{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T02:00:21Z","timestamp":1777341621004,"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":[[2025,9]]},"abstract":"<jats:p>Conducting complex logical queries over knowledge graphs remains a significant challenge. Recent research has successfully leveraged Pre-trained Language Models (PLMs) to tackle Knowledge Graph Complex Query Answering (KGCQA) tasks, which is attributed to PLMs' ability to comprehend logical semantics of queries through context learning. However, existing PLM-based KGCQA methods usually overlook the harm of disordered syntax or fragmented contexts within a serialized query, posing the problem of \u201cimpossible language\u201d to limit PLMs in grasping the logical semantics. To address this problem, we propose a Progressive Prefix-Memory Tuning (PPMT) framework for KGCQA tasks, which effectively rectifies erroneous segments in serialized queries to assist PLMs in query answering. First, we propose a prefix-memory rectification mechanism embedded in a PLM module. This mechanism assigns rectification parameters in memory stores to polish the language segments of entities, relations, and queries through specific prefixes. To further capture the logical semantics in queries, we design a progressive fine-tuning strategy, which optimizes our model through a conditional gradient update process guided by knowledge translation constraints. Extensive experiments on widely used KGCQA benchmarks demonstrate the significant superiority of PPMT in terms of HR@3 and MRR. Our codes are available at https:\/\/github.com\/lazyloafer\/PPMT.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/413","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"3716-3724","source":"Crossref","is-referenced-by-count":2,"title":["Progressive Prefix-Memory Tuning for Complex Logical Query Answering on Knowledge Graphs"],"prefix":"10.24963","author":[{"given":"Xingrui","family":"Zhuo","sequence":"first","affiliation":[{"name":"The Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), School of Computer Science and Information Engineering, Hefei University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shirui","family":"Pan","sequence":"additional","affiliation":[{"name":"Griffith University, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiapu","family":"Wang","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), School of Computer Science and Information Engineering, Hefei University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gongqing","family":"Wu","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), School of Computer Science and Information Engineering, Hefei University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zan","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), School of Computer Science and Information Engineering, Hefei University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Li","sequence":"additional","affiliation":[{"name":"Shandong Inspur Science Research Institute, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zizhong","family":"Wei","sequence":"additional","affiliation":[{"name":"Shandong Inspur Science Research Institute, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xindong","family":"Wu","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), School of Computer Science and Information Engineering, Hefei University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2025","number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2025,8,16]]},"end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:33:58Z","timestamp":1758627238000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/413"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/413","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}