自动发现知识图谱中跨数据源的通用多模态模式，支持学者随时探索新问题。
Bottom-up Anytime Discovery of Generalised Multimodal Graph Patterns for Knowledge Graphs
- 自底向上挖掘包含类型变量与值模式的多模态图模式
- 发现的模式可转为SPARQL查询并交互浏览
- 适合人文领域研究者探索未知知识关联
大量异构知识以知识图谱形式公开可用，常连接此前未关联的数据源，从而支持学者解答诸多新研究问题。然而，事先并不清楚数据能回答哪些问题，可能导致许多有趣且新颖的洞见未被发现。为支持这一科研流程，我们提出一种自底向上的、可随时执行的算法，用于在知识图谱中发现广义多模态图模式。每种模式由带（数据）类型变量、常量和/或值模式的二元陈述合取构成。模式发现后，将转换为SPARQL查询，并通过交互式分面浏览器呈现，附带元数据与溯源信息，使学者可探索、分析和共享查询。我们通过人文学科专家的用户评估验证了该方法的有效性。
原文摘要 · Abstract (English)
Vast amounts of heterogeneous knowledge are becoming publicly available in the form of knowledge graphs, often linking multiple sources of data that have never been together before, and thereby enabling scholars to answer many new research questions. It is often not known beforehand, however, which questions the data might have the answers to, potentially leaving many interesting and novel insights to remain undiscovered. To support scholars during this scientific workflow, we introduce an anytime algorithm for the bottom-up discovery of generalized multimodal graph patterns in knowledge graphs. Each pattern is a conjunction of binary statements with (data-) type variables, constants, and/or value patterns. Upon discovery, the patterns are converted to SPARQL queries and presented in an interactive facet browser together with metadata and provenance information, enabling scholars to explore, analyse, and share queries. We evaluate our method from a user perspective, with the help of domain experts in the humanities.
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