Conceptual annotation of text patterns

Patterns are used as a fundamental means for analyzing data in many data mining applications. Many efficient techniques have been developed to discover patterns. However, the excessive number of discovered patterns and the lack of semantic information have made it difficult for a user to interpret and explore the patterns. A rough idea of the meanings of patterns can benefit the user in the process of exploring them. To address this issue, this paper presents a model for automatically annotating patterns with concepts. In addition, in a given context, the relative importance of each term that defines a concept is not the same. To define a context, there are a number of related information sources, such as documents, patterns, concepts, and an ontology. The question is which information sources are useful for estimating the relative importance of the terms? Should the most accurate one to be focused on or all of them be used to define the context? This research investigated these questions and defined an effective annotation context to estimate the relative importance of the terms, where the aim is to improve the performance of a machine that relies on the subject matter of a pattern set. The model is evaluated by comparing it with different baseline models on 2 standard datasets. The results show that the performance of the proposed model is significantly better.