Master of Web Science

Abstract

As text sources are getting broader, measuring text similarity is becoming more compelling. Automatic
text classification, search engines and auto answering systems are samples of applications that rely on text
similarity. Learning management systems (LMS) are becoming more important since electronic media is getting more publicly available. As LMS continuously needs content enrichment and the web is getting richer,
automatic collection of learning materials becomes an innovative idea. Intelligent agents can be used with
a similarity measurement method to implement the automatic collection process. This paper presents a new
method for measuring text similarity using the well-known WordNet Ontology. The proposed method assumes
that a text is similar to another if it represents a more specific semantic. This is more suitable for LMS
content enrichment as learning content can usually be expanded by a more specific one. This paper shows
how the hierarchy of WordNet can be taken advantage of to determine the importance of a word. It is also
shown how similarity method within an e-learning system is exploited to achieve two goals. The first one is
the enrichment of the e-learning content, and the second is the detection of semantically similar questions in
e-learning questions banks.