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). Recent works have shown the benefits of keyword proximity search in querying XML documents in addition to text documents. For example, given query keywords over Shakespeare's plays in XML, the user might be interested in knowing how the keywords cooccur. In this paper, we focus on XML trees and define XML keyword, proximity queries to return the (possibly heterogeneous) set of minimum connecting trees (MCTs) of the matches to the individual keywords in the query. We consider efficiently executing keyword proximity queries on labeled trees (XML) in various settings: 1) when the XML database has been preprocessed and 2) when no indices are available on the XML database. We perform a detailed experimental evaluation to study the benefits of our approach and show that our algorithms considerably outperform prior algorithms and other applicable approaches. Many online or local data sources provide powerful querying mechanisms but limited ranking capabilities. For instance, PubMed allows users to submit highly expressive Boolean keyword queries, but ranks the query results by date only. However, a user would typically prefer a ranking by relevance, measured by an Information Retrieval (IR) ranking function. The naive approach would be to submit a disjunctive query with all query keywords, retrieve the returned documents, and then re-rank them. Unfortunately, such an operation would be very expensive due to the large number of results returned by disjunctive queries. In this paper we present algorithms that return the top results for a query, ranked according to an IR-style ranking function, while operating on top of a source with a Boolean query interface with no ranking capabilities (or a ranking capability of no interest to the end user). The algorithms generate a series of conjunctive queries that return only documents that are candidates for being highly ranked according to a relevance metric. Our approach can also be applied to other settings where the ranking is monotonic on a set of factors (query keywords in IR) and the source query interface is a Boolean expression of these factors. Our comprehensive experimental evaluation on the PubMed database and TREC dataset show that we achieve order of magnitude improvement compared to the current baseline approaches. We consider the problem of efficiently producing ranked results for keyword search queries over hyperlinked XML documents. Evaluating keyword search queries over hierarchical XML documents, as opposed to (conceptually) flat HTML documents, introduces many new challenges. First, XML keyword search queries do not always return entire documents, but can return deeply nested XML elements that contain the desired keywords. Second, the nested structure of XML implies that the notion of ranking is no longer at the granularity of a document, but at the granularity of an XML element. Finally, the notion of keyword proximity is more complex in the hierarchical XML data model. In this paper, we present the XRANK system that is designed to handle these novel features of XML keyword search. Our experimental results show that XRANK offers both space and performance benefits when compared with existing approaches. An interesting feature of XRANK is that it naturally generalizes a hyperlink based HTML search engine such as Google. XRANK can thus be used to query a mix of HTML and XML documents. Keyword search is a proven, user-friendly way to query HTML documents in the World Wide Web. We propose keyword search in XML documents, modeled as labeled trees, and describe corresponding efficient algorithms. The proposed keyword search returns the set of smallest trees containing all keywords, where a tree is designated as "smallest" if it contains no tree that also contains all keywords. Our core contribution, the Indexed Lookup Eager algorithm, exploits key properties of smallest trees in order to outperform prior algorithms by orders of magnitude when the query contains keywords with significantly different frequencies. The Scan Eager variant is tuned for the case where the keywords have similar frequencies. We analytically and experimentally evaluate two variants of the Eager algorithm, along with the Stack algorithm [13]. We also present the XKSearch system, which utilizes the Indexed Lookup Eager, Scan Eager and Stack algorithms and a demo of which on DBLP data is available at http: www.db.ucsd.edu projects xksearch. Finally, we extend the Indexed Lookup Eager algorithm to answer Lowest Common Ancestor (LCA) queries. Keyword-based search engines are in widespread use today as a popular means for Web-based information retrieval. Although such systems seem deceptively simple, a considerable amount of skill is required in order to satisfy non-trivial information needs. This paper presents a new conceptual paradigm for performing search in context, that largely automates the search process, providing even non-professional users with highly relevant results. This paradigm is implemented in practice in the IntelliZap system, where search is initiated from a text query marked by the user in a document she views, and is guided by the text surrounding the marked query in that document (“the context”). The context-driven information retrieval process involves semantic keyword extraction and clustering to automatically generate new, augmented queries. The latter are submitted to a host of general and domain-specific search engines. Search results are then semantically reranked, using context. Experimental results testify that using context to guide search, effectively offers even inexperienced users an advanced search tool on the Web. We showcase QUEST (QUEry generator for STructured sources), a search engine for relational databases that combines semantic and