Query Expansion as one of Query adaptation approaches, improves retrieval effectiveness of information retrieval. Pseudo-relevance feedback (PRF) is a Query Expansion approach that supposes top-ranked documents are relevant to the Query concept, and selects Expansion terms from top-ranked documents. However, Existing of irrelevant document in top-ranked documents is possible. Many approaches have been proposed for selecting relevant documents and ignoring irrelevant ones, which use clustering or classification of documents. Important issue in Query Expansion approaches is using relevant documents for selecting Expansion terms. In this paper, we propose clustering of pseudo-relevant documents based on Query sensitive similarity, which is efficient for placing similar documents together. Query sensitive similarity obtained good results in document retrieval rather than term-based similarity, is the reason for using in this paper. Clusters are ranked based on inner similarity, and some top ranked ones are selected for Query Expansion. Then, we extract Expansion terms from documents of selected clusters based on Term Frequency- Inverse document frequency (TF-IDF) scoring function. Conducted experiments over Medicine dataset (MED) shows that retrieval results for expanded queries with selected documents from clusters is better than basic retrieval (VSM) and Pseudo-relevance feedback. In addition, the effectiveness of retrieval is raised.

Introduction: Query Expansion is considered as an appropriate solution for solving the problem of short and ambiguous user queries. The purpose of this study is to conduct a systematic review of Persian language Query Expansion. Methodology: Current research is done with using a systematic review method based on Okoli & Schabram's Guidance. Searching in the scientific databases with related keywords led to 35 works in Persian language and 18 works in English language. By applying primary refinement, inclusion and exclusion criteria to study and after expert review, six Persian works and eight English works were selected for doing a systematic review. A checklist was designed and needed information extracted from the works. Finally, findings were processed to achieve four goals of the study: identifying methods, knowledge sources, test collections, and research gaps. Findings: The systematic review showed that 14 works deal with Query Expansion in Persian language. These works fall into four categories based on knowledge sources for term Expansion: relevance-based (eight works), knowledge-based (two works), web-based (two works), and combined-based resources (two works). Most of these studies have been done on news test collections and the Hamshahri newspaper corpus has been used almost in the half of researches as a knowledge source for term Expansion as well as test collection. Conclusion: There is much room to research in the field of Query Expansion in Persian language. Various knowledge sources, especially web-based ontologies and resources should be considered and used for Query Expansion in Persian language. Besides, the use of a standard test collection provides the researchers with the facility of comparing the different methods.

Term mismatch is the most important challege in web information retrieval. The term mismatch problem is defined as differences between user queries and contents of documents while referring to the same topic. Query Expansion methods deal with term mismatch by reformulating the queries to increase their term-overlap with relevant documents. In this paper, we proposed a Query Expansion framework based on a deep Siamese LSTM neural network. In addition, we defined the relevant relatedness for the first time and used this concept to label pairs made from user Query and candidate Query. Weakly-supervised labeled pairs are utilized in training of the deep Siamese network. The trained Siamese network provides labels for testset pairs in addition to contrastive loss values. The contrastive loss value reflects the cost of pulling together similar pairs. Pairs with minimum contrastive loss values are selected and merged together to form one expanded Query. Results of our tests showed that the proposed framework outperforms similar word embedding based Query Expansion methods.

Due to the increasing web, there are many challenges to establish a general framework for data mining and retrieving structured data from the Web. Creating an ontology is a step towards solving this problem. The ontology raises the main entity and the concept of any data in data mining. In this paper, we tried to propose a method for applying the "meaning" of the search system, But the problem for these methods is building a knowledge base that can be used for semantic search. The previous work interprets the Query in three ways:’semantic relation in ontology’, ‘co-occurrence in the document’, and ‘semantic relation from Thesaurus’. The proposed method has two parts. The first part, using domain ontology for classified web pages based on keyword and the concept in each domain and builds Fuzzy ontology as Knowledge Base and the next section offers a method for expanding the Query using built fuzzy ontology. In this paper, we tried to create knowledge base with WordNet as a comprehensive dictionary and extracted Sub string (phrases include multi words) from WordNet for each keyword in each domain ontology. The created Search engine was applied to an experimental system to evaluate the "precision -Recall” and it was revealed that applying the proposed method can improve Query Expansion 11% better in our experiments for precision.

A major problem in information retrieval is the difficulty to define the information needs of user and on the other hand, when user offers your Query there is a vast amount of information to retrieval. Different methods, therefore, have been suggested for Query Expansion which concerned with reconfiguring of Query by increasing efficiency and improving the criterion accuracy in the information retrieval system. Accordingly in this paper, in addition to propose a new coherent categorization for approaches, we proceed to detailed identify them, and proper functional criteria to evaluate each of these approaches are suggested.

Search engines start by typing several words and pressing the search button. Often, when searching for a specific person, they enter the name of the person and click the search button. This type of search leads to problems that many researchers are trying to solve. The search result for the search person is a combination of different information related to several people with common names. To solve this problem in this research, a solution to improve the search engine using semantic search is provided.The user Query is expanded automatically first. This Expansion is performed using the IT domain's ontology and its relationships, built using an existing ontology. Then a vector is created according to the user's interest. In this vector, both the user's interest (in the type of news being read) and the Query entered by the user, which has been expanded using domain's ontology, will be affected by a factor.Finally, the search results are sorted using this vector and calculating its similarity to the news vector. In fact, In this research, domain ontology is customized using user interests and searches are performed based on this. Finally, proposed method with the usual method of expanding the Query using ontology and the method of using keywords is compared. The result of the combined research is that by having large data without knowing the semantic structure of words, by associating words with each other and tasteful communication between users, the meaning of communication between words can be found. In addition to the use of ontology, it is also a textual search method using recommendatory methods that has more dominant power.

Query Expansion is a method for improving retrieval performance by supplementing an original Query with additional terms. This process improves the quality of search engine results and helps users to find the required information. In the recent years, different methods have been proposed in this area. In addition to such a variety of different approaches in this area and necessity of the study of their characteristics, the lack of a comprehensive classification based on candidate Expansion terms extraction methods and also suitable and complete criteria to evaluate them, make the precise study, comparison and evaluation of methods for Query Expansion and choosing appropriate method based on need difficult for researchers. Therefore, in this paper a new useful framework is presented. In the proposed framework, in addition to the identification of three basic approaches based on the candidate Expansion terms extraction methods for Query Expansion and expressing their properties, appropriate criteria for qualitative evaluation of these methods will be described. Next, the proposed approaches will be evaluated qualitatively based on these criteria. Using the systematic and structured framework proposed in this paper leads a useful platform for researchers to be provided for the comparative study of existing methods in the field, investigating their features specially their drawbacks to improve them and choosing appropriate method based on their needs.