FAIR data representation in times of eScience: a comparison of instance-based and class-based semantic representations of empirical data using phenotype descriptions as example

Abstract

Background The size, velocity, and heterogeneity of Big Data outclasses conventional data management tools and requires data and metadata to be fully machine-actionable (i.e., eScience-compliant) and thus findable, accessible, interoperable, and reusable (FAIR). This can be achieved by using ontologies and through representing them as semantic graphs. Here, we discuss two different semantic graph approaches of representing empirical data and metadata in a knowledge graph, with phenotype descriptions as an example. Almost all phenotype descriptions are still being published as unstructured natural language texts, with far-reaching consequences for their FAIRness, substantially impeding their overall usability within the life sciences. However, with an increasing amount of anatomy ontologies becoming available and semantic applications emerging, a solution to this problem becomes available. Researchers are starting to document and communicate phenotype descriptions through the Web in the form of highly formalized and structured semantic graphs that use ontology terms and Uniform Resource Identifiers (URIs) to circumvent the problems connected with unstructured texts. Results Using phenotype descriptions as an example, we compare and evaluate two basic representations of empirical data and their accompanying metadata in the form of semantic graphs: the class-based TBox semantic graph approach called Semantic Phenotype and the instance-based ABox semantic graph approach called Phenotype Knowledge Graph. Their main difference is that only the ABox approach allows for identifying every individual part and property mentioned in the description in a knowledge graph. This technical difference results in substantial practical consequences that significantly affect the overall usability of empirical data. The consequences affect findability, accessibility, and explorability of empirical data as well as their comparability, expandability, universal usability and reusability, and overall machine-actionability. Moreover, TBox semantic graphs often require querying under entailment regimes, which is computationally more complex. Conclusions We conclude that, from a conceptual point of view, the advantages of the instance-based ABox semantic graph approach outweigh its shortcomings and outweigh the advantages of the class-based TBox semantic graph approach. Therefore, we recommend the instance-based ABox approach as a FAIR approach for documenting and communicating empirical data and metadata in a knowledge graph.

Knowledge Representation and Management: Notable Contributions in 2021

“…The section editors' initial screening based on the title and abstract resulted in 326 papers. This set of papers was further reviewed jointly by the section editors to select a consensus list of 15 candidate best papers [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. The four top-ranked papers according to expert reviewers' evaluation were selected as the best papers in the field of KRM published in 2021 (see Table 1).…”

“…In addition to the best paper from Vogt [6], which recommends using a knowledge graph rather than an ontology to represent empirical data in anatomy, there are three other candidate papers about building knowledge graphs for disparate domains [10][11][12].…”

“…The four top-ranked papers according to expert reviewers' evaluation were selected as the best papers in the field of KRM published in 2021 (see Table 1). The first paper is a contribution by Vogt [6], who performed a comprehensive comparison of class-based TBox representation (i.e., ontology) and instance-based ABox representation (i.e., knowledge graph) to document and manage empirical data and metadata for anatomical research, in compliance with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. It was concluded that the ABox approach seems to be in general superior to the TBox approach for representing and managing empirical data.…”

Computable species descriptions and nanopublications: applying ontology-based technologies to dung beetles (Coleoptera, Scarabaeinae)

“…These approaches are also referred to as the TBox (Terminological Box) approaches, since the TBox represents the structural framework of an ontology and includes logical definitions and a hierarchy of classes. The class-based approaches present their own set of challenges, such as complex semantics, limited expressiveness and difficulty in interpreting the results by humans ( Vogt 2021 ). An alternative approach, the individual-based approach, in which phenotypic statements are expressed as ontology individuals, seems to be more intuitive and effective ( Vogt 2021 ).…”

SCICERO: A deep learning and NLP approach for generating scientific knowledge graphs in the computer science domain

“…They are typically encoded using the Web Ontology Language (OWL) 1 and are considered the cornerstones of the Semantic Web [7]. KGs consist of large networks of entities and relationships that provide machine readable and understandable information about a specific domain following a formal semantics [8].…”