Retrieving keyworded subgraphs with graph ranking score

Keyword queries have long been popular to search engines and to the information retrieval community and have recently gained momentum for its usage in the expert systems community. The conventional semantics for processing a user query is to find a set of top-k web pages such that each page contains all user keywords. Recently, this semantics has been extended to find a set of cohesively interconnected pages, each of which contains one of the query keywords scattered across these pages. The keyword query having the extended semantics (i.e., more than a list of keywords hyperlinked with each other) is referred to the graph query. In case of the graph query, all the query keywords may not be present on a single Web page. Thus, a set of Web pages with the corresponding hyperlinks need to be presented as the search result. The existing search systems reveal serious performance problem due to their failure to integrate information from multiple connected resources so that an efficient algorithm for keyword query over graph-structured data is proposed. It integrates information from multiple connected nodes of the graph and generates result trees with the occurrence of all the query keywords. We also investigate a ranking measure called graph ranking score (GRS) to evaluate the relevant graph results so that the score can generate a scalar value for keywords as well as for the topology.     

基于短期记忆与遗忘系数的用户个性化建模方法研究

搜索引擎的一个标准是不同的用户用相同的查询条件检索时,返回的结果相同。为解决准确性问题,个性化搜索引擎被提出,它可以根据用户的不同个性化特征提供不同的搜索结果。然而,现有的方法更注重用户的长时记忆和独立的用户日志文件,从而降低了个性化搜索的有效性。获取用户短时记忆模型来提供准确有效的用户偏好的个性化搜索方法被广泛采用。首先,根据基于查询关键词的相关概念生成短期记忆模型;接着,基于用户的时序有效点击数据生成用户个性化模型;最后,在用户会话中引入了遗忘因子来优化用户个性化模型。实验结果表明,所提出的方法可以较好地表达用户信息需求,较为准确地构建用户的个性化模型。     

On solving efficiently the view selection problem under bag and bag-set semantics

In this paper, we investigate the problem of view selection for workloads of conjunctive queries under bag and bag-set semantics. In particular, for both semantics we aim to limit the search space of candidate viewsets. We also start delineating boundaries between query workloads for which certain even more restricted search spaces suffice. They suffice in the sense that they do not compromise optimality in that they contain at least one of the optimal solutions. We start with the general case for both bag and bag-set semantics, where we give a tight condition that candidate views can satisfy and still the search space (thus limited) does contain at least one optimal solution. We show that these results, for both semantics, reduce the size of the search space significantly. Further on, due to this analysis for both semantics, a delineation of the space of viewsets and the space of the corresponding equivalent rewritings for a certain conjunctive query workload is given. We show that for chain query workloads under both bag and bag-set semantics, taking only chain views may miss optimal solutions, whereas, if we further limit the queries to be path-queries (i.e., chain queries over a single binary relation), then, under bag semantics, path-views suffice. Concentrating to bag-set semantics, we show that the path-viewsets do not suffice for every path-query workload.     

Personalized Concept-Based Clustering of Search Engine Queries

The exponential growth of information on the Web has introduced new challenges for building effective search engines. A major problem of web search is that search queries are usually short and ambiguous, and thus are insufficient for specifying the precise user needs. To alleviate this problem, some search engines suggest terms that are semantically related to the submitted queries so that users can choose from the suggestions the ones that reflect their information needs. In this paper, we introduce an effective approach that captures the user's conceptual preferences in order to provide personalized query suggestions. We achieve this goal with two new strategies. First, we develop online techniques that extract concepts from the web-snippets of the search result returned from a query and use the concepts to identify related queries for that query. Second, we propose a new two-phase personalized agglomerative clustering algorithm that is able to generate personalized query clusters. To the best of the authors' knowledge, no previous work has addressed personalization for query suggestions. To evaluate the effectiveness of our technique, a Google middleware was developed for collecting clickthrough data to conduct experimental evaluation. Experimental results show that our approach has better precision and recall than the existing query clustering methods.     

A graphical user interface for Boolean query specification

Online information repositories commonly provide keyword search facilities through textual query languages based on Boolean logic. However, there is evidence to suggest that the syntactic demands of such languages can lead to user errors and adversely affect the time that it takes users to form queries. Users also face difficulties because of the conflict in semantics between AND and OR when used in Boolean logic and English language. Analysis of usage logs for the New Zealand Digital Library (NZDL) show that few Boolean queries contain more than three terms, use of the intersection operator dominates and that query refinement is common. We suggest that graphical query languages, in particular Venn-like diagrams, can alleviate the problems that users experience when forming Boolean expressions with textual languages. A study of the utility of Venn diagrams for query specification indicates that with little or no training users can interpret and form Venn-like diagrams in a consistent manner which accurately correspond to Boolean expressions. We describe VQuery, a Venn-diagram based user interface to the New Zealand Digital Library (NZDL). In a study which compared VQuery with a standard textual Boolean interface, users took significantly longer to form queries and produced more erroneous queries when using VQuery. We discuss the implications of these results and suggest directions for future work. Received: 15 December 1997 / Revised: June 1999     

