{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T22:06:03Z","timestamp":1767650763235},"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":[[2023,8]]},"abstract":"<jats:p>Most systems integrating data-driven machine learning with knowledge-driven reasoning usually rely on a specifically designed knowledge base to enable efficient symbolic inference. However, it could be cumbersome for the nonexpert end-users to prepare such a knowledge base in real tasks. Recent years have witnessed the success of large-scale knowledge graphs, which could be ideal domain knowledge resources for real-world machine learning tasks. However, these large-scale knowledge graphs usually contain much information that is irrelevant to a specific learning task. Moreover, they often contain a certain degree of noise. Existing methods can hardly make use of them because the large-scale probabilistic logical inference is usually intractable. To address these problems, we present ABductive Learning with Knowledge Graph (ABL-KG) that can automatically mine logic rules from knowledge graphs during learning, using a knowledge forgetting mechanism for filtering out irrelevant information. Meanwhile, these rules can form a logic program that enables efficient joint optimization of the machine learning model and logic inference within the Abductive Learning (ABL) framework. Experiments on four different tasks show that ABL-KG can automatically extract useful rules from large-scale and noisy knowledge graphs, and significantly improve the performance of machine learning with only a handful of labeled data.<\/jats:p>","DOI":"10.24963\/ijcai.2023\/427","type":"proceedings-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:31:30Z","timestamp":1691742690000},"page":"3839-3847","source":"Crossref","is-referenced-by-count":7,"title":["Enabling Abductive Learning to Exploit Knowledge Graph"],"prefix":"10.24963","author":[{"given":"Yu-Xuan","family":"Huang","sequence":"first","affiliation":[{"name":"Nanjing University"}]},{"given":"Zequn","family":"Sun","sequence":"additional","affiliation":[{"name":"Nanjing University"}]},{"given":"Guangyao","family":"Li","sequence":"additional","affiliation":[{"name":"Nanjing University"}]},{"given":"Xiaobin","family":"Tian","sequence":"additional","affiliation":[{"name":"Nanjing University"}]},{"given":"Wang-Zhou","family":"Dai","sequence":"additional","affiliation":[{"name":"Nanjing University"}]},{"given":"Wei","family":"Hu","sequence":"additional","affiliation":[{"name":"Nanjing University"}]},{"given":"Yuan","family":"Jiang","sequence":"additional","affiliation":[{"name":"Nanjing University"}]},{"given":"Zhi-Hua","family":"Zhou","sequence":"additional","affiliation":[{"name":"Nanjing University"}]}],"member":"10584","event":{"number":"32","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2023","name":"Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}","start":{"date-parts":[[2023,8,19]]},"theme":"Artificial Intelligence","location":"Macau, SAR China","end":{"date-parts":[[2023,8,25]]}},"container-title":["Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:48:18Z","timestamp":1691743698000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2023\/427"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2023\/427","relation":{},"subject":[],"published":{"date-parts":[[2023,8]]}}}