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Volume 9 Issue 11
Nov.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2022  >  9(11): 1990-2004
L. Y. Yang, C. Lv, X. Wang, J. Qiao, W. P. Ding, J. Zhang, and F.-Y. Wang, “Collective entity alignment for knowledge fusion of power grid dispatching knowledge graphs,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 11, pp. 1990–2004, Nov. 2022. doi: 10.1109/JAS.2022.105947
Citation: L. Y. Yang, C. Lv, X. Wang, J. Qiao, W. P. Ding, J. Zhang, and F.-Y. Wang, “Collective entity alignment for knowledge fusion of power grid dispatching knowledge graphs,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 11, pp. 1990–2004, Nov. 2022. doi: 10.1109/JAS.2022.105947
shu

Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs

doi: 10.1109/JAS.2022.105947
  • 1.

    State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, and also with the School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China

  • 2.

    China Electric Power Research Institute, Beijing 100192, China

  • 3.

    State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, and also with the Qingdao Academy of Intelligent Industries, Qingdao 256200, China

  • 4.

    School of Information Science and Technology, Nantong University, Nantong 226019, China

  • 5.

    School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

Funds:  This work was supported by the National Key R&D Program of China (2018AAA0101502) and the Science and Technology Project of SGCC (State Grid Corporation of China): Fundamental Theory of Human-in-the-Loop Hybrid-Augmented Intelligence for Power Grid Dispatch and Control
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    Abstract
  • Knowledge graphs (KGs) have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services. In recent years, researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power grids. With multiple power grid dispatching knowledge graphs (PDKGs) constructed by different agencies, the knowledge fusion of different PDKGs is useful for providing more accurate decision supports. To achieve this, entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical step. Existing entity alignment methods cannot integrate useful structural, attribute, and relational information while calculating entities’ similarities and are prone to making many-to-one alignments, thus can hardly achieve the best performance. To address these issues, this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart assignments. This model proposes a novel knowledge graph attention network (KGAT) to learn the embeddings of entities and relations explicitly and calculates entities’ similarities by adaptively incorporating the structural, attribute, and relational similarities. Then, we formulate the counterpart assignment task as an integer programming (IP) problem to obtain one-to-one alignments. We not only conduct experiments on a pair of PDKGs but also evaluate our model on three commonly used cross-lingual KGs. Experimental comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.

     

