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.

HOD2MLC

2018 ◽  
pp. 659-685
Author(s):  
Rishi Kanth Saripalle ◽  
Steven A. Demurjian ◽  
Michael Blechner ◽  
Thomas Agresta
Keyword(s):  
Domain Knowledge ◽  
Software Process ◽  
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.

HOD2MLC

2018 ◽  
pp. 1228-1253
Author(s):  
Rishi Kanth Saripalle ◽  
Steven A. Demurjian ◽  
Michael Blechner ◽  
Thomas Agresta
Keyword(s):  
Domain Knowledge ◽  
Software Process ◽  
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 Big Data: Are Biomedical and Health Informatics Training Programs Ready?

2014 ◽  
Vol 23 (01) ◽  
pp. 177-181 ◽  
Author(s):  
W. Hersh ◽  
A. U. Jai Ganesh ◽  
P. Otero
Keyword(s):  
Big Data ◽  
Programming Languages ◽  
Health Informatics ◽  
Domain Knowledge ◽  
Training Programs ◽  
Biomedical Data ◽  
Informatics Training ◽  
Statistical Programming ◽  
Tools And Techniques ◽  
And Training

Summary Objective: The growing volume and diversity of health and biomedical data indicate that the era of Big Data has arrived for healthcare. This has many implications for informatics, not only in terms of implementing and evaluating information systems, but also for the work and training of informatics researchers and professionals. This article addresses the question: What do biomedical and health informaticians working in analytics and Big Data need to know? Methods: We hypothesize a set of skills that we hope will be discussed among academic and other informaticians. Results: The set of skills includes: Programming - especially with data-oriented tools, such as SQL and statistical programming languages; Statistics - working knowledge to apply tools and techniques; Domain knowledge - depending on one’s area of work, bioscience or health care; and Communication - being able to understand needs of people and organizations, and articulate results back to them. Conclusion: Biomedical and health informatics educational programs must introduce concepts of analytics, Big Data, and the underlying skills to use and apply them into their curricula. The development of new coursework should focus on those who will become experts, with training aiming to provide skills in “deep analytical talent” as well as those who need knowledge to support such individuals.

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.

Building Collaborative Ontologies

2015 ◽  
pp. 305-324
Author(s):  
Tatiana A. Gavrilova ◽  
Irina A. Leshcheva
Keyword(s):  
Cognitive Style ◽  
Cognitive Styles ◽  
Knowledge Structure ◽  
Ontology Development ◽  
New Paradigm ◽  
Ontology Design ◽  
Analytical Work ◽  
Knowledge Structuring ◽  
Two Stages ◽  
Graph Map

The chapter describes the research performed within the KOMET (Knowledge and cOntent structuring via METhods of collaborative ontology design) project, which was aimed at developing a new paradigm for knowledge structuring. By knowledge structure, the authors define the main domain concepts and relations between them in a form of graph, map, or diagram. The approach considers the specifics of individual cognitive style. Two stages of research have been completed: research into correlations between the expert's individual cognitive style and the peculiarities of expert's subject domain ontology development; and study of correlations between the expert's individual cognitive style and the group ontology design (including the design performed in groups consisting of experts either of similar or of different cognitive styles). The results of this work can be applied to organizing collaborative ontology design (especially for research and learning purposes), data structuring, and other group analytical work. Implications for practice are briefly delineated.

A Trajectory Ontology Design Pattern for Semantic Trajectory Data Warehouses

2019 ◽  
pp. 83-104 ◽  
Author(s):  
Marwa Manaa ◽  
Thouraya Sakouhi ◽  
Jalel Akaichi
Keyword(s):  
Case Studies ◽  
Design Pattern ◽  
Design Patterns ◽  
Domain Knowledge ◽  
Data Warehouses ◽  
Trajectory Data ◽  
Semantic Approach ◽  
Mobility Data ◽  
Ontology Design ◽  
Ontology Design Pattern

Mobility data became an important paradigm for computing performed in various areas. Mobility data is considered as a core revealing the trace of mobile objects displacements. While each area presents a different optic of trajectory, they aim to support mobility data with domain knowledge. Semantic annotations may offer a common model for trajectories. Ontology design patterns seem to be promising solutions to define such trajectory related pattern. They appear more suitable for the annotation of multiperspective data than the only use of ontologies. The trajectory ontology design pattern will be used as a semantic layer for trajectory data warehouses for the sake of analyzing instantaneous behaviors conducted by mobile entities. In this chapter, the authors propose a semantic approach for the semantic modeling of trajectory and trajectory data warehouses based on a trajectory ontology design pattern. They validate the proposal through real case studies dealing with behavior analysis and animal tracking case studies.

