With the advances of Artificial Intelligence, the need for annotated data increases. However, the quality of these annotations can be impacted by the evolution of domain knowledge since the relations between successive versions of ontologies are rarely described and the history of concepts is not kept at the ontology level. As a consequence, using datasets annotated at different times becomes a real challenge for data- and knowledge-intensive systems. This work presents a way to address this problem. We introduce a Historical Knowledge Graph (HKG), where information from previous versions of an ontology can be found inside a single graph, reducing storage space (no need for versioning) and data treatment time (no need for laborious analysis of each version of the ontology). The HKG proposed in this work represents the evolutionary aspects of the knowledge in a structural way. Examples of the applicability of an HKG for information retrieval and the maintenance of semantic annotations show the capability of our approach for improving the quality of existing techniques.

随着人工智能的发展，对带注释数据的需求也在增加。但是，由于很少描述本体的连续版本之间的关系并且概念的历史未保持在本体级别上，因此这些注释的质量会受到领域知识的发展的影响。因此，对于数据和知识密集型系统而言，使用在不同时间进行注释的数据集成为一个真正的挑战。这项工作提出了解决此问题的方法。我们引入了历史知识图（HKG），可在单个图内找到先前版本本体的信息，从而减少了存储空间（无需版本控制）和数据处理时间（无需费力地分析每个版本的知识）。本体）。这项工作中提出的HKG以结构化的方式代表了知识的进化方面。HKG适用于信息检索和语义注释维护的示例说明了我们的方法能够提高现有技术的质量。