machine learning techniques for transforming keyword queries into meaningful SQL queries. The search engine relies on two approaches: the forward, providing mappings of keywords into database terms (names of tables and attributes, and domains of attributes), and the backward, computing the paths joining the data structures identified in the forward step. The results provided by the two approaches are combined within a probabilistic framework based on the Dempster-Shafer Theory. We demonstrate QUEST capabilities, and we show how, thanks to the flexibility obtained by the probabilistic combination of different techniques, QUEST is able to compute high quality results even with few training data and or with hidden data sources such as those found in the Deep Web. Inspired by the great success of information retrieval (IR) style keyword search on the web, keyword search on XML has emerged recently. The difference between text database and XML database results in three new challenges: (1) Identify the user search intention, i.e. identify the XML node types that user wants to search for and search via. (2) Resolve keyword ambiguity problems: a keyword can appear as both a tag name and a text value of some node; a keyword can appear as the text values of different XML node types and carry different meanings. (3) As the search results are sub-trees of the XML document, new scoring function is needed to estimate its relevance to a given query. However, existing methods cannot resolve these challenges, thus return low result quality in term of query relevance. In this paper, we propose an IR-style approach which basically utilizes the statistics of underlying XML data to address these challenges. We first propose specific guidelines that a search engine should meet in both search intention identification and relevance oriented ranking for search results. Then based on these guidelines, we design novel formulae to identify the search for nodes and search via nodes of a query, and present a novel XML TF*IDF ranking strategy to rank the individual matches of all possible search intentions. Lastly, the proposed techniques are implemented in an XML keyword search engine called XReal, and extensive experiments show the effectiveness of our approach. Although many studies have addressed the problem of finding Web pages seeking relevant and popular information from a query, very few have focused on the discovery of unexpected information. This paper provides and evaluates methods for discovering unexpected information for a keyword query. For example, if the user inputs "Michael Jackson," our system first discovers the unexpected related term "karate" and then returns the unexpected information "Michael Jackson is good at karate." Discovering unexpected information is useful in many situations. For example, when a user is browsing a news article on the Web, unexpected information about a person associated with the article can pique the user's interest. If a user is sightseeing or driving, providing unexpected, additional information about a building or the region is also useful. Our approach collects terms related to a keyword query and evaluates the degree of unexpectedness of each related term for the query on the basis of (i) the relationships of coordinate terms of both the keyword query and related terms, and (ii) the degree of popularity of each related term. Experimental results show that considering these two factors are effective for discovering unexpected information. Internet search engines have popularized the keyword-based search paradigm. While traditional database management systems offer powerful query languages, they do not allow keyword-based search. In this paper, we discuss DBXplorer, a system that enables keyword-based searches in relational databases. DBXplorer has been implemented using a commercial relational database and Web server and allows users to interact via a browser front-end. We outline the challenges and discuss the implementation of our system, including results of extensive experimental evaluation. In recent years, the amount of entities in large knowledge bases available on the Web has been increasing rapidly, making it possible to propose new ways of intelligent information access. Within the context of globalization, there is a clear need for techniques and systems that can enable multilingual and cross-lingual information access. In this paper, we present XKnowSearch!, a novel entity-based system for multilingual and cross-lingual information retrieval, which supports keyword search and also allows users to influence the search process according to their search intents. By leveraging the multilingual knowledge base on the Web, keyword queries and documents can be represented in their semantic forms, which can facilitate query disambiguation and expansion, and can also overcome the language barrier between queries and documents in different languages. The fast development of GPS equipped devices has aroused widespread use of spatial keyword querying in location based services nowadays. Existing spatial keyword query methodologies mainly focus on the spatial and textual similarities, while leaving the semantic understanding of keywords in spatial Web objects and queries to be ignored. To address this issue, this paper studies the problem of semantic based spatial keyword querying. It seeks to return the k objects most similar to the query, subject to not only their spatial and textual properties, but also the coherence of their semantic meanings. To achieve that, we propose novel indexing structures, which integrate spatial, textual and semantic information in a hierarchical manner, so as to prune the search space effectively in query processing. Extensive experiments are carried out to evaluate and compare them with other