{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,13]],"date-time":"2026-11-13T17:58:17Z","timestamp":1794592697518,"version":"4.9.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/674","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"4853-4859","source":"Crossref","is-referenced-by-count":70,"title":["Data Poisoning Attack against Knowledge Graph Embedding"],"prefix":"10.24963","author":[{"given":"Hengtong","family":"Zhang","sequence":"first","affiliation":[{"name":"SUNY at Buffalo, Buffalo, NY USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianhang","family":"Zheng","sequence":"additional","affiliation":[{"name":"SUNY at Buffalo, Buffalo, NY USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Gao","sequence":"additional","affiliation":[{"name":"SUNY at Buffalo, Buffalo, NY USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenglin","family":"Miao","sequence":"additional","affiliation":[{"name":"SUNY at Buffalo, Buffalo, NY USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Su","sequence":"additional","affiliation":[{"name":"SUNY at Buffalo, Buffalo, NY USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaliang","family":"Li","sequence":"additional","affiliation":[{"name":"Alibaba Group, Bellevue, WA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kui","family":"Ren","sequence":"additional","affiliation":[{"name":"Zhejiang University, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:50:58Z","timestamp":1564285858000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/674"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/674","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}