Learning relational fractals for deep knowledge graph embedding in online social networks
Paper
Paper/Presentation Title | Learning relational fractals for deep knowledge graph embedding in online social networks |
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Presentation Type | Paper |
Authors | Zhang, Ji (Author), Tan, Leonard (Author), Tao, Xiaohui (Author), Wang, Dianwei (Author), Ying, Josh Jia-Ching (Author) and Wang, Xin (Author) |
Editors | Cheng, Reynold, mamoulis, Nikos, Sun, Yizhou and Huang, Xin |
Journal or Proceedings Title | Lecture Notes in Computer Science (Book series) |
Journal Citation | 11881, pp. 660-674 |
Number of Pages | 15 |
Year | 2019 |
Publisher | Springer |
Place of Publication | Singapore |
ISSN | 1611-3349 |
0302-9743 | |
ISBN | 9783030342227 |
9783030342234 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-030-34223-4_42 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-030-34223-4_42 |
Conference/Event | 20th International Conference on Web Information Systems Engineering (WISE 2019): Workshop, Demo and Tutorial |
Event Details | 20th International Conference on Web Information Systems Engineering (WISE 2019): Workshop, Demo and Tutorial Event Date 19 to end of 22 Jan 2020 Event Location Hong Kong, China |
Abstract | Knowledge Graphs (KGs) have deep and impactful applications in a wide-array of information networks such as natural language processing, recommendation systems, predictive analysis, recognition, classification, etc. Embedding real-life relational representations in KGs is an essential process of abstracting facts for many important data mining tasks like information retrieval, privacy and control, enrichment and so on. In this paper, we investigate the embedding of the relational fractals which are learned from the Relational Turbulence profiles in the transactions of Online Social Networks (OSNs) into KGs. These relational fractals have the capability of building both compositional-depth hierarchies and shallow-wide continuous vector spaces for more efficient computations on devices with limited resources. The results from our RFT model show accurate predictions of relational turbulence patterns in OSNs which can be used to evolve facts in KGs for more accurate and timely information representations. |
Keywords | deep learning, fact evolution, knowledge graph embedding, online social networks, relational turbulence |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Sciences |
Xi’an University of Posts and Telecommunications, China | |
National Chung Hsing University, Taiwan | |
Southwest Jiaotong University, China | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q59yy/learning-relational-fractals-for-deep-knowledge-graph-embedding-in-online-social-networks
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