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    • References(50)
  • [1]
    A. Almalaq, J. Hao, J. J. Zhang, and F.-Y. Wang, “Parallel building: A complex system approach for smart building energy management,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1452–1461, 2019. doi: 10.1109/JAS.2019.1911768
    [2]
    T. Liu, B. Tian, Y. Ai, and F.-Y. Wang, “Parallel reinforcement learning-based energy efficiency improvement for a cyber-physical system,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 617–626, 2020. doi: 10.1109/JAS.2020.1003072
    [3]
    J. J. Zhang, D. W. Gao, Y. Zhang, X. Wang, X. Zhao, D. Duan, X. Dai, J. Hao, and F.-Y. Wang, “Social energy: Mining energy from the society,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 466–482, 2017. doi: 10.1109/JAS.2017.7510547
    [4]
    D. Xiang, G. Liu, C. Yan, and C. Jiang, “Detecting data-flow errors based on petri nets with data operations,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 251–260, 2018. doi: 10.1109/JAS.2017.7510766
    [5]
    Y. Tang, T. Liu, G. Liu, J. Li, R. Dai, and C. Yuan, “Enhancement of power equipment management using knowledge graph,” in Proc. IEEE Innovative Smart Grid Technologies–Asia, May 2019, pp. 905–910.
    [6]
    C. Huang, J. Gu, H. Liu, Y. Lu, and J. Luo, “Economical optimization of grid power factor using predictive data,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 258–267, 2019. doi: 10.1109/JAS.2017.7510691
    [7]
    Z. Li, S. Li, and X. Luo, “An overview of calibration technology of industrial robots,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 23–36, 2021. doi: 10.1109/JAS.2020.1003381
    [8]
    A. J. Hepworth, D. P. Baxter, A. Hussein, K. J. Yaxley, E. Debie, and H. A. Abbass, “Human-swarm-teaming transparency and trust architecture,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1281–1295, 2021. doi: 10.1109/JAS.2020.1003545
    [9]
    J. J. Zhang, F.-Y. Wang, Q. Wang, D. Hao, X. Yang, D. W. Gao, X. Zhao, and Y. Zhang, “Parallel dispatch: A new paradigm of electrical power system dispatch,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 311–319, 2018. doi: 10.1109/JAS.2017.7510778
    [10]
    S. Gao, Z. Yu, L. Shi, X. Yan, and H. Song, “Review expert collaborative recommendation algorithm based on topic relationship,” IEEE/CAA J. Autom. Sinica, vol. 2, no. 4, pp. 403–411, 2015. doi: 10.1109/JAS.2015.7296535
    [11]
    Y. Xian, Z. Fu, S. Muthukrishnan, G. de Melo, and Y. Zhang, “Reinforcement knowledge graph reasoning for explainable recommendation,” in Proc. 42nd Int. ACM SIGIR Conf. Research and Development in Information Retrieval, Jul. 2019, pp. 285–294.
    [12]
    J. Zheng, Y. Wang, and S. Chen, “Dynamic case retrieval method with subjective preferences and objective information for emergency decision making,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 3, pp. 749–757, 2018. doi: 10.1109/JAS.2016.7510232
    [13]
    S. Fan, X. Liu, Y. Chen, Z. Liao, Y. Zhao, H. Luo, and H. Fan, “How to construct a power knowledge graph with dispatching data?” Scientific Programming, vol. 2020, pp. 1–10, 2020.
    [14]
    Y. Dai, Y. Chen, X. Li, S. Zhang, Q. Chen, S. Liao, and J. Zhang, “Automatic generation of power grid dispatching and control scheme based on heterogeneous information network,” in Proc. IEEE 4th Conf. Energy Internet and Energy System Integration, Oct. 2020, pp. 3028–3032.
    [15]
    L. Yang, X. Wang, Y. Dai, K. Xin, X. Zheng, W. Ding, J. Zhang, and F.-Y. Wang, “Hackrl: Reinforcement learning with hierarchical attention for cross-graph knowledge fusion and collaborative reasoning,” Knowledge-Based Systems, vol. 233, p. 107498, 2021.
    [16]
    L. Hu, G. Wu, Y. Xing, and F. Wang, “Things2Vec: Semantic modeling in the Internet of Things with graph representation learning,” IEEE Internet of Things Journal, vol. 7, no. 3, pp. 1939–1948, 2020. doi: 10.1109/JIOT.2019.2962630
    [17]
