Rosetta Statements: Lowering the Barrier for Semantic Parsing and Increasing the Cognitive Interoperability of Knowledge Graphs

Lars Vogt, Marcel Konrad, Kheir Eddine Farfar, Manuel Prinz, Allard Oelen
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Abstract

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 and NLP. The Rosetta Statement metamodel supports a two-step knowledge graph construction procedure. Domain experts can model semantic content without support from ontology engineers, lowering entry barriers and increasing cognitive interoperability. The second level involves developing semantic graph patterns for reasoning, requiring collaboration with ontology engineers.
罗塞塔语句:降低语义解析障碍,提高知识图谱的认知互操作性
知识图谱和本体是实现这一目标的关键,但由于需要具备语义学和数据建模方面的先验知识,它们的使用受到了高访问门槛的阻碍。罗塞塔语句(Rosetta Statement)方法建议对英语自然语言语句建模,而不是对与思维无关的现实建模。我们提出了一种为简单语句类型创建语义模式的模型。该方法支持语句版本化,并提供详细的编辑历史。每个 Rosetta 语句模式都有一个动态标签,用于将语句显示为自然语言句子。作为一个用例在开放研究知识图谱(ORKG)中实施,这种方法允许领域专家在不需要语义知识的情况下定义数据模式。未来的计划包括将 Rosetta 语句与语义单元相结合,将 ORKG 组织成有意义的子图,从而提高可用性。此外,我们还计划提供一个搜索界面,无需 SPARQL 或 Cypher 知识即可查询语句,并提供使用大型语言模型和 NLP 进行数据输入和显示的工具。罗塞塔语句元模型支持两步式知识图谱构建程序。领域专家可以对语义内容进行建模,无需本体论工程师的支持,从而降低了入门门槛,提高了认知互操作性。第二个层次涉及开发用于推理的语义图模式,需要与本体工程师合作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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