scholarly journals Feature analysis of ontology visualization methods and tools

2020 ◽  
Vol 1 (2) ◽  
pp. 61-77
Author(s):  
Merlin Florrence Joseph ◽  
Ravi Lourdusamy
Keyword(s):  
Feature Analysis ◽  
Knowledge Based ◽  
Ontology Visualization ◽  
Semantic Concepts ◽  
Visualization Tools ◽  
Tools And Techniques

Visualization is a technique of creating images, graphs or animations to share knowledge. Different kinds of visualization methods and tools are available to envision the data in an efficient way. The visualization tools and techniques enable the user to understand the knowledge in an easy manner. Nowadays most of the information is presented semantically which provides knowledge based retrieval of the information. Knowledge based visualization tools are required to visualize semantic concepts. This article analyses the existing semantic based visualization tools and plug-ins. The features and characteristics of these tools and plug-ins are analyzed and tabulated.

scholarly journals Towards a Knowledge-Based Recommendation System for Genomics Visualization

2020 ◽  
Author(s):  
Aditeya Pandey ◽  
Sehi L’Yi ◽  
Nils Gehlenborg
Keyword(s):  
Data Visualization ◽  
Biomedical Research ◽  
Recommender System ◽  
Recommendation System ◽  
Formal Training ◽  
Preliminary Design ◽  
Knowledge Based ◽  
Visualization Tools ◽  
Tools And Techniques ◽  
Selection Of

Analysis and interpretation of genomics data are the backbones of breakthroughs and discoveries in biomedical research. Visualization tools and techniques play a significant role in the workflow of genomics researchers, and they are regularly employed in the interpretation of genomics data. However, the vast majority of genomics researchers have little or no formal training in data visualization design. Therefore, they require guidance on designing effective visualizations for a given set of data and analysis tasks. In this poster, we present the methodology behind a recommender system for genomics data and our preliminary design of a knowledge-based recommendation system. The system allows genomics researchers to navigate through a selection of visualization options and identify the techniques that meet their preferences and requirements.

Fuzzy Methods in Data Mining

2011 ◽  
pp. 907-912 ◽  
Author(s):  
Eyke Hüllermeier
Keyword(s):  
Data Mining ◽  
Knowledge Acquisition ◽  
Control Function ◽  
Knowledge Discovery In Databases ◽  
Knowledge Based ◽  
Automated Learning ◽  
Long Time ◽  
Basic Ideas ◽  
Inference Techniques ◽  
Tools And Techniques

Tools and techniques that have been developed during the last 40 years in the field of fuzzy set theory (FST) have been applied quite successfully in a variety of application areas. A prominent example of the practical usefulness of corresponding techniques is fuzzy control, where the idea is to represent the input-output behaviour of a controller (of a technical system) in terms of fuzzy rules. A concrete control function is derived from such rules by means of suitable inference techniques. While aspects of knowledge representation and reasoning have dominated research in FST for a long time, problems of automated learning and knowledge acquisition have more and more come to the fore in recent years. There are several reasons for this development, notably the following: Firstly, there has been an internal shift within fuzzy systems research from “modelling” to “learning”, which can be attributed to the awareness that the well-known “knowledge acquisition bottleneck” seems to remain one of the key problems in the design of intelligent and knowledge-based systems. Secondly, this trend has been further amplified by the great interest that the fields of knowledge discovery in databases (KDD) and its core methodical component, data mining, have attracted in recent years. It is hence hardly surprising that data mining has received a great deal of attention in the FST community in recent years (Hüllermeier, 2005). The aim of this chapter is to give an idea of the usefulness of FST for data mining. To this end, we shall briefly highlight, in the next but one section, some potential advantages of fuzzy approaches. In preparation, the next section briefly recalls some basic ideas and concepts from FST. The style of presentation is purely non-technical throughout; for technical details we shall give pointers to the literature.

Creating an educational quality improvement program for radiation oncology residents in McGill University.

