Towards Federated Decentralized Querying on Knowledge Graphs

被引:0
|
作者
Munir, Siraj [1 ]
Ferretti, Stefano [1 ]
机构
[1] Univ Urbino Carlo Bo, Dept Pure & Appl Sci, Urbino, Italy
关键词
Federated Querying; Knowledge Graph; Semantic Querying; Decentralized Data;
D O I
10.1109/CSCI62032.2023.00104
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Recent innovations in research and development have enabled us to observe the world from different dimensions. Thanks to the technologies that helped us to achieve these objectives efficiently. Often in the real world, we need to find a suitable trade-off. However, the selection of suitable and reasonable trade-offs is an art within itself. While modeling semantics, we often require a hierarchical or relational (triples) representation. Decentralized systems fail to model semantic interoperability implicitly. To fill this gap the proposed work introduces a semantic federated querying scheme for decentralized systems. The proposed approach utilizes state-of-the-art graph technology to model decentralized Knowledge Graphs. Furthermore, we discussed different scenarios where the proposed methodology leads to a reasonable conclusion.
引用
收藏
页码:585 / 591
页数:7
相关论文
共 50 条
  • [21] Sangrahaka: A Tool for Annotating and Querying Knowledge Graphs
    Terdalkar, Hrishikesh
    Bhattacharya, Arnab
    PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21), 2021, : 1520 - 1524
  • [22] Querying in the Age of Graph Databases and Knowledge Graphs
    Arenas, Marcelo
    Gutierrez, Claudio
    Sequeda, Juan F.
    SIGMOD '21: PROCEEDINGS OF THE 2021 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, 2021, : 2821 - 2828
  • [23] FedDKD: Federated learning with decentralized knowledge distillation
    Li, Xinjia
    Chen, Boyu
    Lu, Wenlian
    APPLIED INTELLIGENCE, 2023, 53 (15) : 18547 - 18563
  • [24] Personalized Decentralized Federated Learning with Knowledge Distillation
    Jeong, Eunjeong
    Kountouris, Marios
    ICC 2023-IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, 2023, : 1982 - 1987
  • [25] FedDKD: Federated learning with decentralized knowledge distillation
    Xinjia Li
    Boyu Chen
    Wenlian Lu
    Applied Intelligence, 2023, 53 : 18547 - 18563
  • [26] Diversified Top-k Querying in Knowledge Graphs
    Guo, Xintong
    Gao, Hong
    An, Yinan
    Zou, Zhaonian
    WEB AND BIG DATA, PT I, APWEB-WAIM 2020, 2020, 12317 : 319 - 336
  • [27] GQBE: Querying Knowledge Graphs by Example Entity Tuples
    Jayaram, Nandish
    Gupta, Mahesh
    Khan, Arijit
    Li, Chengkai
    Yan, Xifeng
    Elmasri, Ramez
    2014 IEEE 30TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE), 2014, : 1250 - 1253
  • [28] Automated Query Graph Generation for Querying Knowledge Graphs
    Zheng, Weiguo
    Zhang, Mei
    PROCEEDINGS OF THE 30TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, CIKM 2021, 2021, : 2698 - 2707
  • [29] Enabling Web-Scale Knowledge Graphs Querying
    Azzam, Amr
    SEMANTIC WEB: ESWC 2020 SATELLITE EVENTS, 2020, 12124 : 229 - 239
  • [30] DECENTRALIZED FEDERATED LEARNING VIA MUTUAL KNOWLEDGE DISTILLATION
    Huang, Yue
    Kong, Lanju
    Li, Qingzhong
    Zhang, Baochen
    2023 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, ICME, 2023, : 342 - 347