Lights and shadows in creating a glossary about ontology engineering

Terminology ◽  
2013 ◽  
Vol 19 (2) ◽  
pp. 202-236 ◽  
Author(s):  
Mari Carmen Suárez-Figueroa ◽  
Guadalupe Aguado-de-Cea ◽  
Asunción Gómez-Pérez
Keyword(s):  
Domain Knowledge ◽  
Knowledge Organization ◽  
Ontology Engineering ◽  
Ontology Development ◽  
Precise Description ◽  
Key Issues ◽  
Ontology Building

This paper addresses the lack of an explicitly agreed and defined terminology in the ontology engineering field, and particularly, the need for a glossary, which consists of terms and definitions for actions when developing ontologies. The novelty of this paper lies in the precise description of a methodology for building a glossary with the processes and activities involved in ontology development as well as the relations between them (such as subtype, composition and synonym). The methodology proposed in this paper takes its inspiration from ideas taken from earlier research on methodological processes for creating multilingual terminological products and for defining a glossary in a particular domain as well as on domain knowledge organization. The description of our methodology includes the approach followed and the steps carried out, as well as the key issues that arise when the glossary was being created. So far as we are aware, this is the first attempt to normalize the terminology (denominations and definitions) of process and activities in ontology building.

An Ontology based on the Methodology Proposed by Ushold and King

2016 ◽  
Vol 7 (1) ◽  
pp. 13-26 ◽  
Author(s):  
Neha Jain ◽  
Lalit Sen Sharma
Keyword(s):  
Software Development ◽  
Ontology Engineering ◽  
Ontology Development ◽  
Semantic Technologies ◽  
Domain Specific ◽  
Development Processes ◽  
Ontology Building ◽  
Software Development Methodologies ◽  
Semantic Ontology ◽  
Standard Software

A number of methodologies are available in literature for ontology development but as the Ontology engineering field is relatively new, it is still unclear how the existing ontology building methodologies can be applied to develop semantic ontology models. In this work, firstly an overview of various ontology building methodologies and their limitations as compared to some standard software development methodologies are presented. Then the methodology proposed by Ushold and King is selected to build an ontology in e-banking domain. The challenge in this domain is to recognize, communicate and steadily improvise the banking solutions. The ontologies are prospective candidates to assist overcome these challenges and enhance interoperability of banking data and services. The study aims to provide direction for the application of existing ontology building methodologies in the Semantic Web Development processes of e-banking domain specific models which would enable their reusability and repeatability in other projects and strengthen the adoption of semantic technologies in the domain.

Ontological Conceptualization Based on the SKOS

2014 ◽  
Vol 14 (3) ◽  
Author(s):  
Farhad Ameri ◽  
Boonserm Kulvatunyou ◽  
Nenad Ivezic ◽  
Khosrow Kaikhah
Keyword(s):  
Domain Ontology ◽  
Knowledge Organization ◽  
Ontology Development ◽  
Stepping Stone ◽  
Simple Knowledge Organization System ◽  
Knowledge Organization System ◽  
Representation Formalism

Ontological conceptualization refers to the process of creating an abstract view of the domain of interest through a set of interconnected concepts. In this paper, a thesaurus-based methodology is proposed for systematic ontological conceptualization in the manufacturing domain. The methodology has three main phases, namely, thesaurus development, thesaurus evaluation, and thesaurus conversion and it uses simple knowledge organization system (SKOS) as the thesaurus representation formalism. The concept-based nature of a SKOS thesaurus makes it suitable for identifying important concepts in the domain. To that end, novel thesaurus evaluation and thesaurus conversion metrics that exploit this characteristic are presented. The ontology conceptualization methodology is demonstrated through the development of a manufacturing thesaurus, referred to as ManuTerms. The concepts in ManuTerms can be converted into ontological classes. The whole conceptualization process is the stepping stone to developing more axiomatic ontologies. Although the proposed methodology is developed in the context of manufacturing ontology development, the underlying methods, tools, and metrics can be applied to development of any domain ontology. The developed thesaurus can serve as a standalone lightweight ontology and its concepts can be reused by other ontologies or thesauri.

Ontologies application in the sharing economy domain: a systematic review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ummul Hanan Mohamad ◽  
Mohammad Nazir Ahmad ◽  
Ahmad Mujahid Ubaidillah Zakaria
Keyword(s):  
Domain Knowledge ◽  
Industrial Revolution ◽  
Common Knowledge ◽  
Sharing Economy ◽  
Ontology Development ◽  
Comprehensive Overview ◽  
Content Type ◽  
Digital Platforms ◽  
Application Ontology ◽  
Data Heterogeneity

PurposeThis systematic literature review (SLR) paper presents the overview and analysis of the existing ontologies application in the SE domain. It discusses the main challenges in terms of its ontologies development and highlights the key knowledge areas for subdomains in the SE domain that provides a direction to develop ontologies application for SE systematically. The SE is not as straightforward as the traditional economy. It transforms the existing economy ecosystem through peer-to-peer collaborations mediated by the technology. Hence, the complexity of the SE domain accentuates the need to make the SE domain knowledge more explicit.Design/methodology/approachFor the review, the authors only focus on the journal articles published from 2010 to 2020 and mentioned ontology as a solution to overcome the issues specific for the SE domain. The initial identification process produced 3,326 papers from 10 different databases.FindingsAfter applying the inclusion and exclusion criteria, a final set of 11 articles were then analyzed and classified. In SE, good ontology design and development is essential to manage digital platforms, deal with data heterogeneity and govern the interoperability of the SE systems. Yet the preference to build an application ontology, lack of perdurant design and minimal use of the existing standard for building SE common knowledge are deterring the ontology development in this domain. From this review, an anatomy of the SE key subdomain areas is visualized as a reference to further develop the domain ontology for the SE domain systematically.Originality/valueWith the arrival of the Fourth Industrial Revolution (4IR), the sharing economy (SE) has become one of the important domains whose impact has been explosive, and its domain knowledge is complex. Yet, a comprehensive overview and analysis of the ontology applications in the SE domain is not available or well presented to the research community.

