Entity alignment, as an important research direction in knowledge graph research, aims to connect different entities pointing to the same real-world object in different knowledge graphs, and thus to achieve the expansion of knowledge graphs. At present, there are two mainstream research approaches in this field. One is to analyze the structural characteristics of knowledge graphs, and the other is to analyze the element information (such as entity name, relation name, attribute name) of knowledge graphs. In this article, a novel entity alignment model EAFF (Entity Alignment based on Feature Fusion) is proposed to analyze the features of knowledge graphs from the perspectives of graph structure and element information. First, a graph neural networkbased entity alignment algorithm was designed to obtain aligned entity pairs based on graph structures. Then, an entity alignment algorithm based on element information was designed to obtain aligned entity pairs based on element information. Finally, using feature transformation and sorting algorithms, two sets of aligned entity pairs are sorted to obtain aligned entities in the knowledge graph. In the experiment, EAFF achieved relatively good results, surpassing current mainstream algorithms.
[1] Chen M H, Tian Y T, Yang M H, et al. Multilingual knowledge graph embeddings for cross-lingual knowledge alignment[EB/OL]. [2024-02-04]. http://arxiv.org/abs/1611.03954.
[2] Yang Y H, Huang C, Xia L H, et al. Knowledge graph self-supervised rationalization for recommendation[C]//Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York: ACM, 2023: 3046-3056.
[3] Liang K, Liu Y, Zhou S H, et al. Knowledge graph contrastive learning based on relation-symmetrical structure [J]. IEEE Transactions on Knowledge and Data Engineering, 2024, 36(1): 226-238.
[4] Fanourakis N, Efthymiou V, Kotzinos D, et al. Knowledge graph embedding methods for entity alignment: Experimental review[J]. Data Mining and Knowledge Discovery, 2023, 37(5): 2070-2137.
[5] Cao J H, Fang J Y, Meng Z Q, et al. Knowledge graph embedding: A survey from the perspective of representation spaces[J]. ACM Computing Surveys, 2024, 56(6): 1-42.
[6] Munne R F, Ichise R. Entity alignment via summary and attribute embeddings[J]. Logic Journal of the IGPL, 2023, 31(2): 314-324.
[7] Xu C H, Cheng J W, Zhang F. P-NAL: An effective and interpretable entity alignment method[EB/OL]. [2024-02-06]. http://arxiv.org/abs/2404.11968.
[8] Chen H J, Deng S M, Zhang W, et al. Neural symbolic reasoning with knowledge graphs: Knowledge extraction, relational reasoning, and inconsistency checking[J]. Fundamental Research, 2021, 1(5): 565-573.
[9] Jiang T T, Bu C Y, Zhu Y, et al. Combining embeddingbased and symbol-based methods for entity alignment[J]. Pattern Recognition, 2022, 124: 108433.
[10] Liu X, Hong H Y, Wang X H, et al. SelfKG: Self-supervised entity alignment in knowledge graphs[C]//Proceedings of the ACM Web Conference 2022. New York: ACM, 2022: 860-870.
[11] Bordes A, Usunier N, Garcia-Duran A, et al. Translating embeddings for modeling multi-relational data[J]. Advances in neural information processing systems, 2013, 26: 1-9.
[12] Wang Z, Zhang J, Feng J, et al. Knowledge graph embedding by translating on hyperplanes[C]//Proceedings of the AAAI conference on artificial intelligence. California USA: Palo Alto, 2014: 1112-1119.
[13] Lin Y K, Liu Z Y, Sun M S, et al. Learning entity and relation embeddings for knowledge graph completion[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2015, 29(1): 2181-2187.
[14] Guo L B, Sun Z Q, Hu W. Learning to exploit long-term relational dependencies in knowledge graphs[EB/OL]. [2024-02-22]. http://arxiv.org/abs/1905.04914.
[15] Xiao H, Huang M L, Hao Y, et al. TransA: An adaptive approach for knowledge graph embedding[EB/OL]. [2024-02-22]. http://arxiv.org/abs/1509.05490.
[16] Shen L S, He R B, Huang S B. Entity alignment with adaptive margin learning knowledge graph embedding[J]. Data & Knowledge Engineering, 2022, 139: 101987.
[17] Li Y J, Tarlow D, Brockschmidt M, et al. Gated graph sequence neural networks[EB/OL]. [2024-02-22]. http://arxiv.org/abs/1511.05493.
[18] Mao X, Wang W, Xu H, et al. Relational reflection entity alignment[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020: 1095-1104.
[19] Wu Y T, Liu X, Feng Y S, et al. Neighborhood matching network for entity alignment[EB/OL]. [2024-02-25]. http://arxiv.org/abs/2005.05607.
[20] Huang J, Wang J Q, Li Y H, et al. A survey of entity alignment of knowledge graph based on embedded representation[J]. Journal of Physics: Conference Series, 2022, 2171(1): 012050.
[21] Zeng W X, Zhao X, Tang J Y, et al. Collective entity alignment via adaptive features[C]//Proceedings of IEEE 36th International Conference on Data Engineering (ICDE). Piscataway, NJ: IEEE, 2020: 1870-1873.
[22] Yang H W, Zou Y Y, Shi P, et al. Aligning cross-lingual entities with multi-aspect information[EB/OL]. [2024-03-02]. http://arxiv.org/abs/1910.06575.
[23] Tang X, Zhang J, Chen B, et al. BERT-INT: A BERTbased interaction model for knowledge graph alignment [J]. interactions, 2020, 100: e1.
[24] Li Y J, Gu C J, Dullien T, et al. Graph matching networks for learning the similarity of graph structured objects[EB/OL]. [2024-03-15]. http://arxiv.org/abs/1904.12787
[25] Sun Z Q, Wang C M, Hu W, et al. Knowledge graph alignment network with gated multi-hop neighborhood aggregation[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(1): 222-229.
[26] Fey M, Lenssen J E, Morris C, et al. Deep graph matching consensus[EB/OL]. [2024-03-22]. http://arxiv.org/abs/2001.09621.
[27] Chen B, Zhang J, Tang X B, et al. JarKA: modeling attribute interactions for cross-lingual knowledge alignment [C]//Pacific-Asia Conference on Knowledge Discovery and Data Mining. Cham: Springer, 2020: 845-856.
[28] Liu B, Song R Y, Xiang Y J, et al. Self-supervised entity alignment based on multi-modal contrastive learning [J]. CAA Journal of Automatica Sinica, 2022, 9(11): 2031-2033.
[29] Guo L B, Chen Z, Chen J Y, et al. Revisit and outstrip entity alignment: A perspective of generative models[EB/ OL]. [2024-04-22]. http://arxiv.org/abs/2305.14651.
[30] Chen Z, Guo L B, Fang Y, et al. Rethinking uncertainly missing and ambiguous visual modality in multi-modal entity alignment[C]//International Semantic Web Conference. Cham: Springer, 2023: 121-139.
[31] Chen Z, Chen J Y, Zhang W, et al. MEAformer: Multimodal entity alignment transformer for meta modality hybrid[C]//Proceedings of the 31st ACM International Conference on Multimedia. New York: ACM, 2023: 3317-3327.
[32] Wang Y Y, Sun H F, Wang J B, et al. Towards semantic consistency: Dirichlet energy driven robust multi-modal entity alignment[EB/OL]. [2024-05-25]. http://arxiv.org/abs/2401.17859.
[33] Yang L Y, Chen H Y, Wang X, et al. Two heads are better than one: Integrating knowledge from knowledge graphs and large language models for entity alignment [EB/OL]. [2024-06-26]. http://arxiv.org/abs/2401.16960.
