A Survey on Keyword Diversification Over XML Data

Keyword queries are those terms that users enter and use to retrieve documents that have all or any of those terms. They are the most familiar and popular method used by ordinary users to search data. Keyword queries are highly ambiguous. Keyword search querying has emerged as one of the most effective way for information discovery, especially over HTML documents in the World Wide Web. Because of its simplicity keyword queries are one of the most effective ways for information discovery in World Wide Web. When using Keyword queries users do not have to learn a complex query language, nor needs him to have any prior knowledge about the structure of the underlying data. When considering the keyword query interpretations, a single keyword query interpretation will not be sufficient to satisfy the user. Many interpretations may yield unnecessary results. This will lead to user’s dissatisfaction. Diversification is mainly meant to minimize user’s dissatisfaction. The keyword query should return the major possible interpretations for the keywords in the underlying database. This will enable the user to easily select the intended interpretation. In the case of information retrieval (IR) keyword queries always retrieve a list of relevant documents and it needs to be analyzed manually one by one. But keyword queries over structured data give a more direct and effective way of diversification. In the case of keyword search over structured or semi-structured database if a keyword comes as value of more than one attribute, then each occurrence can be taken as different interpretations. Each interpretation will yield different results. Keyword Query Diversification can be defined as for a given keyword query over the XML dataset, the user should get a result set of top-k results, where each result should be relevant to the given keyword query and they should be maximum different to each other [1]. Diversification is done based on the underlying data that is to be searched. There are many approaches for keyword query diversification over XML data. These approaches derive different search intentions for each keyword in the query. This paper presents a comparative study on these different approaches. And also identifies context-based keyword diversification is the most effective method for keyword query diversification which will automatically diversifies the given keyword query based on different contexts of the keywords in the XML data. The method is to extract feature terms for each keyword in the given keyword query. And to generate query candidates of high relevance and novelty. Then search results are retrieved for those query candidates. This method can efficiently retrieve relevant search results, the method works effectively with purely text based data set. The existing method cannot retrieve images and text formats like italics and bold as such. The proposed system will overcome this drawback by considering images and text formats while diversification. It can also provide effective query suggestions to users.