A Metadata Application Profile Framework for Actionable Profiling and Semantic Data Publishing
Creators: name: Thalhath, Nishad
Publisher: name: Zenodo
Year: 2024
Subjects: Metadata
Application Profiles
FAIR
RDF

description: Data demands and use cases have been through a pivotal change in recent years. An unprecedented pandemic has shown the world significance of interdisciplinary data interoperability. The rise of Artificial Intelligence (AI) technologies, especially Large Language Models (LLM), demonstrates that data is a more significant resource in shaping computing and related technologies in the future. The consumption of data is also going through a significant shift. Machine learning models and automated systems became the primary consumers of data. This also opens up a new set of challenges in data interoperability, especially when reasoning and understanding the inherent meaning of the data becomes more significant. Semantics, the underlying meaning of data, is the most critical aspect in progressing the development of AI to Artificial General Intelligence (AGI). This will eventually result in the development of new standards and technologies to support data interoperability, especially semantic interoperability.

Semantic interoperability in data is crucial for understanding and interpreting information. Semantic interoperability refers to the capacity of different information systems to exchange and interpret data consistently, allowing for seamless data integration across varied platforms and applications without losing its intended meaning. Traditionally, data’s meaning was outlined through human-readable documentation, metadata, annotations, or tags. Despite the undeniable advantages of the World Wide Web, the foundational principles of its operation have only recently been applied to managing data. In the past, web-published data was often shared in unstructured formats like CSV or XML or displayed within HTML tables, stripping away much of its structural and semantic integrity. The simple hypertext Web implies relationships between linked documents, but creating explicit connections between entities in documents requires a more sophisticated format than HTML can offer. The introduction of the Resource Description Framework (RDF) and the Web Ontology Language (OWL) has revolutionized the embedding of data semantics directly within the data, overcoming these limitations. RDF is proposed by the World Wide Web Consortium (W3C) as the standard for Semantic Web information representation. This framework, along with its specifications maintained by W3C, differs notably from traditional formats by employing a directed graph model and embedded semantics.

For open data publishing, a well-established 5-star open data model was proposed by Tim Berners-Lee. Five stars were proposed to guide the publication of open data on the Web, based on the following measures: 1) data available on the Web under an open license; 2) available as machine-readable structured data 3) uses a non-proprietary format 4) uses open standards from W3C 5) Link data to other data to provide context. Hyvönen et al. proposed two extra stars: The 6th star is given if the schemas (vocabularies) used in the dataset are explicitly described and published alongside the dataset. For the 7th star, the dataset’s quality against the schemas used must be explained so that the user can evaluate the data quality.

Datasets must be adequately documented, and the vocabularies used are explicitly stated to make them well-reusable for the local application. Also, even if the schema is documented, evaluating how well the data aligns with the expected schema and ensuring the data quality without an actionable validation mechanism may be difficult. A significant challenge of dataset re-using is evaluating the data’s suitability for the local application. Recent movements on adapting Findable, Accessible, Interoperable, and Reusable (FAIR) principles for data management and stewardship have shown that data quality and fitness are crucial for data reusability. This research aims to develop a framework that aligns with the 5-star and 7-star models, ensuring that the data is open, semantically interoperable, and verifiable and that the semantics are preserved and maintained throughout the data lifecycle. The framework will also enable semantic interoperability and better reusability of the data with semantics and validation to enforce interoperability (I) and reusability (R) and satisfy F2, A2, I(1-3), and R1 in the FAIR principles.

Based on the RDF model, this research focuses on developing a practical framework for the semantic interoperability of data. The approach of this research is adapting metadata application profiles as a means for semantic interoperability. Metadata application profiles, or simply application profiles, are sets of vocabularies derived from multiple namespaces designed for specific localized uses. These profiles serve as an explainer and a constraint for metadata, ensuring the metadata remains explicit and relevant within a local application context. Application profiles play a crucial role in applying semantic rules to metadata, improving its interoperability, compatibility, and usability. This research examined the possibility of creating a detailed application profile that complies with semantic web standards, promoting interoperability among different profiles and data sets. It utilizes the Dublin Core Description Set Profiles (DSP) as the basis for structuring metadata application profiles, guiding them towards compatibility with RDF. The findings introduce "Yet Another Metadata Application Profiles" (YAMA), a profile authoring format based on the DSP model that assists in creating and managing metadata application profiles. YAMA supports the production of application profiles in various standard formats, making them accessible in formats that are understandable to humans, executable by machines, and suitable for data verification purposes. The framework proposed in this research is designed to support the FAIR principles, ensuring that the data and semantics are well documented for better interoperability and reusability. Additionally, the research proposes an extension for data mapping within YAMA to enhance the semantic interoperability of openly shared data, facilitating the conversion of non-RDF data formats to RDF. This conversion is pivotal for publishing data with semantics and ensuring the efficient integration of datasets by preserving the semantics, making it semantically interoperable, and progressing toward 5-star, 7-star, and FAIR-compatible open data.

Ensuring the provenance of the application profiles is equally essential as the data provenance to ensure that the changes in the semantics and constraints on the validation of the corresponding dataset versions are preserved. This is possible through documenting and versioning the changes in the underlying vocabularies or the application profiles. This research also proposes a versioning mechanism for the application profiles to record the application profiles’ provenance, authoring, and versioning to ensure that the application profiles are well maintained and the changes are well documented. The versioning mechanism proposed in this research is based on the Provenance, Authoring, and Versioning (PAV) ontology, a lightweight ontology for data provenance, and implemented in such a way that the provenance information of the profiles is also semantically interoperable and verifiable.

The metadata application profile framework proposed in this study can be expressed in human-readable and machine-actionable formats. The human-readable format is designed to be understandable by the domain experts and the data stewards. In contrast, the machine-actionable format is intended to be used by the machines for the validation and the quality assurance of the data using established specifications such as Shape Expressions (ShEx) and Shapes Constraint Language (SHACL). The profiles can also be expressed in RDF so that the application profiles themselves are semantically interoperable and verifiable. This will also help integrate datasets with verifiable semantics with less effort and ensure the semantics remain interoperable.

As an overview, this research aims to develop a complete application profile framework for semantic interoperability, resulting in an extensible application profile authoring format, which helps generate different application profile formats for both humans and machines. A data mapping extension is proposed to enhance the semantic interoperability of openly shared data, facilitating the conversion of non-RDF data formats to RDF. A provenance and versioning model for application profiles is also an outcome of this research. Both the application profiles and the provenance of the profile can be expressed in RDF to ensure the semantic interoperability of the profiles. The proposed framework supports the FAIR principles, providing the data and semantics are well-documented for better interoperability and reusability. The resulting framework is designed to be extensible and adaptable to future standards and technologies, ensuring that the data and the semantics are well-maintained and interoperable.