SciMantify -- A Hybrid Approach for the Evolving Semantification of Scientific Knowledge
This addresses the need for more accessible and reusable scientific knowledge for researchers and machines, but it appears incremental as it builds on existing models like Linked Open Data and platforms like ORKG.
The paper tackles the problem of static and unstructured scientific publications by proposing SciMantify, a hybrid approach for evolving semantification that leverages tabular formats and human-machine collaboration to transition knowledge into a semantic representation integrated in a knowledge graph, with a preliminary user experiment showing it simplifies preprocessing and reduces effort.
Scientific publications, primarily digitized as PDFs, remain static and unstructured, limiting the accessibility and reusability of the contained knowledge. At best, scientific knowledge from publications is provided in tabular formats, which lack semantic context. A more flexible, structured, and semantic representation is needed to make scientific knowledge understandable and processable by both humans and machines. We propose an evolution model of knowledge representation, inspired by the 5-star Linked Open Data (LOD) model, with five stages and defined criteria to guide the stepwise transition from a digital artifact, such as a PDF, to a semantic representation integrated in a knowledge graph (KG). Based on an exemplary workflow implementing the entire model, we developed a hybrid approach, called SciMantify, leveraging tabular formats of scientific knowledge, e.g., results from secondary studies, to support its evolving semantification. In the approach, humans and machines collaborate closely by performing semantic annotation tasks (SATs) and refining the results to progressively improve the semantic representation of scientific knowledge. We implemented the approach in the Open Research Knowledge Graph (ORKG), an established platform for improving the findability, accessibility, interoperability, and reusability of scientific knowledge. A preliminary user experiment showed that the approach simplifies the preprocessing of scientific knowledge, reduces the effort for the evolving semantification, and enhances the knowledge representation through better alignment with the KG structures.