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.
