TeKnowBase : a tool for enrichment of textbooks using discussion forums

Abstract

Several knowledge resources are available both online and o line in learning technical topics. Textbooks act as a basic reference with their general organization into sections where each section is dedicated in explaining a single topic. Other online resources like Wikipedia articles and its topic hierarchy help the users in structured learning of a speci c technical topic by providing them with the details on advanced applications. Various discussion forums aid the users in clarifying the doubts on real world implementation details of the technical topics. For an e ective learning of technical topics, all these features are to be curated. By making use of online knowledge resources,through TeKnowBase, we present our early explorations in trying to bridge the gaps in textbooks by providing more details on the technical topic to be learnt. To extract the discussions on the details of real world implementation and advanced applications of the topics, we make use the data in StackOver ow, a discussion forum on computer programming. Extraction of relevant discussions on a speci c topic is performed using query expansion with various keywords. Two approaches are used in the query expansion, one using the keywords from Wikipedia category hierarchy and the other using the keywords describing context of a topic in textbook. A database of topics is built from the index terms and their parent topics of textbooks. Using these parent topics as keywords, we expand our search in Stackover ow for extracting more relevant discussions on the selected topic. Keywords from the above mentioned resources helps in re ning the search and extraction of relevant discussions by setting the context from the textbook to learn a topic and by using the category hierarchy of Wikipedia. The results obtained from the expanded query search are evaluated manually. Both the techniques showed an improvement over the normal keyword search in extracting relevant discussions when queried using the search framework of Lucene and evaluated using the graded evaluation measure of DCG@k.