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UniSKGRep: A unified representation learning framework of social network and knowledge graph by Yinghan Shen; Xuhui Jiang; Zijian Li; Yuanzhuo Wang; Chengjin Xu; Huawei Shen; Xueqi Cheng is a Computer Science article available to read on EtoBox.

What is UniSKGRep: A unified representation learning framework of social network and knowledge graph about?

The human-oriented applications aim to exploit behaviors of people, which impose challenges on user modeling of integrating social network (SN) with knowledge graph (KG), and jointly analyzing two types of graph data. However, existing graph representation learning methods merely represent one of two graphs alone, and hence are unable to comprehensively consider features of both SN and KG with profiling the correlation between them, resulting in unsatisfied performance in downstream tasks. Considering the diverse gap of features and the difficulty of associating of the two graph data, we introduce a Unified Social Knowledge Graph Representation learning framework (UniSKGRep), with the goal to leverage the multi-view information inherent in the SN and KG for improving the downstream tasks of user modeling. To the best of our knowledge, we are the first to present a unified representation learning framework for SN and KG. Concretely, the SN and KG are organized as the Social Knowledge Graph (SKG), a unified representation of SN and KG. For the representation learning of SKG, first, two separate encoders in the Intra-graph model capture both the social-view and knowledge-view in two e

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Author

Yinghan Shen; Xuhui Jiang; Zijian Li; Yuanzhuo Wang; Chengjin Xu; Huawei Shen; Xueqi Cheng

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)