Managing Evolution in Software-Engineering Knowledge Management Systems

Mohamed, Abdulmajid Hissen

doi:10.1109/icdim.2007.369324

Cited by 2 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This dichotomy between explicit and tacit knowledge is vital, and is essential in understanding the challenges in the KM discipline. Tacit knowledge is known as individual knowledge that results from interaction between individuals or groups of people (Mohamed et al, 2006). On the other hand, explicit knowledge is viewed as being procedural or declarative knowledge (Anderson, 1983).…”

Section: Knowledge Managementmentioning

confidence: 99%

A Generic Model of Ontology to Visualize Information Science Domain (OIS)

Sawsaa

2017

Ontologies and Big Data Considerations for Effective Intelligence

View full text Add to dashboard Cite

A Generic Model of Ontology to Visualize Information Science Domain(OIS)Original Citation Sawsaa, Ahlam (2013) A Generic Model of Ontology to Visualize Information Science Domain(OIS). Doctoral thesis, University of Huddersfield.This version is available at http://eprints.hud.ac.uk/id/eprint/17545/ The University Repository is a digital collection of the research output of the University, available on Open Access. Copyright and Moral Rights for the items on this site are retained by the individual author and/or other copyright owners. Users may access full items free of charge; copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational or notforprofit purposes without prior permission or charge, provided:• The authors, title and full bibliographic details is credited in any copy;• A hyperlink and/or URL is included for the original metadata page; and • The content is not changed in any way.For more information, including our policy and submission procedure, please contact the Repository A great thanks also to all who gave me a helping hand to reach the end of my journey. V AbstractOntology has been a subject of many studies carried out in artificial intelligence ( While the bottom-up method starts with description of the domain to gain a hierarchal taxonomy.-Designing Ontocop system a novel method presented to support the developing process as specific virtual community of IS. The Ontocop consists of a number of experts in the subject area around the world. Their feedback and assessment improve the ontology development during the creating process.The findings of the research revealed that overall feedback from the IS community has been positive and that the model met the ontology quality criteria. It was appropriate to provide consistency and clear understanding of the subject area. OIS ontology unifies information science, which is composed of library science, computer science and archival science, by creating the theoretical base useful for further practical systems. Developing ontology of information science (OIS) is not an easy task, due to the complex nature of the field. It needs to be integrated with other ontologies such as social science, cognitive science, philosophy, law management and mathematics, to provide a basic knowledge for the semantic web and also to leverage information retrieval.

show abstract

Section: Knowledge Managementmentioning

confidence: 99%

A Generic Model of Ontology to Visualize Information Science Domain (OIS)

Sawsaa

2017

Ontologies and Big Data Considerations for Effective Intelligence

View full text Add to dashboard Cite

show abstract

“…Meanwhile, the deletion of critical cases can significantly reduce the competence of a CBR system, rendering certain classes of target problems permanently unsolvable [16]. Further details about artificial forgetting can be explored in ( [17], [18], [2], [19]). …”

Section: Machine Forgettingmentioning

confidence: 99%

“…For this reason, there must be a continual maintenance of knowledge captured by these systems. knowledge evolution issue has not been addressed so far [2].…”

Section: Introductionmentioning

confidence: 99%

A Constructive System Forgetting as part of Case-Based Reasoning

Mohamed

Ben-salem

Dagez

2018

int. jour. eng. com. sci

View full text Add to dashboard Cite

Abstract:The human behavior has always been very influential in systems engineering. In fact, AI methods and techniques are largely influenced by the human behavior in the form of mental and mechanical capabilities. The human learning, persevering and then recalling knowledge is the focal point in artificial intelligence research. But less attention is paid to forgetting as one of the characteristics that have a very positive role in human intelligence. This paper seeks to integrate data forgetting as part of the behavior of case-based reasoning systems. The aim is to improve the performance of CBR systems by filtering out irrelevant cases as part of the machine behavior in the form of CBR systems. The paper presents a prototypical implementation of the of a forgettable CBR system that provides course recommendations as part of student registration system. Experimental work has been carried out using historical data of postgraduate students in the Computer science department, Tripoli University, Libya.

show abstract

Managing Evolution in Software-Engineering Knowledge Management Systems

Cited by 2 publications

References 10 publications

A Generic Model of Ontology to Visualize Information Science Domain (OIS)

A Generic Model of Ontology to Visualize Information Science Domain (OIS)

A Constructive System Forgetting as part of Case-Based Reasoning

Contact Info

Product

Resources

About