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PurposeThe current work provides a framework for the ranking of ontology development methodologies (ODMs).Design/methodology/approachThe framework is a step-by-step approach reinforced by an array of ranking features and a quantitative tool, weighted decision matrix. An extensive literature investigation revealed a set of aspects that regulate ODMs. The aspects and existing state-of-the-art estimates facilitated in extracting the features. To determine weight to each of the features, an online survey was implemented to secure evidence from the Semantic Web community. To demonstrate the framework, the authors perform a pilot study, where a collection of domain ODMs, reported in 2000–2019, is used.FindingsState-of-the-art research revealed that ODMs have been accumulated, surveyed and assessed to prescribe the best probable ODM for ontology development. But none of the prevailing studies provide a ranking mechanism for ODMs. The recommended framework overcomes this limitation and gives a systematic and uniform way of ranking the ODMs. The pilot study yielded NeOn as the top-ranked ODM in the recent two decades.Originality/valueThere is no work in the literature that has investigated ranking the ODMs. Hence, this is a first of its kind work in the area of ODM research. The framework supports identifying the topmost ODMs from the literature possessing a substantial amount of features for ontology development. It also enables the selection of the best possible ODM for the ontology development.

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. Keywords: ontologies, narrative information, modelling

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