Abstract
Research in the digital humanities often involves vague information, either because our objects of study lack clearly defined boundaries, or because our knowledge about them is incomplete or hypothetical, which is especially true in disciplines about our past (such as history, archaeology, and classical studies). Most techniques used to represent data vagueness emerged from natural sciences, and lack the expressiveness that would be ideal for humanistic contexts. Building on previous work, we present here a conceptual framework based on the ConML modelling language for the expression of information vagueness in digital humanities. In addition, we propose an implementation on non-relational data stores, which are becoming popular within the digital humanities. Having clear implementation guidelines allow us to employ search engines or big data systems (commonly implemented using non-relational approaches) to handle the vague aspects of information. The proposed implementation guidelines have been validated in practice, and show how we can query a vagueness-aware system without a large penalty in analytical and processing power.
Highlights
We generate knowledge from raw data through different mechanisms, such as observation, perception, theorization, and deduction [1], producing information models that constitute the starting point of any knowledge generation process
Conceptual models have been successfully used in humanities projects such as Europeana [4], ARIADNE [5], and DARIAH [6]
7).2The project uses a web-based environment with non-relational real-time database provided in DICTOMAGRED, expressed first in natural language and subsequently executed in by Firebase is a mobile and web application development platform run by the Algolia search systems accessing the Firebase-defined structure: Google since 2014 that allow us to personalize the non-relational database implementation with
Summary
We generate knowledge from raw data through different mechanisms, such as observation, perception, theorization, and deduction [1], producing information models that constitute the starting point of any knowledge generation process. When working in the humanities, we create information models that reflect the data that we have and the possible hypotheses from them in order to fill the knowledge gap. This model-building process is especially relevant when working with information about our past, in which this gap is usually larger. Conceptual models have been successfully used in humanities projects such as Europeana [4], ARIADNE [5], and DARIAH [6]
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