FAIR Science for Social Machines: Let’s Share Metadata Knowlets in the Internet of FAIR Data and Services

Abstract

11 figures of this paper. Figure 1 is the hourglass model of the Internet architecture. Figure 2 shows the merge of three hourglasses (data-infrastructure, tools-infrastructure and compute-infrastructure) into the image of a propeller with three blades and the underlying infrastructure. The narrow waist of the hourglass (minimal essential standards and protocols) is comparable to the center of this picture. Figure 3 is the simple Digital Object picture. The smallest conceivable Digital Object is a persistent identifier (PID) (a digital symbol referring to a particular concept). Each digital object that contains “information” should be adorned with metadata asserting things about the nature of that information. Typical intrinsic metadata describe the factual information that is “indisputable” about the digital object itself. Intrinsic metadata containers, expanded metadata containers and the actual containers\r\nholding the data elements or the core (in case of for instance a workflow) could also be treated as separate but permanently-linked digital objects, each with their own unique, persistent and resolvable identifier (UPRI) and thus form a stack of related metadata containers that contain (machine readable, FAIR) metadata of different nature, all asserting, however, relevant information about the data container. Figure 4 shows how in the developing Internet of FAIR Data and Services, a linked-data-compliant query in a virtual machine format could automatically find the most relevant databases. Figure 5 shows the semiotic triangle, based on the concept of cancer. Figure 6 shows the single meaningful assertion in machine readable format, which is called a nanopublication. The smallest conceivable assertion has the structure of a subject, a predicate and an object. To form a nanopublication this “triple” needs to be published in machine readable format with full provenance and publication information (also in machine readable format). Figure 7 shows the Knowlet as a collection of cardinal assertions “about” a given subject. The objects effectively form the “conceptual context” of explicitly associated concepts. The predicates can range from very specific and explicit relationship descriptions such as “inhibits” or “is married to” to more generic and less explicit connections, such as “co-occurs in the same sentence as”. Figure 8 shows that the Knowlet is a digital object and needs to be findable, accessible, interoperable and reusable (i.e.,\r\nFAIR) in its own right. It also may change over time, when more assertions are collected about the core concept. Therefore, each Knowlet in the Internet of FAIR Data and Services (IFDS) needs a unique, persistent and resolvable identifier (UPRI). Figure 9 shows that the Knowlet can be seen as a metadata container for the concept it represents. It can represent many different things from plain concepts like a gene or a person (ORCID record), to a data set, a data base, a work flow or any other thing in the Internet of Things. Figure 10 shows three ways in which Knowlets can be used to connect dispersed digital objects. In Figure 11, A: Concepts, physical objects or things of different semantic types (and thus also intrinsically meaningless unique, persistent and resolvable identifiers (UPRIs)) can cluster based on contextual similarity without ever being explicitly connected (drug might treat disease). B: Nearly identical concepts that are nevertheless in certain circumstances to be seen as distinct, will automatically cluster as one if the resolution of search or matching is lowered, while they will separate out when the resolution is made higher. C: Conceptual and semantic drift occur.