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- W4382407588 abstract "Data from Social Networks is a valuable asset within both a scientific and a business world. In the context of this work, ontological modelling from Social Networks is understood as a knowledge building process to generate a shared domain model. Such a technique relies on a balanced co-existence of human intuition/creativity and technological support, referred to as Hybrid Intelligence. Additionally, it assumes collaborative modelling and collective/social intelligence. The method implies a certain degree of uncertainty that is, in principle, inversely proportional to the achieved consensus. There are two clear different convergence points between the proposed process and collective/social intelligence: (i) at a data level, because of the nature of the input which is generated by different individuals, communities, stakeholders and actors; and (ii) at a modelling level, where human and automatically generated inputs, design decisions and validations are expected to involve several contributors, experts, modellers or analysts. Although looking holistically at the modelling process, this paper concisely focuses mostly on the ontological structure and the associated uncertainty, while resulting systems and studies are object of future work." @default.
- W4382407588 created "2023-06-29" @default.
- W4382407588 creator A5024027601 @default.
- W4382407588 date "2023-01-01" @default.
- W4382407588 modified "2023-09-26" @default.
- W4382407588 title "Ontological Modelling and Social Networks: From Expert Validation to Consolidated Domains" @default.
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- W4382407588 doi "https://doi.org/10.1007/978-3-031-36030-5_53" @default.
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