OntoSeer: A Tool to Ease the Ontology Development Process

2020 ◽  
Author(s):  
Pramit Bhattacharyya ◽  
Raghava Mutharaju
Keyword(s):  
Development Process ◽  
Ontology Development

scholarly journals A Pattern-Based Method for Re-Engineering Non-Ontological Resources into Ontologies

2010 ◽  
Vol 6 (4) ◽  
pp. 27-63 ◽  
Author(s):  
Boris Carmen Villazón-Terrazas ◽  
Mari Suárez-Figueroa ◽  
Asunción Gómez-Pérez
Keyword(s):  
Data Model ◽  
Development Process ◽  
Storage System ◽  
Ontology Development ◽  
Software Library ◽  
Process Ontology ◽  
Classification Schemes ◽  
Speed Up ◽  
And Storage

To speed up the ontology development process, ontology developers are reusing all available ontological and non-ontological resources, such as classification schemes, thesauri, lexicons, and so forth, that have already reached some consensus. Non-ontological resources are highly heterogeneous in their data model and storage system (or implementation). The reuse of these non-ontological resources involves their re-engineering into ontologies. This paper presents a method for re-engineering non-ontological resources into ontologies. The method is based on so-called re-engineering patterns, which define a procedure that transforms the non-ontological resource components into ontology representational primitives using WordNet for making explicit the relations among the non-ontological resource terms. The paper also provides the description of NOR2O, a software library that implements the transformations suggested by the patterns. Finally, it depicts an evaluation of the method, patterns, and software library proposed.

scholarly journals The euBusinessGraph ontology: A lightweight ontology for harmonizing basic company information

Semantic Web ◽  
2021 ◽  
pp. 1-28
Author(s):  
Dumitru Roman ◽  
Vladimir Alexiev ◽  
Javier Paniagua ◽  
Brian Elvesæter ◽  
Bjørn Marius von Zernichow ◽  
...  
Keyword(s):  
Development Process ◽  
Personal Data ◽  
Ontology Development ◽  
Value Chains ◽  
Knowledge Graph ◽  
Balance Sheets ◽  
Business Information ◽  
Data Value ◽  
Core Concepts ◽  
National Business

Company data, ranging from basic company information such as company name(s) and incorporation date to complex balance sheets and personal data about directors and shareholders, are the foundation that many data value chains depend upon in various sectors (e.g., business information, marketing and sales, etc.). Company data becomes a valuable asset when data is collected and integrated from a variety of sources, both authoritative (e.g., national business registers) and non-authoritative (e.g., company websites). Company data integration is however a difficult task primarily due to the heterogeneity and complexity of company data, and the lack of generally agreed upon semantic descriptions of the concepts in this domain. In this article, we introduce the euBusinessGraph ontology as a lightweight mechanism for harmonising company data for the purpose of aggregating, linking, provisioning and analysing basic company data. The article provides an overview of the related work, ontology scope, ontology development process, explanations of core concepts and relationships, and the implementation of the ontology. Furthermore, we present scenarios where the ontology was used, among others, for publishing company data (business knowledge graph) and for comparing data from various company data providers. The euBusinessGraph ontology serves as an asset not only for enabling various tasks related to company data but also on which various extensions can be built upon.

scholarly journals A Framework for Evaluating Agricultural Ontologies

2021 ◽  
Vol 13 (11) ◽  
pp. 6387
Author(s):  
Anat Goldstein ◽  
Lior Fink ◽  
Gilad Ravid
Keyword(s):  
Semantic Web ◽  
Intelligent Systems ◽  
Domain Knowledge ◽  
Development Process ◽  
Evaluation Methods ◽  
Evaluation Procedure ◽  
Ontology Development ◽  
Formal Representation ◽  
Core Component ◽  
Selection Of

An ontology is a formal representation of domain knowledge, which can be interpreted by machines. In recent years, ontologies have become a major tool for domain knowledge representation and a core component of many knowledge management systems, decision-support systems and other intelligent systems, inter alia, in the context of agriculture. A review of the existing literature on agricultural ontologies, however, reveals that most of the studies, which propose agricultural ontologies, are lacking an explicit evaluation procedure. This is undesired because without well-structured evaluation processes, it is difficult to consider the value of ontologies to research and practice. Moreover, it is difficult to rely on such ontologies and share them on the Semantic Web or between semantic-aware applications. With the growing number of ontology-based agricultural systems and the increasing popularity of the Semantic Web, it becomes essential that such evaluation methods are applied during the ontology development process. Our work contributes to the literature on agricultural ontologies by presenting a framework that guides the selection of suitable evaluation methods, which seems to be missing from most existing studies on agricultural ontologies. The framework supports the matching of appropriate evaluation methods for a given ontology based on the ontology’s purpose.

