Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Hypotheses are critical components of scientific argumentation. Knowing established hypotheses is often a prerequisite for following and contributing to scientific arguments in a research field. In scientific publications, hypotheses are usually presented for specific empirical settings, whereas the related general claim is assumed to be known. Prerequisites for developing argumentation machines for assisting scientific workflows are to account for domain-specific concepts needed to understand established hypotheses, to clarify the relationships between specific hypotheses and general claims, and to take steps towards formalization. Here, we develop a framework for formalizing hypotheses in the research field of invasion biology. We suggest conceiving hypotheses as consisting of three basic building blocks: a subject, an object, and a hypothesized relationship between them. We show how the subject-object-relation pattern can be applied to well-known hypotheses in invasion biology and demonstrate that the contained concepts are quite diverse, mirroring the complexity of the research field. We suggest a stepwise approach for modeling them to be machine-understandable using semantic web ontologies. We use the SuperPattern Ontology to categorize hypothesized relationships. Further, we recommend treating every hypothesis as part of a hierarchical system with ‘parents’ and ‘children’. There are three ways of moving from a higher to a lower level in the hierarchy: (i) specification, (ii) decomposition, and (iii) operationalization. Specification involves exchanging subjects or objects. Decomposition means zooming in and making explicit assumptions about underlying (causal) relationships. Finally, operationalizing a hypothesis means providing concrete descriptions of what will be empirically tested.
Hypotheses are critical components of scientific argumentation. Knowing established hypotheses is often a prerequisite for following and contributing to scientific arguments in a research field. In scientific publications, hypotheses are usually presented for specific empirical settings, whereas the related general claim is assumed to be known. Prerequisites for developing argumentation machines for assisting scientific workflows are to account for domain-specific concepts needed to understand established hypotheses, to clarify the relationships between specific hypotheses and general claims, and to take steps towards formalization. Here, we develop a framework for formalizing hypotheses in the research field of invasion biology. We suggest conceiving hypotheses as consisting of three basic building blocks: a subject, an object, and a hypothesized relationship between them. We show how the subject-object-relation pattern can be applied to well-known hypotheses in invasion biology and demonstrate that the contained concepts are quite diverse, mirroring the complexity of the research field. We suggest a stepwise approach for modeling them to be machine-understandable using semantic web ontologies. We use the SuperPattern Ontology to categorize hypothesized relationships. Further, we recommend treating every hypothesis as part of a hierarchical system with ‘parents’ and ‘children’. There are three ways of moving from a higher to a lower level in the hierarchy: (i) specification, (ii) decomposition, and (iii) operationalization. Specification involves exchanging subjects or objects. Decomposition means zooming in and making explicit assumptions about underlying (causal) relationships. Finally, operationalizing a hypothesis means providing concrete descriptions of what will be empirically tested.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.