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Knowl. Discov. Data"],"published-print":{"date-parts":[[2025,2,28]]},"abstract":"<jats:p>\n            Designing a proper scoring function is the key to ensuring the excellent performance of knowledge base (KB) embedding. Recently, the scoring function search method introduces the automated machine learning technique to design the data-aware scoring function for the given binary relational data (a.k.a. knowledge graph, KG), which can consistently achieve good performance on different data sets. However, the current data-aware search method is still not as good as desired. First, the existing model can only search scoring functions on the given binary relational data, which is a special form of N-ary relational KBs. Second, observing that existing scoring functions can exhibit distinct performance on different semantic patterns, we are motivated to explore such semantics by searching pattern-aware scoring functions. Unfortunately, it is hard to extend existing search approaches to the scenarios of N-ary and pattern-aware due to the search efficiency and effectiveness issues. In this paper, we propose latent-based factors to model relational patterns and an efficient search algorithm on the N-ary scenario, i.e., efficient\n            <jats:italic>LA<\/jats:italic>\n            tent-based\n            <jats:italic>SCO<\/jats:italic>\n            ring function search for N-ary relational KBs (LASCO). The empirical results of LASCO on binary and N-ary relational data sets demonstrate that the proposed method can efficiently search pattern-aware scoring functions and achieve better embedding performance than advanced baselines.\n          <\/jats:p>","DOI":"10.1145\/3707644","type":"journal-article","created":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T14:12:48Z","timestamp":1733926368000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient Latent-based Scoring Function Search for N-ary Relational Knowledge Bases"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7394-0082","authenticated-orcid":false,"given":"Shimin","family":"Di","sequence":"first","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong SAR, China and Southeast University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2085-7418","authenticated-orcid":false,"given":"Yongqi","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8944-8618","authenticated-orcid":false,"given":"Quanming","family":"Yao","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6343-1455","authenticated-orcid":false,"given":"Xiaofang","family":"Zhou","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong SAR, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7918-8936","authenticated-orcid":false,"given":"Lei","family":"Chen","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2025,2,6]]},"reference":[{"key":"e_1_3_2_2_2","article-title":"Adaptive stochastic natural gradient method for one-shot neural architecture search","author":"Akimoto Youhei","year":"2019","unstructured":"Youhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari, Kento Uchida, Shota Saito, and Kouhei Nishida. 2019. 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