Automatic text classification using machine learning is significantly affected by the text representation model. The structural information in text is necessary for natural language understanding, which is usually ignored in vector-based representations. In this paper, we present a graph kernel-based text classification framework which utilises the structural information in text effectively through the weighting and enrichment of a graph-based representation. We introduce weighted co-occurrence graphs to represent text documents, which weight the terms and their dependencies based on their relevance to text classification. We propose a novel method to automatically enrich the weighted graphs using semantic knowledge in the form of a word similarity matrix. The similarity between enriched graphs, knowledge-driven graph similarity, is calculated using a graph kernel. The semantic knowledge in the enriched graphs ensures that the graph kernel goes beyond exact matching of terms and patterns to compute the semantic similarity of documents. In the experiments on sentiment classification and topic classification tasks, our knowledge-driven similarity measure significantly outperforms the baseline text similarity measures on five benchmark text classification datasets.

The rapidly increasing volume of medical text data, including biomedical literature and clinical records, presents difficulties to biomedical researchers and clinical practitioners. Automatic text classification is an important means for managing medical text data. The main challenge in medical text classification is the complex terminology used in these documents. Therefore, it is critical to handle synonymy, polysemy, and multi-word concepts so that classification is based on the meaning of these documents. The solution to this problem of complex terminology helps in building systems with better access to relevant data, resulting in more effective utilisation of the existing information. In this paper, we present a simple and effective approach to address this challenge. A concept graph is automatically constructed and enriched for each medical text document with the help of a domain-specific similarity matrix that is built using Unified Medical Language System (UMLS) concepts in the training documents. Medical text documents are compared based on their enriched concept graphs using a graph kernel. Classification is then done based on the comparison result. The benefit of this approach is that it allows the incorporation of domain knowledge into the classification framework. The experiments on biomedical abstracts and clinical reports classification show the effectiveness of the proposed approach. Based on evaluation metrics of precision, recall and F1-scores, our method achieves a significantly higher classification performance than other widely used similarity measures for similarity-based text classification.

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Information Technology (IT) is a vital and an integral part of every organization. IT executives are constantly faced with problems that are difficult to tackle and time consuming. Experience is required to solve these problems easier and faster. We can utilize case-based reasoning (CBR), data mining and information retrieval (IR) techniques to automate IT problem solving and experience management. In this paper, we propose an IT ontology-based system for semantic retrieval that increases the efficiency and quality of IT support service. The proposed approach retrieves similar problem/solution pairs based on the concepts in the query and performs better than the traditional keyword-based approach especially in cases where the keywords of the relevant documents do not match the keywords in the query.

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