Developing Ontologies within Decentralised Settings

Abstract

Abstract. This paper addresses two research questions: "How should a well-engineered methodology facilitate the development of ontologies within communities of practice?" and "What methodology should be used?" If ontologies are to be developed by communities then the ontology development life cycle should be better understood within this context. This paper presents the Melting Point (MP), a proposed new methodology for developing ontologies within decentralized settings. It describes how MP was developed by taking best practices from other methodologies, provides details on recommended steps and recommended processes, and compares MP with alternatives. The methodology presented here is the product of direct first-hand experience and observation of biological communities of practice in which some of the authors have been involved. The Melting Point is a methodology engineered for decentralised communities of practice for which the designers of technology and the users may be the same group. As such, MP provides a potential foundation for the establishment of standard practices for ontology engineering.

The Data Mining OPtimization Ontology

“…There are several methodologies for ontology development in the literature, including Methontology [4], NeON [5], Melting Point [6], and DiDOn [7]. Although these methodologies may di↵er in scope and focus, they have some commonalities that can be roughly mapped in three main stages of the ontology development process: 1) specification with a domain analysis (including use cases, competency questions), 2) the conceptualization, formalization, and implementation, and 3) maintenance with refinement and evolution of the ontology.…”

“…To assist the user in the design of an e↵ective workflow, Intelligent Discovery Assistants (IDAs) have been proposed (a recent survey of IDAs is presented in [28]). In the following, we describe how DMOP was used to construct the e-LICO IDA 6 that is the first IDA capable of both planning and ranking DM workflows. We discuss the evaluation of DMOP-based semantic meta-mining and the deployment of the e-LICO IDA in RapidMiner.…”

Biotea-Biolinks: A semantic infrastructure for exploring and analyzing scientific publications

“…An explanation on the different key points in these definitions, i.e., conceptualization, formal and explicit, and shared, were offered in detail by Guarino, Oberle & Staab [22]. At that point, Ontology Engineering was an incipient scientific discipline looking for "agreements upon standards for protocols to achieve the unified and consistent progression of innovation and knowledge" [23,24]. Those agreements arose with the appearance and use of methodologies and tools, and the active participation of the community involved so the specifications were indeed shared.…”

“…This component 23 renders the group-based distribution for one or more documents as a heat-map matrix. It aims to present an overview of the most representative groups for a cluster of documents.…”

“…The higher the similarity score, the bigger the circle representing the compared article and the shorter the arc to the reference article. On mouse over nodes, a tooltip showing information related to the corresponding article as 21 Biotea-vis-tooltips is publicly available at https://github.com/ljgarcia/biotea-vis-tooltip 22 Biotea-vis-annotations is publicly available at https://github.com/ljgarcia/biotea-vis-annotation 23 Biotea-vis-topicDistribution is publicly available at https://github.com/ljgarcia/biotea-vistopicDistribution 24 Biotea-vis-similarity is publicly available at https://github.com/ljgarcia/biotea-vis-similarity well as the similarity score is displayed. This component has two Biolinks dependencies: biotea-io-parser and biotea-vis-tooltip.…”