{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T15:42:47Z","timestamp":1772206967148,"version":"3.50.1"},"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":[[2022,7]]},"abstract":"<jats:p>Multi-hop reasoning over real-life knowledge graphs (KGs) is a highly challenging  problem as traditional subgraph matching methods are not capable to deal with noise and missing information. Recently, to address this problem a promising approach based on jointly embedding logical queries and KGs into a low-dimensional space to identify answer entities has emerged. However,  existing proposals ignore critical semantic knowledge inherently available in KGs, such  as  type  information. To  leverage type  information, we  propose a novel type-aware  model, TypE-aware Message Passing (TEMP), which enhances the entity and relation representation in queries, and simultaneously improves generalization, and deductive and inductive reasoning. Remarkably, TEMP is a plug-and-play model that can be easily incorporated into existing embedding-based models to improve their performance. Extensive experiments on three real-world datasets demonstrate TEMP\u2019s effectiveness.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/427","type":"proceedings-article","created":{"date-parts":[[2022,7,16]],"date-time":"2022-07-16T02:55:56Z","timestamp":1657940156000},"page":"3078-3084","source":"Crossref","is-referenced-by-count":17,"title":["Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs"],"prefix":"10.24963","author":[{"given":"Zhiwei","family":"Hu","sequence":"first","affiliation":[{"name":"Shanxi University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Victor","family":"Gutierrez Basulto","sequence":"additional","affiliation":[{"name":"Cardiff University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiliang","family":"Xiang","sequence":"additional","affiliation":[{"name":"Cardiff University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoli","family":"Li","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research , A*STAR, Singapore\/Nanyang Technological University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ru","family":"Li","sequence":"additional","affiliation":[{"name":"Shanxi University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeff","family":"Z. Pan","sequence":"additional","affiliation":[{"name":"The University of Edinburgh"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","theme":"Artificial Intelligence","location":"Vienna, Austria","acronym":"IJCAI-2022","number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2022,7,23]]},"end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T11:09:40Z","timestamp":1658142580000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/427"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/427","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}