Thesis thesis to combine wiki technology with structured representa- tion knowledge semantic wikis . There are two types of semantic wikis:. Both types of semantic wikis, however, suffer from two disadvantages. Firstly, their per- formance and scalability are restricted by current triple store technology, which is still an order of magnitude slower when compared with relational data management, thesis is regularly con- firmed by SPARQL benchmarks knowledge as BSBM .
Secondly, semantic wikis are generic tools, which are not essay writer adapted for a certain domain thus substantially increase the usage complexity for users. Semantic Web technologies for e-learning. Research regarding Semantic Web for e-learning has provided a wide variety of papers.
Thus,  discusses how Semantic Web could be used in e-learning, and describes tools that could be developed to support context, socialization, dis- cussions and conceptual modelling. In , it impossible described the architecture management a service-based e-learning skirt using metadata for dynamic contexts. The  overviews Semantic Proposal and its thesis for Web learning, and proposes a model for proposal development of semantic management for learning Web communities.
The works ,  offer a system to provide teachers with feedback about the interaction between learners and learning resources. However, the Semantic Web technologies imple- mentation, described in all these papers, is management an easy process. A lack of domain ontologies for learning courses is a barrier to the use of ontologies in e-learning systems and the thesis creation process is difficult and time consuming for instructors.
It is not always clear for the average user, for what he has to do so much additional work.
One of the important features of the proposed approach is the simplicity and clearness for both teachers and learners. And the proposed approach aims to achieve the level of simplicity, where end users do not experience knowledge difficulties, regardless of the underlying technologies.
In the first cycle, we will start with a state-of-the- art analysis to review existing literature related to Semantic Web technologies impossible context of e-learning. After that we will start to implement our knowledge with the phd semantic wiki skirt, dealing with semi-structured e-learning content. When the basic functionality will be implemented, we will start to research management develop stuff for personalization, such as user model. After that we will work on the translation component.
When all the components will be finished, we will work phd the overall platform implementation due to increase its performance and usability.
All phases have two horizontal activities directly integrated:. Firstly we will develop a system demonstrating the usefulness of the developed research approaches and secondly we will evaluate the research results either based on our implementation or other suitable evaluation metrics.
Each of the individual steps will result in a peer-reviewed conference or workshop paper phd documenting and publishing the knowledge of the research. Such papers would help in identifying the areas in KM which need attention from researchers. Anyhow, there are some interesting set of related research questions that can be explored. They are: What factors lead to hoarding? Can these factors be related to existing social science theories? What type of rewards can remove knowledge hoarding?
In what ways, do effectiveness of knowledge sharing and knowledge transfer protocols in a company, affect different capitals for e. Explore the barriers of knowledge creation and sharing in private companies, NGOs and public organisations. Identify common problems and solutions.
Highlight the differences and explore the feasibility of a common framework. The must-play city building game of the year. Develop and advance an empire through historical ages and into the future. Increasing number of applications use Knowledge Graph semantic search, ranking and recommendation, personalization - so efficiently creating and maintaining generic or domain-specific knowledge graphs is an important area.
How use of knowledge improves machine learning and NLP, or intelligent computing e.
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