Knowledge Graphs (KGs) are essential in applications like semantic search, question answering, and decision support. They structure knowledge, validate facts, enable inference, and increasingly enhance Large Language Models (LLMs) by grounding outputs in structured, factual data. However, KGs have treated facts as static and timeless, ignoring the temporal nature of many truths. This leads to outdated or incorrect inferences. Temporal Knowledge Graphs (TKGs), like Wikidata and YAGO, address this by modeling the time-bound validity of facts. Multiple recent work has focused on predicting missing temporal facts, yet validating existing temporal information, to ensure the reliability and accuracy of TKGs, remains underexplored. In order to advance this area of research, we introduce the first benchmark designed to evaluate temporal fact validation methods. Derived from Wikidata, this benchmark supports systematic, quantitative, and qualitative comparisons, incorporating diverse assumptions about temporal data (e.g., timestamps, intervals) and KG structures (e.g., density, depth).

In a large and decentralised knowledge representation system such as the Web of Data, it is common for data sets to overlap. In the absence of a central naming authority, semantic heterogeneity is inevitable as such overlapping contents are described using different schemas. To overcome this problem, a number of solutions have automated the integration of these data sets by matching their schemas. In this work, we focus on a specific category of these solutions that relies on the concepts' extension for matching the schemas (i.e., instance-based methods). Rather than introducing a new approach for the task of schema matching, this work studies the impact of exploiting the semantics of owl:sameAs in such instance-based methods. For this empirical analysis, we investigate more than 900K concepts extracted from the Web, and make use of over 35B implicit identity assertions to study their impact. The experiments show that despite the growing doubts over their quality, exploiting owl:sameAs assertions extracted from the Web can improve instance-based schema matching techniques.

International audience

International audience