Abstract In order to improve the retrieval performance, the query is reformulated by the process of Query expansion (QE). Most of the existing query expansion techniques do not consider the context of the terms present in the user's query which can result in low precision and recall. Through this paper, the query consisting of ambiguous terms (polysemy words) is expanded by selecting the terms, which are in close proximity to the query terms while context meaning of the terms is automatically incorporated. The basis of this query expansion method is to investigate the role of graph structure (which is being created for the query) and determining the importance of each node in the graph using WordNet. The relevant nodes representing word senses are identified from the graph and can be chosen as additional terms to be added to the query for improving the retrieval of web pages. The experiments, conducted on data sets of ambiguous queries show that proposed approach outperforms other query expansion methodologies by enhancing precision and recall.
Jain et al. REF proposed a method that investigate the role of graph structure for query expansion and determine the importance of each node in the graph using an external resource called WordNet.
Automatically incorporating context meaning for query expansion using graph connectivity measures
Progress in Artificial Intelligence
Progress in Artificial Intelligence
Computer Science