DCRL-KG: Distributed Multi-Modal Knowledge Graph Retrieval Platform Based on Collaborative Representation Learning  

作　　者：Leilei Li Yansheng Fu Dongjie Zhu Xiaofang Li Yundong Sun Jianrui Ding Mingrui Wu Ning Cao Russell Higgs 

机构地区：[1]Artificial Intelligence Academy,Wuxi Vocational College of Science and Technology,Wuxi,214068,China [2]School of Computer Science and Technology,Harbin Institute of Technology,Weihai,204209,China [3]Department of Mathematics,Harbin Institute of Technology,Weihai,264209,China [4]College of Information Engineering,Shandong Vocational and Technical University of International Studies,Rizhao,276826,China [5]School of Mathematics and Statistics,University College Dublin,Dublin,D04 V1W8,Ireland

出　　处：《Intelligent Automation & Soft Computing》2023年第6期3295-3307,共13页智能自动化与软计算（英文）

基　　金：This work is supported by the Fundamental Research Funds for the Central Universities(Grant No.HIT.NSRIF.201714);Weihai Science and Technology Development Program(2016DX GJMS15);Weihai Scientific Research and Innovation Fund(2020);Key Research and Development Program in Shandong Provincial(2017GGX90103).

摘　　要：The knowledge graph with relational abundant information has been widely used as the basic data support for the retrieval platforms.Image and text descriptions added to the knowledge graph enrich the node information,which accounts for the advantage of the multi-modal knowledge graph.In the field of cross-modal retrieval platforms,multi-modal knowledge graphs can help to improve retrieval accuracy and efficiency because of the abundant relational infor-mation provided by knowledge graphs.The representation learning method is sig-nificant to the application of multi-modal knowledge graphs.This paper proposes a distributed collaborative vector retrieval platform(DCRL-KG)using the multi-modal knowledge graph VisualSem as the foundation to achieve efficient and high-precision multimodal data retrieval.Firstly,use distributed technology to classify and store the data in the knowledge graph to improve retrieval efficiency.Secondly,this paper uses BabelNet to expand the knowledge graph through multi-ple filtering processes and increase the diversification of information.Finally,this paper builds a variety of retrieval models to achieve the fusion of retrieval results through linear combination methods to achieve high-precision language retrieval and image retrieval.The paper uses sentence retrieval and image retrieval experi-ments to prove that the platform can optimize the storage structure of the multi-modal knowledge graph and have good performance in multi-modal space.

关 键 词：Multi-modal retrieval distributed storage knowledge graph 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]