Learning relational fractals for deep knowledge graph embedding in online social networks

Zhang, Ji and Tan, Leonard and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Wang, Dianwei and Ying, Josh Jia-Ching and Wang, Xin (2019) Learning relational fractals for deep knowledge graph embedding in online social networks. In: 20th International Conference on Web Information Systems Engineering (WISE 2019), 26-30 Nov 2019, 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.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: c. Springer Nature Singapore Pte Ltd. 2020. Permanent restricted access to Published version in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sept 2019 -)
Faculty/School / Institute/Centre: Historic - Institute for Resilient Regions - Centre for Health, Informatics and Economic Research (1 Aug 2018 - 31 Mar 2020)
Date Deposited: 02 Jun 2020 06:05
Last Modified: 05 Jun 2020 05:06
Uncontrolled Keywords: deep learning, fact evolution, knowledge graph embedding, online social networks, relational turbulence
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Identification Number or DOI: 10.1007/978-3-030-34223-4_42
URI: http://eprints.usq.edu.au/id/eprint/38128

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