HOD2MLC

2015 ◽  
Vol 10 (2) ◽  
pp. 16-42 ◽  
Author(s):  
Rishi Kanth Saripalle ◽  
Steven A. Demurjian ◽  
Michael Blechner ◽  
Thomas Agresta
Keyword(s):  
Domain Knowledge ◽  
Protein Gene ◽  
Ontology Development ◽  
Data Intensive ◽  
Ontology Design ◽  
Proper Perspective ◽  
Agile Software ◽  
Development Alternatives ◽  
Tools And Techniques ◽  
Compare And Contrast

Ontologies have gained increasing usage to augment an application with domain knowledge, particularly in healthcare, where they represent knowledge ranging from: bioinformatics data such as protein, gene, etc. to biomedical informatics such as diseases, diagnosis, symptoms, etc. However, the current ontology development efforts and process are data intensive and construction based, creating ontologies for specific applications/requirements, rather than designing an abstract ontological solution(s) that can be reusable across the domain using a well-defined design process. To address this deficiency, the work presented herein positions ontologies as software engineering artifact that allows them to be placed into the position to share the captured domain conceptualization and its vocabulary involving disparate domain backgrounds, that can then be created, imported, exported and re-used using different frameworks, tools and techniques. Towards this end, the authors propose an agile software process for ontologies referred to as the Hybrid Ontology Design & Development Model with Lifecycle,HOD2MLC. To place HOD2MLC into a proper perspective, they explore, compare, and contrast it to existing ontology design and development alternatives with respect their various phases as related to the authors' work and phases in varied SDP models.

scholarly journals Heterogeneous Data Clustering Considering Multiple User-provided Constraints

2019 ◽  
Vol 14 (2) ◽  
pp. 170-182 ◽  
Author(s):  
Yue Huang
Keyword(s):  
Heterogeneous Networks ◽  
Data Clustering ◽  
Domain Knowledge ◽  
General Framework ◽  
Heterogeneous Data ◽  
Similarity Measurement ◽  
Clustering Methods ◽  
Multiple User ◽  
Key Issues ◽  
Internal Information

Clustering on heterogeneous networks which consist of multi-typed objects and links has proved to be a useful technique in many scenarios. Although numerous clustering methods have achieved remarkable success, current clustering methods for heterogeneous networks tend to consider only internal information of the dataset. In order to utilize background domain knowledge, we propose a general framework for clustering heterogeneous data considering multiple user-provided constrains. Specifically, we summarize that three types of manual constraints on the object can be used to guide the clustering process. Then we propose the User- HeteClus algorithm to solve the key issues in the case of star-structure heterogeneous data, which incorporating the user constraint into similarity measurement between central objects. Experiments on a real-world dataset show the effectiveness of the proposed algorithm.

F-ONTOCOM: A Fuzzified Cost Estimation Approach for Ontology Engineering

2021 ◽  
Author(s):  
Sonika Malik ◽  
Sarika Jain
Keyword(s):  
Cost Estimation ◽  
Evaluation Criteria ◽  
Ontology Engineering ◽  
Estimation Accuracy ◽  
Effort Estimation ◽  
Wide Scale ◽  
Ontology Building ◽  
The Cost ◽  
Essential Prerequisite ◽  
Estimation Models

Estimating effort is an essential prerequisite for the wide-scale dispersal of ontologies. Not much attention has yet been paid to this essential aspect of ontology building. To date, ONTOCOM is the most prominent model for ontology cost estimation. Many factors influencing the building cost of an ontology are depicted by linguistic terms like Very High, High, . . . and so on; making them vague and indistinct. This fuzziness is quite uncertain and must be taken into consideration. The available effort estimation models do not consider the uncertainty of fuzziness. In this work, we propose an effort estimation methodology for ontology engineering using Fuzzy Logic i.e. F-ONTOCOM (Fuzzy-ONTOCOM) to overcome of uncertainty and imprecision. We have defined the corresponding Fuzzy sets for each effort multiplier and its associated linguistic value, and represented the same by triangular membership functions. F-ONTOCOM is applied to a dataset of 148 ontology projects and evaluated over various evaluation criteria. FONTOCOM outperforms the existing effort-estimation models; it has been concluded that F-ONTOCOM improves the cost estimation accuracy and estimated cost is very close to actual cost.

Methodology of Creating Ontology of Information Science (OIS)

2017 ◽  
pp. 510-517
Author(s):  
Ahlam F. Sawsaa ◽  
Joan Lu
Keyword(s):  
Set Theory ◽  
Domain Knowledge ◽  
Virtual Communities ◽  
Information Science ◽  
Theoretical Perspective ◽  
Ontology Development ◽  
Theoretical Base ◽  
Engineering Knowledge ◽  
Virtual Communities Of Practice

In the previous chapter we have discussed the main fields related to the research: ontological engineering, knowledge management, and Virtual communities of practice. As stated before, our concern is representing domain knowledge by creating OIS ontology. After reviewing the ontology literature to find an appropriate theoretical perspective focusing on the content-related variables for theoretical model construction, we found that theories can help to define formal ontological properties that contribute to characterising the concepts. Meanwhile, ontologists nowadays have a choice of formal frameworks which derive from formal logic, algebra, category theory, set theory and Mereotopology. However, to gain a better understand of OIS ontology development and its role in semantic web, the framework is established to describe the main theoretical base. The theoretical base of our framework is based on ontology theoretic.

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