Rosetta Statements: 以用户为中心简化FAIR知识图谱构建
Rosetta Statements: Simplifying FAIR Knowledge Graph Construction with a User-Centered Approach
AI总结：
提出Rosetta Statement方法，通过自然语言语句建模和元模型，降低领域专家构建FAIR知识图谱的语义门槛，并在ORKG中实现。
摘要：
机器需要数据和元数据具有机器可操作性和FAIR（可发现、可访问、可互操作、可重用）特性，以管理日益增长的数据量。知识图谱和本体是实现这一目标的关键，但由于需要语义和数据建模方面的先验知识，其使用受到高访问壁垒的阻碍。Rosetta Statement方法提出建模英语自然语言语句，而非独立于思维的现实。我们提出了一个元模型，用于为简单语句类型创建语义模式。该方法支持语句的版本控制，并提供详细的编辑历史。每个Rosetta Statement模式都有一个动态标签，用于将语句显示为自然语言句子。在开放研究知识图谱（ORKG）中作为用例实现，该方法允许领域专家定义数据模式，而无需语义知识。未来计划包括将Rosetta Statements与语义单元结合，将ORKG组织成有意义的子图，提高可用性。还计划提供一个无需SPARQL或Cypher知识的查询语句的搜索界面，以及使用大型语言模型进行数据输入和显示的工具。Rosetta Statement元模型支持三步知识图谱构建过程。领域专家可以通过使用Wikidata，在无需本体工程师支持的情况下建模语义内容，降低入门门槛并提高认知互操作性。第二级涉及将Wikidata术语映射到已建立的本体，第三级开发用于推理的语义图模式，这需要与本体工程师合作。
英文摘要：
Machines need data and metadata to be machine-actionable and FAIR (findable, accessible, interoperable, reusable) to manage increasing data volumes. Knowledge graphs and ontologies are key to this, but their use is hampered by high access barriers due to required prior knowledge in semantics and data modelling. The Rosetta Statement approach proposes modeling English natural language statements instead of a mind-independent reality. We propose a metamodel for creating semantic schema patterns for simple statement types. The approach supports versioning of statements and provides a detailed editing history. Each Rosetta Statement pattern has a dynamic label for displaying statements as natural language sentences. Implemented in the Open Research Knowledge Graph (ORKG) as a use case, this approach allows domain experts to define data schema patterns without needing semantic knowledge. Future plans include combining Rosetta Statements with semantic units to organize ORKG into meaningful subgraphs, improving usability. A search interface for querying statements without needing SPARQL or Cypher knowledge is also planned, along with tools for data entry and display using Large Language Models. The Rosetta Statement metamodel supports a three-step knowledge graph construction procedure. Domain experts can model semantic content without support from ontology engineers by using Wikidata, lowering entry barriers and increasing cognitive interoperability. The second level involves mapping Wikidata terms to established ontologies, and the third step developing semantic graph patterns for reasoning, requiring collaboration with ontology engineers.
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。