    M. Chen, Y. Tian, M. Yang, and C. Zaniolo, “Multilingual knowledge graph embeddings for cross-lingual knowledge alignment,” in Proc. 26th Int. Joint Conf. Artificial Intelligence, Aug. 2017, pp. 1511–1517.
    [18]
    H. Zhu, R. Xie, Z. Liu, and M. Sun, “Iterative entity alignment via joint knowledge embeddings,” in Proc. 26th Int. Joint Conf. Artificial Intelligence, Aug. 2017, pp. 4258–4264.
    [19]
    Z. Wang, Q. Lv, X. Lan, and Y. Zhang, “Cross-lingual knowledge graph alignment via graph convolutional networks,” in Proc. Conf. Empirical Methods in Natural Language Processing, Oct. 2018, pp. 349–357.
    [20]
    C. Li, Y. Cao, L. Hou, J. Shi, J. Li, and T.-S. Chua, “Semi-supervised entity alignment via joint knowledge embedding model and cross-graph model,” in Proc. Conf. Empirical Methods in Natural Language Processing and the 9th Int. Joint Conf. Natural Language Processing, Nov. 2019, pp. 2723–2732.
    [21]
    Y. Cao, Z. Liu, C. Li, Z. Liu, J. Li, and T.-S. Chua, “Multi-channel graph neural network for entity alignment,” in Proc. 57th Annu. Meeting of the Association for Computational Linguistics, Jul. 2019, pp. 1452–1461.
    [22]
    L. Guo, Z. Sun, and W. Hu, “Learning to exploit long-term relational dependencies in knowledge graphs,” in Proc. 36th Int. Conf. Machine Learning, Jun. 2019, pp. 2505–2514.
    [23]
    Z. Sun, W. Hu, and C. Li, “Cross-lingual entity alignment via joint attribute-preserving embedding,” in Proc. 16th Int. Semantic Web Conf., Oct. 2017, pp. 628–644.
    [24]
    Y. Peng, J. Zhang, C. Zhou, and J. Xu, “Embedding-based entity alignment using relation structural similarity,” in Proc. IEEE Int. Conf. Knowledge Graph, Aug. 2020, pp. 123–130.
    [25]
    Y. Wu, X. Liu, Y. Feng, Z. Wang, and D. Zhao, “Jointly learning entity and relation representations for entity alignment,” in Proc. Conf. Empirical Methods in Natural Language Processing and the 9th Int. Joint Conf. Natural Language Processing, Nov. 2019, pp. 240–249.
    [26]
    W. Zeng, X. Zhao, W. Wang, J. Tang, and Z. Tan, “Degree-aware alignment for entities in tail,” in Proc. 43rd Int. ACM SIGIR Conf. Research and Development in Information Retrieval, Jul. 2020, pp. 811–820.
    [27]
    X. Chen, S. Jia, and Y. Xiang, “A review: Knowledge reasoning over knowledge graph,” Expert Systems with Applications, vol. 141, pp. 1–21, 2020.
    [28]
    A. Bordes, N. Usunier, A. Garcia-Durán, J. Weston, and O. Yakhnenko, “Translating embeddings for modeling multi-relational data,” in Proc. 26th Int. Conf. Neural Information Processing Systems, Dec. 2013, pp. 2787–2795.
    [29]
    Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu, “Learning entity and relation embeddings for knowledge graph completion,” in Proc. 29th AAAI Conf. Artificial Intelligence, Jan. 2015, pp. 2181–2187.
    [30]
    G. Ji, S. He, L. Xu, K. Liu, and J. Zhao, “Knowledge graph embedding via dynamic mapping matrix,” in Proc. 53rd Annu. Meeting of the Association for Computational Linguistics and 7th Int. Joint Conf. Natural Language Processing, Jul. 2015, pp. 687–696.
    [31]
    M. Nickel, V. Tresp, and H.-P. Kriegel, “A three-way model for collective learning on multi-relational data,” in Proc. 28th Int. Conf. Machine Learning, Jun. 2011, pp. 809–816.
    [32]
    B. Yang, S. W.-t. Yih, X. He, J. Gao, and L. Deng, “Embedding entities and relations for learning and inference in knowledge bases,” in Proc. 3rd Int. Conf. Learning Representations, May 2015, pp. 1–13.
    [33]
    T. Trouillon, J. Welbl, S. Riedel, E. Gaussier, and G. Bouchard, “Complex embeddings for simple link prediction,” in Proc. 33rd Int. Conf. Machine Learning, vol. 48, Jun. 2016, pp. 2071–2080.
    [34]
    A. Bordes, X. Glorot, J. Weston, and Y. Bengio, “A semantic matching energy function for learning with multi-relational data,” Machine Learning, vol. 94, no. 2, pp. 233–259, 2014. doi: 10.1007/s10994-013-5363-6
    [35]
