{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T16:10:54Z","timestamp":1777651854505,"version":"3.51.4"},"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":[[2017,8]]},"abstract":"<jats:p>The objective of knowledge graph embedding is to encode both entities and relations of knowledge graphs into continuous low-dimensional vector spaces. Previously, most works focused on symbolic representation of knowledge graph with structure information, which can not handle  new entities or entities with few facts well. In this paper, we propose a novel deep architecture to utilize both structural and textual information of entities. Specifically, we introduce three neural models to encode the valuable information from text description of entity, among which an attentive model can select related information as needed. Then, a gating mechanism is applied to integrate representations of structure and text into a unified architecture. Experiments show that our models outperform baseline and obtain state-of-the-art results on link prediction and triplet classification tasks.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/183","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T09:14:07Z","timestamp":1501233247000},"page":"1318-1324","source":"Crossref","is-referenced-by-count":64,"title":["Knowledge Graph Representation with Jointly Structural and Textual Encoding"],"prefix":"10.24963","author":[{"given":"Jiacheng","family":"Xu","sequence":"first","affiliation":[{"name":"Software School, Fudan University, Shanghai, China"},{"name":"Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xipeng","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University, Shanghai, China"},{"name":"Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University, Shanghai, China"},{"name":"Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuanjing","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University, Shanghai, China"},{"name":"Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","theme":"Artificial Intelligence","location":"Melbourne, Australia","acronym":"IJCAI-2017","number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"start":{"date-parts":[[2017,8,19]]},"end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T11:52:39Z","timestamp":1501242759000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/183"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/183","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}