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Sparse KG reasoning, which infers missing knowledge in sparse graphs, remains challenging because information is scarce and path quality is difficult to control. StruProKGR proposes a structural and probabilistic framework that builds interpretable, distance-guided path collections with probabilistic path aggregation. 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The goal is effective, efficient, and interpretable inference under sparsity.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does StruProKGR collect paths more efficiently than random walks?",{"text":118,"@type":114},"It uses a distance-guided path collection mechanism to reduce computational cost while focusing on more relevant paths. This avoids the stochastic inefficiency and variable-quality paths common in random-walk approaches.",{"name":120,"@type":111,"acceptedAnswer":121},"How does StruProKGR improve reasoning beyond independent path processing?",{"text":122,"@type":114},"It incorporates structural information through probabilistic path aggregation that prioritizes paths reinforcing each other. This addresses the limitation of many methods that treat paths independently and miss collective relational patterns.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},273362,1789471071,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},1099513958762,"https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253","StruProKGR: A Structural and Probabilistic Framework for Sparse  \nKnowledge Graph Reasoning  \nYucan Guo, Saiping Guan†, Miao Su, Jiyao Wei, Xiaolong Jin†, Jiafeng Guo, Xueqi Cheng  \n1 State Key Laboratory of AI Safety  \n2Institute of Computing Technology, Chinese Academy of Sciences  \n3University of Chinese Academy of Sciences  \n{guoyucan23z, guansaiping, sumiao22z, weijiyao20z, jinxiaolong, guojiafeng, [cxq}@ict.ac.cn](cxq}@ict.ac.cn)  \narXiv : 25 12 . 126 13v2 [cs .CL] 2 1 Aug 2026  \nAbstract  \nSparse Knowledge Graphs (KGs) are commonly encountered in real-world applications, where knowledge is often incomplete or limited. Sparse KG reasoning, the task of inferring missing knowledge over sparse KGs, is inherently challenging due to the scarcity of knowledge and the difficulty of capturing relational patterns in sparse scenarios. Among all sparse KG reasoning methods, path-based ones have attracted plenty of attention due to their interpretability. Existing path-based methods typically rely on computationally intensive random walks to collect paths, producing paths of variable quality. Additionally, these methods fail to leverage the structured nature of graphs by treating paths independently. To address these shortcomings, we propose a Structural and Probabilistic framework named StruProKGR, tailored for efficient and interpretable reasoning on sparse KGs. StruProKGR utilizes a distanceguided path collection mechanism to significantly reduce computational costs while exploring more relevant paths. It further enhances the reasoning process by incorporating structural information through probabilistic path aggregation, which prioritizes paths that reinforce eachother. Extensive experiments on five sparse KG reasoning benchmarks reveal that StruProKGR surpasses existing path-based methods in both effectiveness and efficiency, providing an effective, efficient, and interpretable solution for sparse KG reasoning.1  \n1 Introduction  \nKnowledge Graphs (KGs) contain facts in the form of triples (head entity, relation, tail entity), denoted as (h, r, t) . They support a variety of downstream applications, including question answering (Agarwal et al., 2024 ; Liu et al., 2025), recommender systems (Wang et al., 2024, 2025), and  \n†Corresponding authors.  \n1The code is publicly available at [https://github.com/](https://github.com/)[ ](https://github.com/)YucanGuo/StruProKGR.  \nFigure 1: An example illustrating sparse KG reasoning.  \ninformation retrieval (Gutiérrez et al., 2024 ; Caiet al., 2025) . In real-world situations, KGs often exhibit sparsity, as many triples are missing due to incomplete knowledge collection. For example, considering Freebase (Bollacker et al., 2008), the well-known open-source KG, 71% of individuals in Freebase have no recorded place of birth, and 75% have no identified nationality (Dong et al., 2014) . Sparse KG reasoning, the task of inferring missing knowledge over sparse KGs, is crucial for uncovering valuable insights in knowledge-scarce scenarios.  \nFigure 1 shows an example of sparse KG reasoning over a highly incomplete KG with six entities and five relations. Solid arrows denote facts that are observed, while the dotted arrows highlight the relations that should be completed, i.e., (Albert Einstein, citizenship, ?) and (Albert Einstein, field of study, ?) . These two relations do not appear explicitly in the observed graph but can be inferred from other information within it. The citizenship of Albert Einstein is likely to be deduced from the institution he employed by, while the field of study can often be inferred by propagating the field from the prize to its laureate. However, the scarcity of knowledge and the complexity of understanding intricate relational patterns render this task exceptionally challenging. Existing KG reasoning methods often fall short in sparsity scenarios (Pujara et al., 2017), highlighting  \nthe need for approaches specifically designed for sparse KGs.  \nSparse KG reasonin","cbCaioU4Nt1gU1vu","https://ap.wps.com/l/cbCaioU4Nt1gU1vu","pdf",1452625,15,"English","# Abstract\n# Introduction","[{\"question\":\"What problem does StruProKGR address?\",\"answer\":\"StruProKGR targets sparse knowledge graph reasoning—inferring missing facts in knowledge graphs where many triples are absent. The goal is effective, efficient, and interpretable inference under sparsity.\"},{\"question\":\"How does StruProKGR collect paths more efficiently than random walks?\",\"answer\":\"It uses a distance-guided path collection mechanism to reduce computational cost while focusing on more relevant paths. This avoids the stochastic inefficiency and variable-quality paths common in random-walk approaches.\"},{\"question\":\"How does StruProKGR improve reasoning beyond independent path processing?\",\"answer\":\"It incorporates structural information through probabilistic path aggregation that prioritizes paths reinforcing each other. This addresses the limitation of many methods that treat paths independently and miss collective relational patterns.\"}]","StruProKGR - A Structural and Probabilistic Framework for Sparse Knowledge Graph Reasoning | PDF",38]