Search personalization through query and page topical analysis

Thousands of users issue keyword queries to the Web search engines to find information on a number of topics. Since the users may have diverse backgrounds and may have different expectations for a given query, some search engines try to personalize their results to better match the overall interests of an individual user. This task involves two great challenges. First the search engines need to be able to effectively identify the user interests and build a profile for every individual user. Second, once such a profile is available, the search engines need to rank the results in a way that matches the interests of a given user. In this article, we present our work towards a personalized Web search engine and we discuss how we addressed each of these challenges. Since users are typically not willing to provide information on their personal preferences, for the first challenge, we attempt to determine such preferences by examining the click history of each user. In particular, we leverage a topical ontology for estimating a user’s topic preferences based on her past searches, i.e. previously issued queries and pages visited for those queries. We then explore the semantic similarity between the user’s current query and the query-matching pages, in order to identify the user’s current topic preference. For the second challenge, we have developed a ranking function that uses the learned past and current topic preferences in order to rank the search results to better match the preferences of a given user. Our experimental evaluation on the Google query-stream of human subjects over a period of 1 month shows that user preferences can be learned accurately through the use of our topical ontology and that our ranking function which takes into account the learned user preferences yields significant improvements in the quality of the search results.     

Approximate query processing using wavelets

Approximate query processing has emerged as a cost-effective approach for dealing with the huge data volumes and stringent response-time requirements of today's decision support systems (DSS). Most work in this area, however, has so far been limited in its query processing scope, typically focusing on specific forms of aggregate queries. Furthermore, conventional approaches based on sampling or histograms appear to be inherently limited when it comes to approximating the results of complex queries over high-dimensional DSS data sets. In this paper, we propose the use of multi-dimensional wavelets as an effective tool for general-purpose approximate query processing in modern, high-dimensional applications. Our approach is based on building wavelet-coefficient synopses of the data and using these synopses to provide approximate answers to queries. We develop novel query processing algorithms that operate directly on the wavelet-coefficient synopses of relational tables, allowing us to process arbitrarily complex queries entirely in the wavelet-coefficient domain. This guarantees extremely fast response times since our approximate query execution engine can do the bulk of its processing over compact sets of wavelet coefficients, essentially postponing the expansion into relational tuples until the end-result of the query. We also propose a novel wavelet decomposition algorithm that can build these synopses in an I/O-efficient manner. Finally, we conduct an extensive experimental study with synthetic as well as real-life data sets to determine the effectiveness of our wavelet-based approach compared to sampling and histograms. Our results demonstrate that our techniques: (1) provide approximate answers of better quality than either sampling or histograms; (2) offer query execution-time speedups of more than two orders of magnitude; and (3) guarantee extremely fast synopsis construction times that scale linearly with the size of the data. Received: 7 August 2000 / Accepted: 1 April 2001 Published online: 7 June 2001     

Similarity-based ranking and query processing in multimedia databases

Since media-based evaluation yields similarity values, results to a multimedia database query, Q(Y₁,…,Y_n), is defined as an ordered list S_Q of n-tuples of the form X₁,…,X_n. The query Q itself is composed of a set of fuzzy and crisp predicates, constants, variables, and conjunction, disjunction, and negation operators. Since many multimedia applications require partial matches, S_Q includes results which do not satisfy all predicates. Due to the ranking and partial match requirements, traditional query processing techniques do not apply to multimedia databases. In this paper, we first focus on the problem of “given a multimedia query which consists of multiple fuzzy and crisp predicates, providing the user with a meaningful final ranking”. More specifically, we study the problem of merging similarity values in queries with multiple fuzzy predicates. We describe the essential multimedia retrieval semantics, compare these with the known approaches, and propose a semantics which captures the requirements of multimedia retrieval problem. We then build on these results in answering the related problem of “given a multimedia query which consists of multiple fuzzy and crisp predicates, finding an efficient way to process the query.” We develop an algorithm to efficiently process queries with unordered fuzzy predicates (sub-queries). Although this algorithm can work with different fuzzy semantics, it benefits from the statistical properties of the semantics proposed in this paper. We also present experimental results for evaluating the proposed algorithm in terms of quality of results and search space reduction.     

