In this thesis, the concept of mobile messaging awareness has been investigated by designing and implementing a framework which is able to annotate the short text messages with context ontology for semantic reasoning inference and classification purposes. The annotated metadata of text message keywords are identified and annotated with concepts, entities and knowledge that drawn from ontology without the need of learning process and the proposed framework supports semantic reasoning based messages awareness for categorization purposes. The first stage of the research is developing the framework of facilitating mobile communication with short text annotated messages (SAMS), which facilitates annotating short text message with part of speech tags augmented with an internal and external metadata. In the SAMS framework the annotation process is carried out automatically at the time of composing a message. The obtained metadata is collected from the device’s file system and the message header information which is then accumulated with the message’s tagged keywords to form an XML file, simultaneously. The significance of annotation process is to assist the proposed framework during the search and retrieval processes to identify the tagged keywords and The Semantic Web Technologies are utilised to improve the reasoning mechanism. Later, the proposed framework is further improved “Contextual Ontology based Short Text Messages reasoning (SOIM)”. SOIM further enhances the search capabilities of SAMS by adopting short text message annotation and semantic reasoning capabilities with domain ontology as Domain ontology is modeled into set of ontological knowledge modules that capture features of contextual entities and features of particular event or situation. Fundamentally, the framework SOIM relies on the hierarchical semantic distance to compute an approximated match degree of new set of relevant keywords to their corresponding abstract class in the domain ontology. Adopting contextual ontology leverages the framework performance to enhance the text comprehension and message categorization. Fuzzy Sets and Rough Sets theory have been integrated with SOIM to improve the inference capabilities and system efficiency. Since SOIM is based on the degree of similarity to choose the matched pattern to the message, the issue of choosing the best-retrieved pattern has arisen during the stage of decision-making. Fuzzy reasoning classifier based rules that adopt the Fuzzy Set theory for decision making have been applied on top of SOIM framework in order to increase the accuracy of the classification process with clearer decision. The issue of uncertainty in the system has been addressed by utilising the Rough Sets theory, in which the irrelevant and indecisive properties which affect the framework efficiency negatively have been ignored during the matching process.

Variability of transgene expression levels resulting from gene silencing is considered as ahindrance to the successful application of plant genetic engineering. Towards alleviatinggene silencing, I decided to screen for novel genes involved in transgene silencing and toinvestigate how these genes regulate plant development. Genes encoding putative chromatinremodeling factors, especially those including a SET domain, were selected as candidatetargets. A bioinformatic analysis of the Arabidopsis SET genes (AtSET) was performed andthese genes were classified into 6 groups based on the domain architecture. RNA interference (RNAi) vectors were constructed for ~ 20 AtSET genes and wereintroduced into both wild type lines and transgenic lines silenced for a GFP reporter gene.Surprisingly, altered developmental phenotypes were only observed for three constructs,raising questions as to the effectiveness of the RNAi approach for the chosen Arabidopsissystem. To assess this situation, I targeted a phytoene desaturase (PDS) gene using the sameRNAi approach. Inactivation of PDS renders plant a readily identifiable phenotype. Whereasthe RNAi penetrance in Arabidopsis can be very high, the expressivity of RNAi in varioustissues and among different plants can vary dramatically. Contradictory to previous reports,I found that there is correlation between transcript level and silencing phenotype. Possiblereasons for this discrepancy are discussed. No apparent correlation between transgene copynumber and RNAi phenotypes was observed. Among the three RNAi constructs that caused an abnormal development inArabidopsis, K-23 which targets SuvR3 has the highest expressivity and could reactivate asilenced GFP locus. SuvR3 RNAi lines were selfed for six generations and were screenedfor morphological phenotypes. Abnormal number of flower organs, loss of viability of malegametophytes, and decreased seedling germination percentage were found in SuvR3 RNAilines. A progressive increase in both severity and frequency of abnormal phenotypes wereseen in subsequent generations, suggesting an epigenetic regulatory mechanism involvedwith SuvR3. Alternative splicing of SuvR3 was also observed in most of Arabidopsis tissues.One of the protein isoforms, SuvR3, lacks 16 amino acids within the highly conserved SETdomain. Possible effects of isoform interaction are proposed.

Variability of transgene expression levels resulting from gene silencing is considered as ahindrance to the successful application of plant genetic engineering. Towards alleviatinggene silencing, I decided to screen for novel genes involved in transgene silencing and toinvestigate how these genes regulate plant development. Genes encoding putative chromatinremodeling factors, especially those including a SET domain, were selected as candidatetargets. A bioinformatic analysis of the Arabidopsis SET genes (AtSET) was performed andthese genes were classified into 6 groups based on the domain architecture. RNA interference (RNAi) vectors were constructed for ~ 20 AtSET genes and wereintroduced into both wild type lines and transgenic lines silenced for a GFP reporter gene.Surprisingly, altered developmental phenotypes were only observed for three constructs,raising questions as to the effectiveness of the RNAi approach for the chosen Arabidopsissystem. To assess this situation, I targeted a phytoene desaturase (PDS) gene using the sameRNAi approach. Inactivation of PDS renders plant a readily identifiable phenotype. Whereasthe RNAi penetrance in Arabidopsis can be very high, the expressivity of RNAi in varioustissues and among different plants can vary dramatically. Contradictory to previous reports,I found that there is correlation between transcript level and silencing phenotype. Possiblereasons for this discrepancy are discussed. No apparent correlation between transgene copynumber and RNAi phenotypes was observed. Among the three RNAi constructs that caused an abnormal development inArabidopsis, K-23 which targets SuvR3 has the highest expressivity and could reactivate asilenced GFP locus. SuvR3 RNAi lines were selfed for six generations and were screenedfor morphological phenotypes. Abnormal number of flower organs, loss of viability of malegametophytes, and decreased seedling germination percentage were found in SuvR3 RNAilines. A progressive increase in both severity and frequency of abnormal phenotypes wereseen in subsequent generations, suggesting an epigenetic regulatory mechanism involvedwith SuvR3. Alternative splicing of SuvR3 was also observed in most of Arabidopsis tissues.One of the protein isoforms, SuvR3, lacks 16 amino acids within the highly conserved SETdomain. Possible effects of isoform interaction are proposed.