2017 ◽  
Vol 35 (8_suppl) ◽  
pp. 118-118
Author(s):  
Catherine Anne Pembroke ◽  
Alain Biron ◽  
Joanne Alfieri
Keyword(s):  
Radiation Oncology ◽  
Self Assessment ◽  
Improvement Program ◽  
Post Graduate ◽  
Knowledge Based ◽  
Modern Health Care ◽  
Mcgill University ◽  
The Individual ◽  
Tools And Techniques ◽  
Attending Physician

118 Background: Quality insurance (QI) is a pillar of good clinical governance and is at the centre of modern health care. The Royal College of Physicians and Surgeons of Canada CanMeds 2015 have now mandated that QI should be taught and the competencies assessed in all post-graduate residency programs. To our knowledge, this is the first attempt to create a post-graduate QI curriculum amongst radiation oncology trainees. We aim to describe the feasibility of introducing these professional skills which should be integral to every training program. Methods: A QI team has been created within the department of Radiation Oncology at McGill University consisting of a clinical fellow and 3 staff physicians. QI teaching will take place in a longitudinal manner with the mandatory curriculum divided into foundation, intermediate and advanced competencies depending on years of seniority. Teaching is delivered by a combination of two academic half days, consisting of didactic lectures and practical workshops, and self-directed online modules. Each resident during the intermediate years (PGY2-4) will complete a QI project in 9 months under the supervision of an attending physician. The resident will become well versed with QI tools and techniques by presenting their project at specific 3-monthly time points to their supervisor and QI team. In June we will host a QI day where a QI scholar will be invited to teach, each resident will present their project and merit prizes will be awarded. Formal mandatory assessments will take place with a combination of self-assessment, QI- knowledge based assessments (QI-KATs) and balanced score cards. Results: The curriculum has been developed with input from McGill University curriculum and assessment experts. This is a pilot program for the academic 2016/17 year. We are currently meeting our pre-defined milestones. The program will be formally evaluated and adapted to ensure sustainability. Conclusions: The QI skills gained will enable the individual to maintain the highest standards throughout their subsequent careers. A robust, interactive, sustainable curriculum will ensure that this is delivered effectively within radiation oncology and act as a model for all residency programmes.

Nondestructive Evaluation of Offshore Structures Using Fracture Mechanics

1988 ◽  
Vol 41 (2) ◽  
pp. 36-49 ◽  
Author(s):  
S. Dharmavasan ◽  
W. D. Dover
Keyword(s):  
Fracture Mechanics ◽  
Nondestructive Evaluation ◽  
Offshore Structures ◽  
Knowledge Based Systems ◽  
Knowledge Based ◽  
Non Destructive Evaluation ◽  
Computer Aided ◽  
Cad Cam ◽  
Tools And Techniques ◽  
Non Destructive

Developments in fast modelling of processes, materials response and methodology in combination with appropriate knowledge based systems opens up the possibility of linking CAD/CAM with Computer Aided Serviceability (CAS) for use in a few industries. This paper reviews the tools and techniques which are available and being developed to implement this philosophy in non-destructive evaluation of offshore structures using fracture mechanics.

Big Data Visualization Tools and Techniques

2022 ◽  
pp. 590-621
Author(s):  
Obinna Chimaobi Okechukwu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Visualization ◽  
Data Sets ◽  
New Paradigm ◽  
Volume Velocity ◽  
Big Data Visualization ◽  
Visualization Tools ◽  
Tools And Techniques ◽  
Data Analysis Methods

In this chapter, a discussion is presented on the latest tools and techniques available for Big Data Visualization. These tools, techniques and methods need to be understood appropriately to analyze Big Data. Big Data is a whole new paradigm where huge sets of data are generated and analyzed based on volume, velocity and variety. Conventional data analysis methods are incapable of processing data of this dimension; hence, it is fundamentally important to be familiar with new tools and techniques capable of processing these datasets. This chapter will illustrate tools available for analysts to process and present Big Data sets in ways that can be used to make appropriate decisions. Some of these tools (e.g., Tableau, RapidMiner, R Studio, etc.) have phenomenal capabilities to visualize processed data in ways traditional tools cannot. The chapter will also aim to explain the differences between these tools and their utilities based on scenarios.

scholarly journals MLGrafViz: multilingual ontology visualization plug-in for protégé

2021 ◽  
Vol 2 (1) ◽  
pp. 43-48
Author(s):  
Merlin Florrence
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Knowledge Based Systems ◽  
Natural Languages ◽  
The Core ◽  
Knowledge Based ◽  
Ontology Visualization ◽  
Language User ◽  
Core Ontology

Natural Language Processing (NLP) is rapidly increasing in all domains of knowledge acquisition to facilitate different language user. It is required to develop knowledge based NLP systems to provide better results.  Knowledge based systems can be implemented using ontologies where ontology is a collection of terms and concepts arranged taxonomically.  The concepts that are visualized graphically are more understandable than in the text form.   In this research paper, new multilingual ontology visualization plug-in MLGrafViz is developed to visualize ontologies in different natural languages. This plug-in is developed for protégé ontology editor. This plug-in allows the user to translate and visualize the core ontology into 135 languages.

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