凹形区域与带洞区域的拓扑关系表示与分析

0 下载量 7 浏览量 更新于2024-08-27 收藏 392KB PDF 举报
本文探讨了"凹形区域和带单洞区域间拓扑关系的表示"这一关键主题,针对的是空间分析中的一个具体问题。在现有的空间拓扑关系模型中,许多都是针对相同类型的空间对象设计,但在实际应用中,对于像凹形区域(非凸区域)和带有单个孔洞的简单区域这样的复杂几何形状之间的关系,这些模型可能存在局限性。研究者们在4-交集模型的基础上进行创新,通过扩展4-交集矩阵来表达这种特殊的拓扑关系。 文章首先定义了161种不同的拓扑关系,这些关系涵盖了凹形区域与带单洞区域的各种可能交互情况。作者提供了前10种拓扑关系的示意图,以便读者直观理解。他们不仅理论构建了这些关系,还开发了一套算法,并通过编程验证了这161种关系的实现可能性,确保了模型的有效性。 为了保证模型的严谨性,作者进一步证明了这161种基本关系的完备性,即它们能够覆盖所有可能的情况,以及互斥性,即任何两种关系不会同时存在。此外,通过对相关工作的对比,作者强调了他们的模型在表达能力上的优势,相比于其他模型,它能更准确地描述凹形区域与带单洞区域之间的复杂联系。 关键词包括"拓扑关系"、"凹形区域"、"区域连接演算(RCC5)"、"人工智能"以及"带单洞区域"和"4-交集矩阵",这些词汇揭示了论文的核心内容和研究背景。该研究不仅对空间分析领域有重要贡献,也为人工智能和GIS(地理信息系统)的应用提供了一个重要的理论支持。 这篇文章是一项深入细致的空间拓扑关系研究,它扩展了现有模型,特别关注了非标准空间对象的处理,并通过实证方法证明了其有效性和优越性。这对于处理空间数据和理解空间现象的复杂性具有重要意义。

Compared with homogeneous network-based methods, het- erogeneous network-based treatment is closer to reality, due to the different kinds of entities with various kinds of relations [22– 24]. In recent years, knowledge graph (KG) has been utilized for data integration and federation [11, 17]. It allows the knowledge graph embedding (KGE) model to excel in the link prediction tasks [18, 19]. For example, Dai et al. provided a method using Wasser- stein adversarial autoencoder-based KGE, which can solve the problem of vanishing gradient on the discrete representation and exploit autoencoder to generate high-quality negative samples [20]. The SumGNN model proposed by Yu et al. succeeds in inte- grating external information of KG by combining high-quality fea- tures and multi-channel knowledge of the sub-graph [21]. Lin et al. proposed KGNN to predict DDI only based on triple facts of KG [66]. Although these methods have used KG information, only focusing on the triple facts or simple data fusion can limit performance and inductive capability [69]. Su et al. successively proposed two DDIs prediction methods [55, 56]. The first one is an end-to-end model called KG2ECapsule based on the biomedical knowledge graph (BKG), which can generate high-quality negative samples and make predictions through feature recursively propagating. Another one learns both drug attributes and triple facts based on attention to extract global representation and obtains good performance. However, these methods also have limited ability or ignore the merging of information from multiple perspectives. Apart from the above, the single perspective has many limitations, such as the need to ensure the integrity of related descriptions, just as network-based methods cannot process new nodes [65]. So, the methods only based on network are not inductive, causing limited generalization [69]. However, it can be alleviated by fully using the intrinsic property of the drug seen as local information, such as chemical structure (CS) [40]. And a handful of existing frameworks can effectively integrate multi-information without losing induction [69]. Thus, there is a necessity for us to propose an effective model to fully learn and fuse the local and global infor- mation for improving performance of DDI identification through multiple information complementing.是什么意思

2023-06-11 上传