graph Representation Learning in Hyperbolic Space."

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DOI: 10.1145/3459637.3482421

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DataType-Aware Knowledge Graph Representation Learning in Hyperbolic Space

Y Shen ， Z Li ， X Wang ， J Li ， X Zhang

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Knowledge Graph (KG) representation learning aims to encode both entities and relations into a continuous low-dimensional vector space. Most existing methods only concentrate on learning representations from structural triples in Euclidean space, which cannot well exploit the rich semantic information with hierarchical structure in KGs. In this paper, we propose a novel DataType-aware hyperbolic knowledge representation learning model called DT-GCN, which has the advantage of fully embedding attribute values of data types information. We refine data types into five primitive modalities, including integer, double, Boolean, temporal, and textual. For each modality, an encoder is specifically designed to learn its embedding. In addition, we define a unified space based on Euclidean, spherical, and hyperbolic space, which is a continuous curvature space that combines advantages of three different spaces. Extensive experiments on both synthetic and real-world datasets show that our model is consistently better than the state-of-the-art models. The average performance is improved by 2.19% and 3.46% than the optimal baseline model on node classification and link prediction tasks, respectively. The results of ablation experiments demonstrate the advantages of embedding data types information and leveraging the unified space.

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Multi-Information-Enhanced Knowledge Embedding in Hyperbolic Space

• Conference paper
• First Online: 10 February 2023
• Cite this conference paper

• Jiajun Wu 13 ,
• Qian Zhou 14 ,
• Yuxuan Xiang 13 ,
• Tianlun Dai 13 ,
• Hua Dai 14 ,
• Hao Wen 13 &
• Qun Yang 13  

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13422))

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• Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data

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Knowledge Graph Representation Learning(KGRL) aims to map entities and relationships into a low-dimensional dense vector space. Most of the existing models focus only on the information of the triple when doing representation learning, ignoring the rich external semantic information. At the same time, these models consider entities and relations as static and single representations, so the knowledge represent ability is poor. Accordingly, we propose a novel knowledge graph representation model which enhanced knowledge graph embedding with multi-information. Firstly, our model carries out text enhancement and hyperbolic space embedding of triples in the knowledge graph respectively; Secondly, we concatenate the enhanced vector. Then, the concatenated vector through two transformation layer to fuse the semantic information and spacial information. Finally, we use the fused information to learn the context information through the Transformer coding layer, which will dynamically produce the final representation of the entity based on its context. Experimental results show that our model has a great improvement over other models. In the link prediction task, the evaluation protocol Hits@10 and MRR in the public dataset FB15k improve by 28.4% and 29.5% compared with the translation model. Compared with state-of-the-art model, the improvement is 2.5%, 6.3%.

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Acknowledgements

This work was supported partly by the National Key R &D Program of China(2020YFB1708100), National Natural Science Foundation of China(62172351), the 14th Five-Year Plan “Civil Aerospace Pre-research Project of China (D020101), Fundamental Research Funds for the Central Universities(NS2019001), the Fund of Prospective Layout of Scientific Research for NUAA(Nanjing University of Aeronautics and Astronautics.

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Jiajun Wu, Yuxuan Xiang, Tianlun Dai, Hao Wen & Qun Yang

Nanjing University of Posts and Communications, Nanjing, 210003, China

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Wu, J. et al. (2023). Multi-Information-Enhanced Knowledge Embedding in Hyperbolic Space. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13422. Springer, Cham. https://doi.org/10.1007/978-3-031-25198-6_23

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Knowledge Association with Hyperbolic Knowledge Graph Embeddings

• Zequn Sun , Muhao Chen , +3 authors W. Zhang
• Published in Conference on Empirical… 5 October 2020
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• Wang H Lian D Tong H Liu Q Huang Z Chen E (2021) HyperSoRec: Exploiting Hyperbolic User and Item Representations with Multiple Aspects for Social-aware Recommendation ACM Transactions on Information Systems 10.1145/3463913 40 :2 (1-28) Online publication date: 27-Sep-2021 https://dl.acm.org/doi/10.1145/3463913
• Shen Y Li Z Wang X Li J Zhang X Demartini G Zuccon G Culpepper J Huang Z Tong H (2021) DataType-Aware Knowledge Graph Representation Learning in Hyperbolic Space Proceedings of the 30th ACM International Conference on Information & Knowledge Management 10.1145/3459637.3482421 (1630-1639) Online publication date: 26-Oct-2021 https://dl.acm.org/doi/10.1145/3459637.3482421

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