    Y. Zhuang, G. Li, Z. Zhong, and J. Feng, “Hike: A hybrid human-machine method for entity alignment in large-scale knowledge bases,” in Proc. ACM Conf. Information and Knowledge Management, Nov. 2017, pp. 1917–1926.
    [36]
    Z. Sun, W. Hu, Q. Zhang, and Y. Qu, “Bootstrapping entity alignment with knowledge graph embedding,” in Proc. 27th Int. Joint Conf. Artificial Intelligence, Jul. 2018, pp. 4396–4402.
    [37]
    Z. Sun, J. Huang, W. Hu, M. Chen, L. Guo, and Y. Qu, “Transedge: Translating relation-contextualized embeddings for knowledge graphs,” in Proc. 18th Int. Semantic Web Conf., Oct. 2019, pp. 612–629.
    [38]
    Z. Sun, C. Wang, W. Hu, M. Chen, J. Dai, W. Zhang, and Y. Qu, “Knowledge graph alignment network with gated multi-hop neighborhood aggregation,” in Proc. 34th AAAI Conf., Feb. 2020, pp. 222–229.
    [39]
    Y. Wu, X. Liu, Y. Feng, Z. Wang, and D. Zhao, “Neighborhood matching network for entity alignment,” in Proc. 58th Annu. Meeting of the Association for Computational Linguistics, Jul. 2020, pp. 6477–6487.
    [40]
    X. Mao, W. Wang, H. Xu, M. Lan, and Y. Wu, “Mraea: An efficient and robust entity alignment approach for cross-lingual knowledge graph,” in Proc. 13th Int. Conf. Web Search and Data Mining, Jan. 2020, pp. 420–428.
    [41]
    R. Ye, X. Li, Y. Fang, H. Zang, and M. Wang, “A vectorized relational graph convolutional network for multi-relational network alignment,” in Proc. 28th Int. Joint Conf. Artificial Intelligence, Aug. 2019, pp. 4135–4141.
    [42]
    Y. Wu, X. Liu, Y. Feng, Z. Wang, R. Yan, and D. Zhao, “Relation-aware entity alignment for heterogeneous knowledge graphs,” in Proc. 28th Int. Joint Conf. Artificial Intelligence, Aug. 2019, pp. 5278–5284.
    [43]
    K. Xu, L. Wang, M. Yu, Y. Feng, Y. Song, Z. Wang, and D. Yu, “Cross-lingual knowledge graph alignment via graph matching neural network,” in Proc. 57th Annual Meeting of the Association for Computational Linguistics, Jul. 2019, pp. 3156–3161.
    [44]
    K. Xu, L. Song, Y. Feng, Y. Song, and D. Yu, “Coordinated reasoning for cross-lingual knowledge graph alignment,” in Proc. 34th AAAI Conf., Feb. 2020, pp. 9354–9361.
    [45]
    W. Zeng, X. Zhao, J. Tang, and X. Lin, “Collective entity alignment via adaptive features,” in Proc. IEEE 36th Int. Conf. Data Engineering, Apr. 2020, pp. 1870–1873.
    [46]
    T. T. Nguyen, T. T. Huynh, H. Yin, V. V. Tong, D. Sakong, B. Zheng, and Q. V. H. Nguyen, “Entity alignment for knowledge graphs with multi-order convolutional networks,” IEEE Trans. Knowledge and Data Engineering, pp. 1–14, 2020.
    [47]
    S. Li, H. Wang, R. Pan, and M. Mao, “Memorypath: A deep reinforcement learning framework for incorporating memory component into knowledge graph reasoning,” Neurocomputing, vol. 419, pp. 273–286, 2021. doi: 10.1016/j.neucom.2020.08.032
    [48]
    A. E. Roth, “Deferred acceptance algorithms: History, theory, practice, and open questions,” Int. Journal of Game Theory, vol. 36, no. 3, pp. 537–569, 2008.
    [49]
    W. Zeng, X. Zhao, J. Tang, X. Lin, and P. Groth, “Reinforcement learning based collective entity alignment with adaptive features,” arXiv preprint arXiv: 2101.01353, 2021.
    [50]
    X. Du, J. Yan, and H. Zha, “Joint link prediction and network alignment via cross-graph embedding,” in Proc. 28th Int. Joint Conf. Artificial Intelligence, Aug. 2019, pp. 2251–2257.
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    Highlights

    • A collective entity alignment model for the knowledge fusion of multiple power grid dispatching knowledge graphs is proposed
    • A novel knowledge graph attention network is proposed to learn the structural relatedness of entities and relations explicitly
    • The structural, attribute, and relational similarities between entities are adaptively integrated to obtain the synthesized similarities
    • Entities are collectively aligned with consistency and exclusiveness constraints based on a novel integer programming-based counterpart assignment strategy

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