A Pattern Driven Approach to Knowledge Representation in the Disaster Domain
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
SN Computer Science
Abstract
Access to integrated disaster-related data through querying is still a problem due to associated semantic barriers. The disaster
domain largely relies on the top–down approach of ontology development. This limits reuse due to associated commitments
and complex alignments within ontologies. Therefore, there is a need to utilize a bottom-up approach that reuses patterns
for representing disaster knowledge. To bridge the availability gap of patterns for representing disaster knowledge, this study
identifies existing and emerging patterns for reuse while organizing disaster data from multiple sector stakeholders. Based
on the eXtreme Design (XD) methodology and key informant interviews, competency questions (CQs) were elicited from
domain stakeholders. The CQs are matched with existing patterns from other contexts. Emerging patterns (e.g the Event
Classification and Quality Dependence Description for Objects) are also developed for CQs not captured and subsequently
tested using SPARQL queries characterising the CQs. It is in this context that this paper presents a characterisation of disaster
risk knowledge using CQs and corresponding patterns (reusable and emerging) covering the knowledge. Accordingly, we
illustrate a pattern-driven use case to organise drought hazard data for early warning purposes. This provides a powerful use
case for adopting a pattern-based approach to knowledge representation in the disaster domain.
Description
Keywords
Ontology design patterns, Hazard, Vulnerability, Risk
Citation
Mazimwe, A., Hammouda, I., Gidudu, A., & Barasa, B. (2020). A pattern driven approach to knowledge representation in the disaster domain. SN Computer Science, 1(6), 1-17. https://doi.org/10.1007/s42979-020-00342-5