Ontological Engineering

2011 ◽  
pp. 44-70 ◽  
Author(s):  
O. Corcho ◽  
M. Fernández-López ◽  
A. Gómez-Pérez
Keyword(s):  
Development Process ◽  
Ontology Development ◽  
Ontological Engineering ◽  
Current Trends

Ontologies are formal, explicit specifications of shared conceptualizations. There is much literature on what they are, how they can be engineered and where they can be used inside applications. All these literature can be grouped under the term “ontological engineering,” which is defined as the set of activities that concern the ontology development process, the ontology lifecycle, the principles, methods and methodologies for building ontologies, and the tool suites and languages that support them. In this chapter we provide an overview of ontological engineering, describing the current trends, issues and problems.

Modeling a Supply Chain Reference Ontology Based on a Top-Level Ontology

2019 ◽  
Author(s):  
Farhad Ameri ◽  
Boonserm Kulvatunyou
Keyword(s):  
Supply Chain ◽  
Best Practice ◽  
Development Process ◽  
Manufacturing Sector ◽  
Empirical Studies ◽  
Ontology Development ◽  
Reference Ontology ◽  
Application Ontology ◽  
Working Groups ◽  
Development Approach

Abstract Several supply-chain ontologies have been introduced in the past decade with the promise of enabling supply chain interoperability. However, the existing supply-chain ontologies have several gaps with respect to completeness, logical consistency, domain accuracy, and the development approach. In this work, we propose a new, supply-chain, reference ontology that is 1) based on an existing top-level ontology and 2) developed using a collaborative, ontology-development, best practice. We chose this approach because empirical studies have shown the usefulness of adopting a top-level ontology both for improving the efficiency of the development process and enhancing the quality of the resulting ontology. The proposed proof-of-concept reference ontology is developed in the context of the Industrial Ontology Foundry (IOF). IOF is an international effort aimed at providing a coherent set of modular and publicly-available ontologies for the manufacturing sector. Although the proposed reference ontology is still at the draft stage, this paper shows that it has already benefited from the collaborative development process that involves inputs from the other working groups within IOF. Additionally, as a way to validate the proposed reference ontology, an application ontology related to a supplier discovery and evaluation use case is derived from the reference ontology and tested.

scholarly journals A Pattern-Based Method for Re-Engineering Non-Ontological Resources into Ontologies

2012 ◽  
pp. 17-54
Author(s):  
Boris Carmen Villazón-Terrazas ◽  
Mari Suárez-Figueroa ◽  
Asunción Gómez-Pérez
Keyword(s):  
Data Model ◽  
Development Process ◽  
Storage System ◽  
Ontology Development ◽  
Software Library ◽  
Process Ontology ◽  
Classification Schemes ◽  
Speed Up ◽  
And Storage

To speed up the ontology development process, ontology developers are reusing all available ontological and non-ontological resources, such as classification schemes, thesauri, lexicons, and so forth, that have already reached some consensus. Non-ontological resources are highly heterogeneous in their data model and storage system (or implementation). The reuse of these non-ontological resources involves their re-engineering into ontologies. This paper presents a method for re-engineering non-ontological resources into ontologies. The method is based on so-called re-engineering patterns, which define a procedure that transforms the non-ontological resource components into ontology representational primitives using WordNet for making explicit the relations among the non-ontological resource terms. The paper also provides the description of NOR2O, a software library that implements the transformations suggested by the patterns. Finally, it depicts an evaluation of the method, patterns, and software library proposed.

ExtruOnt: An ontology for describing a type of manufacturing machine for Industry 4.0 systems

Semantic Web ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 887-909
Author(s):  
Víctor Julio Ramírez-Durán ◽  
Idoia Berges ◽  
Arantza Illarramendi
Keyword(s):  
Industry 4.0 ◽  
Development Process ◽  
Extrusion Process ◽  
Ontology Development ◽  
Development Effort ◽  
Close Collaboration ◽  
Domain Experts ◽  
Different Types ◽  
Types Of Information ◽  
Manufacturing Machine

Semantically rich descriptions of manufacturing machines, offered in a machine-interpretable code, can provide interesting benefits in Industry 4.0 scenarios. However, the lack of that type of descriptions is evident. In this paper we present the development effort made to build an ontology, called ExtruOnt, for describing a type of manufacturing machine, more precisely, a type that performs an extrusion process (extruder). Although the scope of the ontology is restricted to a concrete domain, it could be used as a model for the development of other ontologies for describing manufacturing machines in Industry 4.0 scenarios. The terms of the ExtruOnt ontology provide different types of information related with an extruder, which are reflected in distinct modules that constitute the ontology. Thus, it contains classes and properties for expressing descriptions about components of an extruder, spatial connections, features, and 3D representations of those components, and finally the sensors used to capture indicators about the performance of this type of machine. The ontology development process has been carried out in close collaboration with domain experts.

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