Abstract
Entity Alignment (EA) aims to find and unite equivalent entities across different knowledge graphs for knowledge fusion. It requires pre-aligned entity pairs as seed alignments to train an EA model. Recent effort has employed active learning (AL) to query more informative seed alignments for effective EA modeling at a lower cost. However, it still challenges existing AL methods to find and diversify seed alignments since true alignments themselves are sparse and unavailable before getting annotated. To address this issue, we manipulate seed alignment query based on entity selection on a single knowledge graph and deploy active learning on the EA task by querying entities that behave with (i) Matching Uncertainty determined by the EA model in training and (ii) Novelty-oriented Uncertainty estimated through diverse entity identification. To adapt the query set to changes in the EA model and aligned entities during AL iterations, we propose a dynamic cascade sampling strategy by trading-off between matching uncertainty and novelty-oriented uncertainty in a two-stage manner. Experiments on real-world benchmark datasets show the effectiveness of the proposed approach in comparison with state-of-the-art methods.
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References
Aggarwal, C.C., Kong, X., Gu, Q., Han, J., Philip, S.Y.: Active learning: a survey. In: Data Classification, pp. 599–634. Chapman and Hall/CRC, Boca Raton (2014)
Berrendorf, M., Faerman, E., Tresp, V.: Active learning for entity alignment. In: Hiemstra, D., Moens, M.-F., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds.) ECIR 2021. LNCS, vol. 12656, pp. 48–62. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72113-8_4
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1–7), 107–117 (1998)
Caramalau, R., Bhattarai, B., Kim, T.K.: Sequential graph convolutional network for active learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9583–9592 (2021)
Chen, M., Tian, Y., Yang, M., Zaniolo, C.: Multilingual knowledge graph embeddings for cross-lingual knowledge alignment. arXiv preprint arXiv:1611.03954 (2016)
Das, K., Samanta, S., Pal, M.: Study on centrality measures in social networks: a survey. Social Netw. Anal. Min. 8, 1–11 (2018)
Gao, Y., Liu, X., Wu, J., Li, T., Wang, P., Chen, L.: ClusterEA: scalable entity alignment with stochastic training and normalized mini-batch similarities. In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 421–431 (2022)
Ge, C., Liu, X., Chen, L., Zheng, B., Gao, Y.: Make it easy: an effective end-to-end entity alignment framework. In: Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 777–786 (2021)
Guo, L., Sun, Z., Hu, W.: Learning to exploit long-term relational dependencies in knowledge graphs. In: International Conference on Machine Learning, pp. 2505–2514. PMLR (2019)
Lehmann, J., et al.: Dbpedia-a large-scale, multilingual knowledge base extracted from wikipedia. Semant. Web 6(2), 167–195 (2015)
Liu, B., Scells, H., Zuccon, G., Hua, W., Zhao, G.: ActiveEA: active learning for neural entity alignment. arXiv preprint arXiv:2110.06474 (2021)
Mahdisoltani, F., Biega, J., Suchanek, F.: Yago3: a knowledge base from multilingual wikipedias. In: Biennial Conference on Innovative Data Systems Research (2014)
Mao, X., Wang, W., Xu, H., Lan, M., Wu, Y.: MRAEA: an efficient and robust entity alignment approach for cross-lingual knowledge graph. In: Proceedings of the International Conference on Web Search and Data Mining, pp. 420–428 (2020)
Ostapuk, N., Yang, J., Cudré-Mauroux, P.: ActiveLink: deep active learning for link prediction in knowledge graphs. In: The World Wide Web Conference, pp. 1398–1408 (2019)
Puthal, D., Nepal, S., Paris, C., Ranjan, R., Chen, J.: Efficient algorithms for social network coverage and reach. In: IEEE International Congress on Big Data, pp. 467–474. IEEE (2015)
Scheffer, T., Decomain, C., Wrobel, S.: Active hidden Markov models for information extraction. In: Hoffmann, F., Hand, D.J., Adams, N., Fisher, D., Guimaraes, G. (eds.) IDA 2001. LNCS, vol. 2189, pp. 309–318. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44816-0_31
Sun, Z., Hu, W., Zhang, Q., Qu, Y.: Bootstrapping entity alignment with knowledge graph embedding. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) (2018)
Sun, Z., Huang, J., Hu, W., Chen, M., Guo, L., Qu, Y.: TransEdge: translating relation-contextualized embeddings for knowledge graphs. In: Ghidini, C., et al. (eds.) ISWC 2019. LNCS, vol. 11778, pp. 612–629. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30793-6_35
Sun, Z., et al.: Knowledge graph alignment network with gated multi-hop neighborhood aggregation. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 222–229 (2020)
Sun, Z., et al.: A benchmarking study of embedding-based entity alignment for knowledge graphs. arXiv preprint arXiv:2003.07743 (2020)
Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014)
Wang, Z., Lv, Q., Lan, X., Zhang, Y.: Cross-lingual knowledge graph alignment via graph convolutional networks. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 349–357 (2018)
Xiong, C., Power, R., Callan, J.: Explicit semantic ranking for academic search via knowledge graph embedding. In: Proceedings of the International Conference on World Wide Web, pp. 1271–1279 (2017)
Yang, L., Zhang, Y., Chen, J., Zhang, S., Chen, D.Z.: Suggestive annotation: a deep active learning framework for biomedical image segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 399–407. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_46
Zeng, W., Zhao, X., Tang, J., Fan, C.: Reinforced active entity alignment. In: Proceedings of the ACM International Conference on Information & Knowledge Management, pp. 2477–2486 (2021)
Zhang, B., Li, L., Yang, S., Wang, S., Zha, Z.J., Huang, Q.: State-relabeling adversarial active learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8756–8765 (2020)
Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma, W.Y.: Collaborative knowledge base embedding for recommender systems. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 353–362 (2016)
Acknowledgment
The work was supported in part by the Major Project of New Generation Artificial Intelligence of the Ministry of Science and Technology of China under Grant 2021ZD0113402 and the National Natural Science Foundation of China under Grant 61976029.
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Xie, J., Li, J., Tan, J., Wang, H. (2023). Cascade Sampling via Dual Uncertainty for Active Entity Alignment. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14118. Springer, Cham. https://doi.org/10.1007/978-3-031-40286-9_15
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