Web Crawler for Indexing Video e-Learning Resources: A YouTube Case Study

Abstract

The main objective of the current paper is to develop and validate an algorithm focused on automatically indexing YouTube e-learning resources about a certain domain of interest. After identifying the keywords specific to the desired domain, a web crawler is developed for evaluating video resources (from the YouTube platform) in terms of relevance for that domain. Once the most relevant video resources are found, they are indexed with the usage of a NER engine applied on their transcripts. In this manner, semantic queries can be used further in order to find exactly the needed information inside these multimedia resources. The crawler will repeat the indexing process daily in order to maintain the repository of semantically indexed videos up to date. The final chapter presents the obtained results together with the validation of the model.