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There is a wider scope in research on log data of computed aided learning and interactive learning. We have an enormous collection of log data of students’ activities during their learning process. Data Mining(DM) algorithms help us to discover knowledge and information from a huge and complex data sets. In a time-series log data, it is very complicated to verify the DM algorithms to mine the dataset. e-Learning activity log data is taken and converted into categorical data to predict the learning behavior of the students to implement the algorithms. The excavated knowledge can be used to modify the e-learning system. It is very easy from the result to note the slow learners and advanced learners well in advance before conducting an examination. Time series data is a numeric data that measures in a time period in successive order. The dataset used in this work is a UCI EPM dataset. It is a Non-Linear Time-Series Data. The converted dataset is used to apply a rule mining algorithm to predict the performance of a student. The measurements support and confidence will help us to predict the students’ performance. The results also have been compared with other classification mining algorithms. It assists to improve and to build an educational model on e-Learning. In turn it supports students, teachers, and educational system as well Learning Management System.

Ontologies are concept specifications and relations that have a major part in semantic web applications through provision of shared knowledge about real world objects ensuring reusability/interoperability among varied modules. So a semantic application should first have an ontology quality related query. Information retrieval (IR) is obtaining information resources relevant to an information need from various information resources. IR has changed over time with expansion of the internet and the arrival of modern graphical user interfaces/ mass storage devices. The aims are using ontologies knowledge to match object with queries on a semantic basis. Ontologies use has many challenges focussing on application of machine learning techniques on features extracted from ontologies concepts and Natural Language Processing. This paper focuses on classifying universities web pages through use of features extracted from an ontology based semantic interpretation.