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Abstract

Finding similarity between concepts based on semantics has become a new trend in many applications (e.g., biomedical informatics, natural language processing). Measuring the Semantic Similarity (SS) with higher accuracy is a challenging task. In this context, the Information Content (IC)-based SS measure has gained popularity over the others. The notion of IC evolves from the science of information theory. Information theory has very high potential to characterize the semantics of concepts. Designing an IC-based SS framework comprises (i) an IC calculator, and (ii) an SS calculator. In this article, we propose a generic intrinsic IC-based SS calculator. We also introduce here a new structural aspect of an ontology called DCS (Disjoint Common Subsumers) that plays a significant role in deciding the similarity between two concepts. We evaluated our proposed similarity calculator with the existing intrinsic IC-based similarity calculators, as well as corpora-dependent similarity calculators using several benchmark data sets. The experimental results show that the proposed similarity calculator produces a high correlation with human evaluation over the existing state-of-the-art IC-based similarity calculators.

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Abstract

The article identifies the core literature available on flood ontologies and presents a review on these ontologies from various perspectives like its purpose, type, design methodologies, ontologies (re)used, and also their focus on specific flood disaster phases. The study was conducted in two stages: i) literature identification, where the systematic literature review methodology was employed; and, ii) ontological review, where the parametric approach was applied. The study resulted in a set of fourteen papers discussing the flood ontology (FO). The ontological review revealed that most of the flood ontologies were task ontologies, formal, modular, and used web ontology language (OWL) for their representation. The most (re)used ontologies were SWEET, SSN, Time, and Space. METHONTOLOGY was the preferred design methodology, and for evaluation, application-based or data-based approaches were preferred. The majority of the ontologies were built around the response phase of the disaster. The unavailability of the full ontologies somewhat restricted the current study as the structural ontology metrics are missing. But the scientific community, the developers, of flood disaster management systems can refer to this work for their research to see what is available in the literature on flood ontology and the other major domains essential in building the FO.