Tab2KG：使用轻量级语义概要的语义表解释
Tab2KG: Semantic Table Interpretation with Lightweight Semantic Profiles
摘要：
表格数据在许多数据分析和机器学习任务中起着至关重要的作用。通常，表格数据不具有任何机器可读的语义。在这种情况下，语义表解释对于使数据分析工作流更加健壮和可解释至关重要。本文提出了Tab 2KG-一种新的方法，其目标是解释具有以前看不见的数据的表，并自动推断其语义，将其转换为语义数据图。我们引入原来的轻量级语义配置文件，丰富了领域本体的概念和关系，并表示域和表的特点。我们提出了一个一次性的学习方法，依赖于这些配置文件映射到一个领域本体的表格数据集包含以前看不见的实例。与现有的语义表解释方法相比，Tab 2KG仅依赖于语义配置文件，不需要任何实例查找。这个属性使得Tab 2KG特别适合数据分析环境，其中数据表通常包含新实例。我们对来自不同应用领域的几个真实数据集的实验评估表明，Tab 2KG优于最先进的语义表解释基线。
英文摘要：
Tabular data plays an essential role in many data analytics and machine learning tasks. Typically, tabular data does not possess any machine-readable semantics. In this context, semantic table interpretation is crucial for making data analytics workflows more robust and explainable. This article proposes Tab2KG - a novel method that targets at the interpretation of tables with previously unseen data and automatically infers their semantics to transform them into semantic data graphs. We introduce original lightweight semantic profiles that enrich a domain ontology's concepts and relations and represent domain and table characteristics. We propose a one-shot learning approach that relies on these profiles to map a tabular dataset containing previously unseen instances to a domain ontology. In contrast to the existing semantic table interpretation approaches, Tab2KG relies on the semantic profiles only and does not require any instance lookup. This property makes Tab2KG particularly suitable in the data analytics context, in which data tables typically contain new instances. Our experimental evaluation on several real-world datasets from different application domains demonstrates that Tab2KG outperforms state-of-the-art semantic table interpretation baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
