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"He who knows, and knows not that he knows, is asleep; wake him," a quote from the Persian philosopher Ibn Yami, highlights the importance of "unknown knowns" -- knowledge that exists but is not acknowledged -- in decision-making, risk assessment, and fields such as psychology and cognition, as well as its role in the explanation of biases. Building on the observation of Sigmund Freud that "the missing, not conveyed, is an integral part of the whole," we propose a methodology for identifying latent, unknown-known attributes of entities. Our approach, called Population-based Latent Personal Analysis (LPA), uses an information-theoretic approach to find population-based attributes for entities. By identifying elements whose Shannon information differs most from their counterparts in the population, LPA can determine an entity's locally overused rare population elements, and elements that are locally underused or missing that are popular in the population. These elements contribute to the entity's LPA's distance attribute and determine its LPA's signature attribute. We show that LPA's attributes enable a quantitative and qualitative understanding, and that the unknown knowns provide additional important information. We demonstrate the practical applications of LPA attributes in various areas, such as impersonation detection in social media, the spectral spread of sub-repertoires of clonal B-cell populations within a person, and the digital humanities. We also discuss how these attributes can be used for identifying trends and patterns in temporal data.