Web of Similarity
- Publication Type:
- Journal Article
- Citation:
- Journal of Computational Science, 2019, 36
- Issue Date:
- 2019-09-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1-s2.0-S1877750316303842-main.pdf | Published Version | 1.47 MB |
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© 2016 Elsevier B.V. Despite the achieved maturity and popularity, the current semantic technology has severe limitations in real-world applications as it is unable to represent uncertain knowledge. Probabilistic Semantics partially address this issue. Unfortunately, their quantitative approach fails in many practical applications that require a more abstracted vision and conceptual model of the uncertainties. Indeed, Probabilistic Semantics can only model ecosystems where all the uncertainties are quantified. In this paper, we introduce a qualitative approach for the representation of the uncertainties in the Semantic Web. We propose a human-inspired model that defines the uncertainty as an explicit similarity, providing a flexible range of solutions for approximate semantic reasoning in uncertain ecosystems. The resulting semantic environment, referred to as Web of Similarity (WoS), is an extension of the Web of Data which is able to represent and process analogies among concepts and individuals. As the generic Semantic Web, the Web of Similarity is a global semantic infrastructure that can support specific systems or applications at a global scale. WoS is a step forward to get richer Web Semantics which are closer to the human ones.
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