A holistic and principled approach for the empty-answer problem

We propose a principled optimization-based interactive query relaxation framework for queries that return no answers. Given an initial query that returns an empty-answer set, our framework dynamically computes and suggests alternative queries with fewer conditions than those the user has initially requested, in order to help the user arrive at a query with a non-empty-answer, or at a query for which no matter how many additional conditions are ignored, the answer will still be empty. Our proposed approach for suggesting query relaxations is driven by a novel probabilistic framework based on optimizing a wide variety of application-dependent objective functions. We describe optimal and approximate solutions of different optimization problems using the framework. Moreover, we discuss two important extensions to the base framework: the specification of a minimum size on the number of results returned by a relaxed query and the possibility of proposing multiple conditions at the same time. We analyze the proposed solutions, experimentally verify their efficiency and effectiveness, and illustrate their advantages over the existing approaches.     

The partial sequenced route query with traveling rules in road networks

In modern geographic information systems, route search represents an important class of queries. In route search related applications, users may want to define a number of traveling rules (traveling preferences) when they plan their trips. However, these traveling rules are not considered in most existing techniques. In this paper, we propose a novel spatial query type, the multi-rule partial sequenced route (MRPSR) query, which enables efficient trip planning with user defined traveling rules. The MRPSR query provides a unified framework that subsumes the well-known trip planning query (TPQ) and the optimal sequenced route (OSR) query. The difficulty in answering MRPSR queries lies in how to integrate multiple choices of points-of-interest (POI) with traveling rules when searching for satisfying routes. We prove that MRPSR query is NP-hard and then provide three algorithms by mapping traveling rules to an activity on vertex network. Afterwards, we extend all the proposed algorithms to road networks. By utilizing both real and synthetic POI datasets, we investigate the performance of our algorithms. The results of extensive simulations show that our algorithms are able to answer MRPSR queries effectively and efficiently with underlying road networks. Compared to the Light Optimal Route Discoverer (LORD) based brute-force solution, the response time of our algorithms is significantly reduced while the distances of the computed routes are only slightly longer than the shortest route.     

No-but-semantic-match: computing semantically matched xml keyword search results

Users are rarely familiar with the content of a data source they are querying, and therefore cannot avoid using keywords that do not exist in the data source. Traditional systems may respond with an empty result, causing dissatisfaction, while the data source in effect holds semantically related content. In this paper we study this no-but-semantic-match problem on XML keyword search and propose a solution which enables us to present the top-k semantically related results to the user. Our solution involves two steps: (a) extracting semantically related candidate queries from the original query and (b) processing candidate queries and retrieving the top-k semantically related results. Candidate queries are generated by replacement of non-mapped keywords with candidate keywords obtained from an ontological knowledge base. Candidate results are scored using their cohesiveness and their similarity to the original query. Since the number of queries to process can be large, with each result having to be analyzed, we propose pruning techniques to retrieve the top-k results efficiently. We develop two query processing algorithms based on our pruning techniques. Further, we exploit a property of the candidate queries to propose a technique for processing multiple queries in batch, which improves the performance substantially. Extensive experiments on two real datasets verify the effectiveness and efficiency of the proposed approaches.     

Spatial skyline queries: exact and approximation algorithms

As more data-intensive applications emerge, advanced retrieval semantics, such as ranking and skylines, have attracted the attention of researchers. Geographic information systems are a good example of an application using a massive amount of spatial data. Our goal is to efficiently support exact and approximate skyline queries over massive spatial datasets. A spatial skyline query, consisting of multiple query points, retrieves data points that are not father than any other data points, from all query points. To achieve this goal, we present a simple and efficient algorithm that computes the correct results, also propose a fast approximation algorithm that returns a desirable subset of the skyline results. In addition, we propose a continuous query algorithm to trace changes of skyline points while a query point moves. To validate the effectiveness and efficiency of our algorithm, we provide an extensive empirical comparison between our algorithms and the best known spatial skyline algorithms from several perspectives.     

Evaluating the Effectiveness of Personalized Web Search

Although personalized search has been under way for many years and many personalization algorithms have been investigated, it is still unclear whether personalization is consistently effective on different queries for different users and under different search contexts. In this paper, we study this problem and provide some findings. We present a large-scale evaluation framework for personalized search based on query logs and then evaluate five personalized search algorithms (including two click-based ones and three topical-interest-based ones) using 12-day query logs of Windows Live Search. By analyzing the results, we reveal that personalized Web search does not work equally well under various situations. It represents a significant improvement over generic Web search for some queries, while it has little effect and even harms query performance under some situations. We propose click entropy as a simple measurement on whether a query should be personalized. We further propose several features to automatically predict when a query will benefit from a specific personalization algorithm. Experimental results show that using a personalization algorithm for queries selected by our prediction model is better than using it simply for all queries.     

