Abstract —Vocabulary and Word mismatches are common problems in Information Retrieval Systems. Query Expansion(QE) gives a solution to these problems. Selection of key terms to expand the root query is a challenging task. Semantic relationships of a particular term in WordNet will improve the performance. Pseudo Relevance Feedback is one of the proven methods of QE which consider relevant terms to be expanded. Initial retrieved Items are considered as relevant to the query and further used to expand the query. Whenever the sense of key term is found, its lexical relationship synonym is considered for QE. The specific terms to be included in the initial query are selected automatically and the terms selected for the initial query are connected before expansion with OR Boolean operator. We found there is no Telugu WordNet available hence the proposed system is aimed to test on hand-crafted WordNet on limited text collection for Telugu language. Index Terms—Query expansion, pseudo relevance feedback, wordnet, synset, word sense disambiguation, parts of speech, normalization. I. INTRODUCTION Web is the best Source for Information and to Retrieve Information, where different techniques are used to give exact information needed by the users. Naive users are not familiar in writing structured queries. They submit short queries that do not consider the variety of terms used to describe a topic, which resulting in poor recall power [1]. Word mismatch is a common problem in Information Retrieval System. Vocabulary mismatch between naïve user and the author makes the search process more difficult. Due to lack of knowledge users face difficulties while searching for information in a knowledge repository to find the words that may retrieve the relevant and interested items or precise answers. On one hand, the vocabulary users employ in their queries may be different from the vocabulary within particular Internet resources; on the other hand, user‘s vocabulary may not be discriminating enough. Both cases lead to retrieval failure [2]. From the last three decades the research brings rich methods to improve results to the user query, where in PRF is one of the proven methods of query expansion for guaranteed better results. Even though the different approaches to PRF method are not in satisfactory levels, this paper introduces a novel approach to PRF method which aims to improve Manuscript received May 22, 2013; revised July 18, 2013. Ramakrishna Kolikipogu is with Computer Science and Information Technology, Hyderabad (e-mail:Krkrishna.csit@gmail.com). Padmaja Rani is with a professor & HOD of CSE, Jntuhcoe, Hyderabad. N. Swapna is with VREC, Hyderabad. precision without compromising recall growth. The proposed framework is aimed to test on Telugu item sets. Telugu is one of the Indian Languages belongs to Dravidian Family. Indian languages are rich in morphology [3]. Words in Telugu language have more variants, this identical feature is also a major problem to apply query expansion techniques. The Term selection plays a key role in retrieving relevant results for user query. In this method WordNet is used to extract candidate words for query expansion. We perform query expansion by generating lexical paraphrases of queries. These paraphrases replace content terms in the queries with their synsets. The resources used for selecting such term required Telugu POS Tagger or Morphological Analyzer, Stemmer and WordNet. This paper is organized into five chapters with introduction as Chapter 1. Chapter 2 is all about Query Expansion and the way how we use Query Expansion. Complete framework has been discussed in Chapter 3. Stop words are removed from each of these questions, and are passed to a shallow stemming program. The shallow stemmer is a rule based stemmer which stems the topic words to return the stem [4]. Use of only global models for query expansion may not affect the results. The integrated approach for query expansion gives observable growth in performance. In this paper our approach uses PRF (Local Model) with WordNet (Global Model). This provides a new platform for query expansion, which reduces the user interaction and preserves the meaning of the query. Off course using WordNet alone may not give effective results in terms of precision as the web is drastically increasing with new terminology day by day. In addition to this approach, to overcome the limitation of WordNet we also propose an online WordNet that would give updated lexical relations to the query terms. This paper aimed to find the difficulties in expanding the Telugu query and propose a solution to improve the Search results in Indian Information Retrieval (IIR). Once the framework is built the same model can be extended for other Indian Languages. Throughout the Paper examples are given in Telugu Language with phonetic representation. II. QUERY EXPANSION Query expansion is a step in deriving more relevant information to the interest of end user by adding new terms to the original query. Query Expansion is one of the promising approaches to deal with the vocabulary mismatch problem in information retrieval. Queries are often not well-formulated, but may be ambiguous, insufficiently precise, or use terminology that is specific to a country. Query Expansion is a process of reformulating the root query by adding an optimal set of terms that improves recall and precision. The Ramakrishna Kolikipogu, B. Padmaja Rani, and N. Swapna Pseudo Relevance Feedback by linking WordNet for Expanding Queries in Information Retrieval Process International Journal of Modeling and Optimization, Vol. 3, No. 5, October 2013 462 DOI: 10.7763/IJMO.2013.V3.321