Definition
The Open World Assumption (OWA) is the assumption that that what is not known to be true or false might be true, or absence of information is interpreted as unknown information, not as negative information. It assumes incomplete information about a given state of affairs, i.e., there may be more relevant information than what is provided. This is useful for describing knowledge in a way that is extensible and most commonly used in Artificial Intelligence and throughout the life sciences. This is contrasted with the Closed World Assumption.
Example
Take the sample data in Table 1 and a query: “Which alumni do not have a PhD?”, then under the OWA, it cannot answer with “Peter” because it does not know if Peter also obtained a PhD: Peter might have, but that has not been represented in the information system yet. To retrieve “Peter” as answer to the above query, an axiom has to be added that states explicitly that Peter does not have a PhD.
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Keet, C.M. (2013). Open World Assumption. In: Dubitzky, W., Wolkenhauer, O., Cho, KH., Yokota, H. (eds) Encyclopedia of Systems Biology. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9863-7_734
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DOI: https://doi.org/10.1007/978-1-4419-9863-7_734
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-9862-0
Online ISBN: 978-1-4419-9863-7
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