baseline algorithms. The increasing growth of the World Wide Web constantly enlarges the revenue generated by search engine advertising. Advertisers bid on keywords associated with their products to display their ads on the search result pages. Keyword suggestion methods are proposed to fill the gap between the keywords chosen by advertisers and the popular queries, through finding new relevant keywords according to some statistical information (for example, the keyword co-occurrence). However, there is little effort taking semantic information, such as concept hierarchy, into account. In this paper, we propose a novel keyword suggestion method that fully exploits the semantic knowledge among concept hierarchy. Given a keyword, we first match it with some relevant concepts. Then the relevant concepts are used with their hierarchy to fertilize the meanings of the keywords. Finally new keywords are suggested according to the concept information rather than the statistical co-occurrence of the keyword itself. Experimental results show that our proposed method can successfully provide suggestion that meets the accuracy and coverage requirements Web services are loosely coupled software components, published, located, and invoked across the web. The growing number of web services available within an organization and on the Web raises a new and challenging search problem: locating desired web services. Traditional keyword search is insufficient in this context: the specific types of queries users require are not captured, the very small text fragments in web services are unsuitable for keyword search, and the underlying structure and semantics of the web services are not exploited. We describe the algorithms underlying the Woogle search engine for web services. Woogle supports similarity search for web services, such as finding similar web-service operations and finding operations that compose with a given one. We describe novel techniques to support these types of searches, and an experimental study on a collection of over 1500 web-service operations that shows the high recall and precision of our algorithms. We present the design of a structured search engine which returns a multi-column table in response to a query consisting of keywords describing each of its columns. We answer such queries by exploiting the millions of tables on the Web because these are much richer sources of structured knowledge than free-format text. However, a corpus of tables harvested from arbitrary HTML web pages presents huge challenges of diversity and redundancy not seen in centrally edited knowledge bases. We concentrate on one concrete task in this paper. Given a set of Web tables T1,..., Tn, and a query Q with q sets of keywords Q1,..., Qq, decide for each Ti if it is relevant to Q and if so, identify the mapping between the columns of Ti and query columns. We represent this task as a graphical model that jointly maps all tables by incorporating diverse sources of clues spanning matches in different parts of the table, corpus-wide co-occurrence statistics, and content overlap across table columns. We define a novel query segmentation model for matching keywords to table columns, and a robust mechanism of exploiting content overlap across table columns. We design efficient inference algorithms based on bipartite matching and constrained graph cuts to solve the joint labeling task. Experiments on a workload of 59 queries over a 25 million web table corpus shows significant boost in accuracy over baseline IR methods. Unlike traditional database queries, keyword queries do not adhere to predefined syntax and are often dirty with irrelevant words from natural languages. This makes accurate and efficient keyword query processing over databases a very challenging task. In this paper, we introduce the problem of query cleaning for keyword search queries in a database context and propose a set of effective and efficient solutions. Query cleaning involves semantic linkage and spelling corrections of database relevant query words, followed by segmentation of nearby query words such that each segment corresponds to a high quality data term. We define a quality metric of a keyword query, and propose a number of algorithms for cleaning keyword queries optimally. It is demonstrated that the basic optimal query cleaning problem can be solved using a dynamic programming algorithm. We further extend the basic algorithm to address incremental query cleaning and top-k optimal query cleaning. The incremental query cleaning is efficient and memory-bounded, hence is ideal for scenarios in which the keywords are streamed. The top-k query cleaning algorithm is guaranteed to return the best k cleaned keyword queries in ranked order. Extensive experiments are conducted on three real-life data sets, and the results confirm the effectiveness and efficiency of the proposed solutions. An important facility to aid keyword search on XML data is suggesting alternative queries when user queries contain typographical errors. Query suggestion thus can improve users' search experience by avoiding returning empty result or results of poor qualities. In this paper, we study the problem of effectively and efficiently providing quality query suggestions for keyword queries on an XML document. We illustrate certain biases in previous work and propose a principled and general framework, XClean, based on the state-of-the-art language model. Compared with previous methods, XClean can accommodate different error models and XML keyword query semantics without losing rigor. Algorithms have been developed that compute the top-k suggestions efficiently. We performed an extensive experiment study using two large-scale real datasets. The experiment results demonstrate the effectiveness and efficiency of the proposed methods.