Search (4 results, page 1 of 1)

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Abstract

So far, within the library and information science (LIS) community, knowledge organization (KO) has developed its own very successful solutions to document search, allowing for the classification, indexing and search of millions of books. However, current KO solutions are limited in expressivity as they only support queries by document properties, e.g., by title, author and subject. In parallel, within the artificial intelligence and semantic web communities, knowledge representation (KR) has developed very powerful end expressive techniques, which via the use of ontologies support queries by any entity property (e.g., the properties of the entities described in a document). However, KR has not scaled yet to the level of KO, mainly because of the lack of a precise and scalable entity specification methodology. In this paper we present DERA, a new methodology inspired by the faceted approach, as introduced in KO, that retains all the advantages of KR and compensates for the limitations of KO. DERA guarantees at the same time quality, extensibility, scalability and effectiveness in search.

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Abstract

From the literature study, it was observed that there are significantly fewer studies that review ontology-based narrative models. This motivates the current work. A parametric approach was adopted to report the existing ontology-driven models for narrative information. The work considers the narrative and ontology components as parameters. This study hopes to encompass the relevant literature and ontology models together. The work adopts a systematic literature review methodology for an extensive literature selection. The models were selected from the literature using a stratified random sampling technique. The findings illustrate an overview of the narrative models across domains. The study identifies the differences and similarities of knowledge representation in ontology-based narrative information models. This paper will explore the basic concepts and top-level concepts in the models. Besides, this study provides a study of the narrative theories in the context of ongoing research. It also identifies the state-of-the-art literature for ontology-based narrative information.

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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.

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Date

29. 7.2018 11:40:33