Keyword search is the most popular querying technique on semistructured data. Keyword queries are simple and con- venient. However, as a consequence of their imprecision, the quality of their answers is poor and the existing algorithms do not scale satisfactorily. In this paper, we introduce the novel concept of cohesive keyword queries for tree data. Intuitively, a cohesiveness relationship on keywords indicates that they should form a cohesive whole in a query result. Cohesive keyword queries allow term nesting and keyword repetition. Although more expressive, they are as simple as flat keyword queries. We provide formal semantics for cohesive keyword queries rank- ing query results on the proximity of the keyword instances. We design a stack based algorithm which efficiently evaluates cohesive keyword queries. Our experiments demonstrate that our approach outperforms in quality previous filtering semantics and our algorithm scales smoothly on queries of even 20 keywords on large datasets.
Keyword queries facilitate the user with the ease of freely forming queries by using only keywords. Approaches that evaluate keyword queries are currently very popular especially in the web where numerous sources contribute data often with unknown structure and where end users with no specialized skills need to find useful information. However, the imprecision of keyword queries results often in low precision and or recall of the search systems. Some approaches (a) combine structural constraints @cite_40 with keyword search while others that (b) try to infer useful structural information implied by simple keyword queries @cite_5 @cite_10 by exploiting statistical information of the queries and the underlying datasets. These approaches require a minimum knowledge of the dataset or a heavy dataset preprocessing in order to be able to accurately assess candidate keyword query results.
@cite_5 @cite_40 @cite_10
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The prevalence of free text search in web search engines has inspired recent interest in keyword search on relational databases. Whereas relational queries formally specify matching tuples, keyword queries are imprecise expressions of the user’s information need. The correctness of search results depends on the user’s subjective assessment. As a result, the empirical evaluation of a keyword retrieval system’s effectiveness is essential. In this paper, we examine the evolving practices and resources for effectiveness evaluation of keyword searches on relational databases. We compare practices with the longer-standing full-text evaluation methodologies in information retrieval. In the light of this comparison, we make some suggestions for the future development of the art in evaluating keyword search effectiveness. One important challenge of current search engines is to satisfy the users' needs when they provide a poorly formulated query. When the pages matching the user's original keywords are judged to be unsatisfactory, query recommendation techniques are used to propose alternative queries and alter the result set. These techniques search for queries that are semantically similar to the user's original query, often searching for keywords that are similar to the keywords given by the user. However, when the original query is sufficiently ill-posed, the user's informational need is best met using entirely different keywords, and a substantially different query may be necessary. We propose a novel approach that is not based on the keywords of the original query. We intentionally seek out orthogonal queries, which are related queries that have (almost) no common terms with the user's query. This allows an orthogonal query to satisfy the user's informational need when small perturbations of the original keyword set are insufficient. By using this technique to generate query recommendations, we outperform several known approaches, being the best for long tail queries. Many applications require finding objects closest to a specified location that contains a set of keywords. For example, online yellow pages allow users to specify an address and a set of keywords. In return, the user obtains a list of businesses whose description contains these keywords, ordered by their distance from the specified address. The problems of nearest neighbor search on spatial data and keyword search on text data have been extensively studied separately. However, to the best of our knowledge there is no efficient method to answer spatial keyword queries, that is, queries that specify both a location and a set of keywords. In this work, we present an efficient method to answer top-k spatial keyword queries. To do so, we introduce an indexing structure called IR2-Tree (Information Retrieval R-Tree) which combines an R-Tree with superimposed text signatures. We present algorithms that construct and maintain an IR2-Tree, and use it to answer top-k spatial keyword queries. Our algorithms are experimentally compared to current methods and are shown to have superior performance and excellent scalability. Vast amount of medical information is increasingly available on the Web. As a result, seeking medical information through queries is gaining importance in the medical domain. The existing keyword-based search engines such as Google, Yahoo fail to suffice the needs of the health-care workers (who are well-versed with the domain knowledge required for querying) using these they often face results which are irrelevant and not useful for their tasks. In this paper, we present the need and the challenges for a user-level, domain-specific query language for the specialized document repositories of the medical domain. This topic has not been sufficiently addressed by the existing approaches including SQL-like query languages or general-purpose keyword-based search engines and document-level indexing based search. We aim to bridge the gap between information needs of the skilled semi-skilled domain users and the query capability provided by the query language. Overcoming such a challenge can facilitate effective use of large volume of information on the Web (and in the electronic health records (EHRs)repositories).