Morphological Ontology Design Engineering

2016 ◽  
pp. 262-291 ◽  
Author(s):  
Joey Jansen van Vuuren ◽  
Louise Leenen ◽  
Marthie M. Grobler ◽  
Ka Fai Peter Chan ◽  
Zubeida C. Khan
Keyword(s):  
South African ◽  
Mixed Methods Research ◽  
Domain Knowledge ◽  
Design Science ◽  
Problem Definition ◽  
Design Engineering ◽  
Domain Experts ◽  
Ontology Design ◽  
Structured Problems ◽  
Science Methodology

In the Social-technical domain scientists are often confronted with a class of problems that are termed messy, ill-structured or wicked. These problems address complex issues that not well-defined, contain unresolvable uncertainties, and are characterized by a lack of common agreement on problem definition. This chapter proposes a new mixed methods research technique, Morphological Ontology Design Engineering (MODE), which can be applied to develop models for ill-structured problems. MODE combines three different research methodologies into a single, methodology. MODE draws from research paradigms that include exploratory and descriptive research approaches to develop models. General morphological analysis offers a systematic method to extract meaningful information from domain experts, while ontology based representation is used to logically represent domain knowledge. The design science methodology guides the entire process. MODE is applied to a case study where an ontological model is developed to support the implementation of a South African national cybersecurity policy.

scholarly journals An Overview of Ontologies in Virtual Reality-Based Training for Healthcare Domain

2021 ◽  
Vol 8 ◽  
Author(s):  
Ummul Hanan Mohamad ◽  
Mohammad Nazir Ahmad ◽  
Youcef Benferdia ◽  
Azrulhizam Shapi'i ◽  
Mohd Yazid Bajuri
Keyword(s):  
Virtual Reality ◽  
Domain Knowledge ◽  
State Of The Art ◽  
Comprehensive Strategy ◽  
Ontology Design ◽  
Medical Education And Training ◽  
Visualization Process ◽  
Basic Concepts ◽  
Disease Analysis ◽  
And Training

Virtual reality (VR) is one of the state-of-the-art technological applications in the healthcare domain. One major aspect of VR applications in this domain includes virtual reality-based training (VRT), which simplifies the complicated visualization process of diagnosis, treatment, disease analysis, and prevention. However, not much is known on how well the domain knowledge is shared and considered in the development of VRT applications. A pertinent mechanism, known as ontology, has acted as an enabler toward making the domain knowledge more explicit. Hence, this paper presents an overview to reveal the basic concepts and explores the extent to which ontologies are used in VRT development for medical education and training in the healthcare domain. From this overview, a base of knowledge for VRT development is proposed to initiate a comprehensive strategy in creating an effective ontology design for VRT applications in the healthcare domain.

scholarly journals Improving Data Management using Domain Knowledge

2018 ◽  
Author(s):  
Magdalena Ortiz
Keyword(s):  
Data Management ◽  
Incomplete Data ◽  
Domain Knowledge ◽  
Query Language ◽  
Reliable Data ◽  
World Assumptions ◽  
Database Query ◽  
Closed World ◽  
Database Query Language ◽  
Tools And Techniques

The development of tools and techniques for flexible and reliable data management is a long-standing challenge, ever more pressing in today’s data-rich world. We advocate using domain knowledge expressed in ontologies to tackle it, and summarize some research efforts to this aim that follow two directions. First, we consider the problem of ontology-mediated query answering (OMQA), where queries in a standard database query language are enriched with an ontology expressing background knowledge about the domain of interest, used to retrieve more complete answers when querying incomplete data. We discuss some of our contributions to OMQA, focusing on (i) expressive languages for OMQA, with emphasis on combining the open- and closed-world assumptions to reason about partially complete data; and (ii) OMQA algorithms based on rewriting techniques. The second direction we discuss proposes to use ontologies to manage evolving data. In particular, we use ontologies to model and reason about constraints on datasets, effects of operations that modify data, and the integrity of the data as it evolves.

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