{"type":"ScholarlyArticle","title":"Data Poisoning Attack against Knowledge Graph Embedding","authors":[{"name":"Hengtong Zhang","slug":"hengtong-zhang","given":"Hengtong","family":"Zhang","affiliation":"SUNY at Buffalo , Buffalo , NY USA"},{"name":"Tianhang Zheng","slug":"tianhang-zheng","given":"Tianhang","family":"Zheng","affiliation":"SUNY at Buffalo , Buffalo , NY USA"},{"name":"Jing Gao","slug":"jing-gao","given":"Jing","family":"Gao","affiliation":"SUNY at Buffalo , Buffalo , NY USA"},{"name":"Chenglin Miao","slug":"chenglin-miao","given":"Chenglin","family":"Miao","affiliation":"SUNY at Buffalo , Buffalo , NY USA"},{"name":"Lu Su","slug":"lu-su","given":"Lu","family":"Su","affiliation":"SUNY at Buffalo , Buffalo , NY USA"},{"name":"Yaliang Li","slug":"yaliang-li","given":"Yaliang","family":"Li","affiliation":"Alibaba Group , Bellevue , WA USA"},{"name":"Kui Ren","slug":"kui-ren","given":"Kui","family":"Ren","affiliation":"Zhejiang University , Zhejiang , China"}],"abstract":"Knowledge graph embedding (KGE) is a technique for learning continuous embeddings for entities and relations in the knowledge graph. Due to its benefit to a variety of downstream tasks such as knowledge graph completion, question answering and recommendation, KGE has gained significant attention recently. Despite its effectiveness in a benign environment, KGE's robustness to adversarial attacks is not well-studied. Existing attack methods on graph data cannot be directly applied to attack the embeddings of knowledge graph due to its heterogeneity. To fill this gap, we propose a collection of data poisoning attack strategies, which can effectively manipulate the plausibility of arbitrary targeted facts in a knowledge graph by adding or deleting facts on the graph. The effectiveness and efficiency of the proposed attack strategies are verified by extensive evaluations on two widely-used benchmarks.","doi":"10.24963/ijcai.2019/674","url":"https://ijcai.org/proceedings/2019/674","pdf":"https://ijcai.org/proceedings/2019/0674.pdf","markdown":"https://ijcai.org/proceedings/2019/674.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/674","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":674,"pages":{"first":4853,"last":4859},"keywords":["Multidisciplinary Topics and Applications: Security and Privacy","Machine Learning: Adversarial Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Biggio et al., 2012] Battista Biggio, Blaine Nelson, and Pavel Laskov. 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