Content-based ontology ranking

Techniques to rank ontologies are crucial to aid and encourage the re-use of publicly available ontologies. This paper presents a system that obtains a list of ontologies from a search engine that contain the terms provided by a knowledge engineer and ranks them. The ranking of these ontologies will be done according to how many of the concept labels in those ontologies match a set of terms extracted from a corpus of documents related to the domain of knowledge identified by the knowledge engineer's original search terms

Information Granulation for the Design of Granular Information Retrieval Systems

With the explosive growth of the amount of information stored on computer networks such as the Internet, it is increasingly more difficult for information seekers to retrieve relevant information. Traditional document ranking functions employed by Internet search engines can be enhanced to improve the effectiveness of information retrieval (IR). This paper illustrates the design and development of a granular IR system to facilitate domain specific search. In particular, a novel computational model is designed to rank documents according the searchers’ specific granularity requirements. The initial experiments confirm that our granular IR system outperforms a classical vector-based IR system. In addition, user-based evaluations also demonstrate that our granular IR system is effective when compared with a well-known Internet search engine. Our research work opens the door to the design and development of the next generation of Internet search engines to alleviate the problem of information overload

The relationship of (perceived) epistemic cognition to interaction with resources on the internet

Information seeking and processing are key literacy practices. However, they are activities that students, across a range of ages, struggle with. These information seeking processes can be viewed through the lens of epistemic cognition: beliefs regarding the source, justification, complexity, and certainty of knowledge. In the research reported in this article we build on established research in this area, which has typically used self-report psychometric and behavior data, and information seeking tasks involving closed-document sets. We take a novel approach in applying established self-report measures to a large-scale, naturalistic, study environment, pointing to the potential of analysis of dialogue, web-navigation – including sites visited – and other trace data, to support more traditional self-report mechanisms. Our analysis suggests that prior work demonstrating relationships between self-report indicators is not paralleled in investigation of the hypothesized relationships between self-report and trace-indicators. However, there are clear epistemic features of this trace data. The article thus demonstrates the potential of behavioral learning analytic data in understanding how epistemic cognition is brought to bear in rich information seeking and processing tasks

Text Messaging a tool in e-Health services

HIV/AIDS continues to be a menace to the global community, especially in sub-Saharan Africa and South Africa is not an exception. The infection rate is continues to grow, in particular, among the young adults. Cell phones have been identified as one of the tools that can be used to overcome the challenge of information dissemination regarding HIV/AIDS among young adults because of its acceptability within this age group. Access to appropriate information can be a powerful for prevention and management of many chronic illnesses, including hypertension, diabetes and HIV/AIDS. Within the young adults age group information access by use of Short Messaging Services (SMS) becomes particularly appealing. In this regard, it is proposed to provide access to carefully screened information on HIV/AIDS within the context of frequently asked questions (FAQ) system. However, automating SMS-based information search and retrieval poses significant challenges because of the inherent noise in SMS communications. In the paper, a special corpus of SMS messages was collected based on a standardised question-answer collection. The SMS messages were then analysed, transcribed and classified, with the aim of building a dictionary of SMS-speak to English translations, with reference to HIV/AIDS