{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T08:40:10Z","timestamp":1755852010156,"version":"3.44.0"},"reference-count":50,"publisher":"Association for Computing Machinery (ACM)","issue":"7","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62172300, 62372326, and 62202336"],"award-info":[{"award-number":["62172300, 62372326, and 62202336"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["2024-4-YB-03"],"award-info":[{"award-number":["2024-4-YB-03"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2025,8,31]]},"abstract":"<jats:p>\n            Inductive relation prediction, an important task for knowledge graph completion, is to predict the relations between entities that are unseen at the training stage. The latest methods use Pre-Trained Language Models (PLMs) to encode the paths between the head entity and tail entity and achieve state-of-the-art prediction performance. However, these methods cannot handle no-path scenarios well and lack the capability to learn comprehensive relation representations for distinguishing different relations. To tackle this issue, we propose a novel\n            <jats:italic toggle=\"yes\">R<\/jats:italic>\n            elation-\n            <jats:italic toggle=\"yes\">a<\/jats:italic>\n            ware\n            <jats:italic toggle=\"yes\">k<\/jats:italic>\n            nowledg\n            <jats:italic toggle=\"yes\">e r<\/jats:italic>\n            easoning model entitled Raker, which introduces an adaptive reasoning information extraction method to identify relation-aware reasoning neighbors of entities in the target triple to handle no-path scenarios and enables the PLM to better distinguish different relations via the relation-specific soft prompting. Raker is evaluated on three public datasets and achieves SOTA performance in inductive relation prediction when compared with the baseline methods. Notably, the absolute improvement of Raker is even more than 5% on the FB15k-237 dataset in the inductive setting. Moreover, Raker also demonstrates the superiority in transductive, few-shot, and unseen relation settings. The code of Raker is available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/ADMIS-TONGJI\/Raker\">https:\/\/github.com\/ADMIS-TONGJI\/Raker<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3745029","type":"journal-article","created":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T06:14:09Z","timestamp":1750400049000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Raker: A Relation-Aware Knowledge Reasoning Model for Inductive Relation Prediction"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7293-3156","authenticated-orcid":false,"given":"Jiaqi","family":"Wang","sequence":"first","affiliation":[{"name":"Tongji University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8768-6740","authenticated-orcid":false,"given":"Wengen","family":"Li","sequence":"additional","affiliation":[{"name":"Tongji University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1150-1569","authenticated-orcid":false,"given":"Yulou","family":"Shu","sequence":"additional","affiliation":[{"name":"Tongji University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2313-7635","authenticated-orcid":false,"given":"Jihong","family":"Guan","sequence":"additional","affiliation":[{"name":"Tongji University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9931-4733","authenticated-orcid":false,"given":"Yichao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tongji University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1949-2768","authenticated-orcid":false,"given":"Shuigeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2025,8,21]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1522"},{"key":"e_1_3_1_3_2","first-page":"2787","volume-title":"Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013","author":"Bordes Antoine","year":"2013","unstructured":"Antoine Bordes, Nicolas Usunier, Alberto Garc\u00eda-Dur\u00e1n, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Christopher J. C. Burges, L\u00e9on Bottou, Zoubin Ghahramani, and Kilian Q. Weinberger (Eds.), 2787\u20132795."},{"key":"e_1_3_1_4_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020 (NeurIPS \u201920), Virtual","author":"Brown Tom B.","year":"2020","unstructured":"Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. In Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020 (NeurIPS \u201920), Virtual. Hugo Larochelle, Marc\u2019Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.)."},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/737"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531757"},{"key":"e_1_3_1_7_2","volume-title":"Proceedings of the 6th International Conference on Learning Representations (ICLR \u201918)","author":"Das Rajarshi","year":"2018","unstructured":"Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, and Andrew McCallum. 2018. Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning. In Proceedings of the 6th International Conference on Learning Representations (ICLR \u201918). 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