A Novel Kernel for Text Classification Based on Semantic and Statistical Information

In text categorization, a document is usually represented by a vector space model which can accomplish the classification task, but the model cannot deal with Chinese synonyms and polysemy phenomenon. This paper presents a novel approach which takes into account both the semantic and statistical information to improve the accuracy of text classification. The proposed approach computes semantic information based on HowNet and statistical information based on a kernel function with class-based weighting. According to our experimental results, the proposed approach could achieve state-of-the-art or competitive results as compared with traditional approaches such as the k-Nearest Neighbor (KNN), the Naive Bayes and deep learning models like convolutional networks

Semantically intelligent semi-automated ontology integration

An ontology is a way of information categorization and storage. Web Ontologies provide help in retrieving the required and precise information over the web. However, the problem of heterogeneity between ontologies may occur in the use of multiple ontologies of the same domain. The integration of ontologies provides a solution for the heterogeneity problem. Ontology integration is a solution to problem of interoperability in the knowledge based systems. Ontology integration provides a mechanism to find the semantic association between a pair of reference ontologies based on their concepts. Many researchers have been working on the problem of ontology integration; however, multiple issues related to ontology integration are still not addressed. This dissertation involves the investigation of the ontology integration problem and proposes a layer based enhanced framework as a solution to the problem. The comparison between concepts of reference ontologies is based on their semantics along with their syntax in the concept matching process of ontology integration. The semantic relationship of a concept with other concepts between ontologies and the provision of user confirmation (only for the problematic cases) are also taken into account in this process. The proposed framework is implemented and validated by providing a comparison of the proposed concept matching technique with the existing techniques. The test case scenarios are provided in order to compare and analyse the proposed framework in the analysis phase. The results of the experiments completed demonstrate the efficacy and success of the proposed framework

A Hotspot Discovery Method Based on Improved FIHC Clustering Algorithm

It was difficult to find the microblog hotspot because the characteristics of microblog were short, rapid, change and so on. A microblog hotspot detection method based on MFIHC and TOPSIS was proposed in order to solve the problem. Firstly, the calculation of HowNet similarity was used in the score function of FIHC, the semantic links between frequent words were considered, and the initial clusters based on frequent words were produced more accurately. Then the initial cluster of the text repletion of mircoblog was reduced, and the idea of Single-Pass clustering was used to the reduced topic cluster in order to get the Hotspot. At last, an improved TOPSIS model was used to sort the hot topics in order to get the rank of the hot topics. Compared with the other text clustering algorithms and hotspot detection methods, the method has good effect, and can be a more comprehensive response to the current hot topics