The effects of multiple query evidences on social image retrieval

System performance assessment and comparison are fundamental for large-scale image search engine development. This article documents a set of comprehensive empirical studies to explore the effects of multiple query evidences on large-scale social image search. The search performance based on the social tags, different kinds of visual features and their combinations are systematically studied and analyzed. To quantify the visual query complexity, a novel quantitative metric is proposed and applied to assess the influences of different visual queries based on their complexity levels. Besides, we also study the effects of automatic text query expansion with social tags using a pseudo relevance feedback method on the retrieval performance. Our analysis of experimental results shows a few key research findings: (1) social tag-based retrieval methods can achieve much better results than content-based retrieval methods; (2) a combination of textual and visual features can significantly and consistently improve the search performance; (3) the complexity of image queries has a strong correlation with retrieval results’ quality—more complex queries lead to poorer search effectiveness; and (4) query expansion based on social tags frequently causes search topic drift and consequently leads to performance degradation.     

Personalized query suggestion diversification in information retrieval

Query suggestions help users refine their queries after they input an initial query.Previous work on query suggestion has mainly concentrated on approaches that are similarity-based or context-based,developing models that either focus on adapting to a specific user(personalization)or on diversifying query aspects in order to maximize the probability of the user being satisfied(diversification).We consider the task of generating query suggestions that are both personalized and diversified.We propose a personalized query suggestion diversification(PQSD)model,where a user's long-term search behavior is injected into a basic greedy query suggestion diversification model that considers a user's search context in their current session.Query aspects are identified through clicked documents based on the open directory project(ODP)with a latent dirichlet allocation(LDA)topic model.We quantify the improvement of our proposed PQSD model against a state-of-the-art baseline using the public america online(AOL)query log and show that it beats the baseline in terms of metrics used in query suggestion ranking and diversification.The experimental results show that PQSD achieves its best performance when only queries with clicked documents are taken as search context rather than all queries,especially when more query suggestions are returned in the list.     

Exploiting community feedback for information retrieval in DHT networks

In this paper, we propose CYBER, a CommunitY Based sEaRch engine, for information retrieval utilizing community feedback information in a DHT network. In CYBER, each user is associated with a set of user profiles that capture his/her interests. Likewise, a document is associated with a set of profiles—one for each indexed term. A document profile is updated by users who query on the term and consider the document as a relevant answer. Thus, the profile acts as a consolidation of users feedback from the same community, and reflects their interests. In this way, as one user finds a document to be relevant, another user in the same community issuing a similar query will benefit from the feedback provided by the earlier user. Hence, the search quality in terms of both precision and recall is improved. Moreover, we further improve the effectiveness of CYBER by introducing an index tuning technique. By choosing the indexing terms more carefully, community-based relevance feedback is utilized in both building/refining indices and re-evaluating queries. We first propose a naive scheme, CYBER+, which involves an index tuning technique based on past queries only, and then re-evaluates queries in a separate step. We then propose a more complex scheme, CYBER+ +, which refines its index based on both past queries and relevance feedback. As the index is built with more selective and accurate terms, the search performance is further improved. We conduct a comprehensive experimental study and the results show the effectiveness of our schemes.     

Mapping queries to the Linking Open Data cloud: A case study using DBpedia

We introduce the task of mapping search engine queries to DBpedia, a major linking hub in the Linking Open Data cloud. We propose and compare various methods for addressing this task, using a mixture of information retrieval and machine learning techniques. Specifically, we present a supervised machine learning-based method to determine which concepts are intended by a user issuing a query. The concepts are obtained from an ontology and may be used to provide contextual information, related concepts, or navigational suggestions to the user submitting the query. Our approach first ranks candidate concepts using a language modeling for information retrieval framework. We then extract query, concept, and search-history feature vectors for these concepts. Using manual annotations we inform a machine learning algorithm that learns how to select concepts from the candidates given an input query. Simply performing a lexical match between the queries and concepts is found to perform poorly and so does using retrieval alone, i.e., omitting the concept selection stage. Our proposed method significantly improves upon these baselines and we find that support vector machines are able to achieve the best performance out of the machine learning algorithms evaluated.     

Querying Incomplete Information in Semistructured Data

Semistructured data occur in situations where information lacks a homogeneous structure and is incomplete. Yet, up to now the incompleteness of information has not been reflected by special features of query languages. Our goal is to investigate the principles of queries that allow for incomplete answers. We do not present, however, a concrete query language. Queries over classical structured data models contain a number of variables and constraints on these variables. An answer is a binding of the variables by elements of the database such that the constraints are satisfied. In the present paper, we loosen this concept in so far as we allow also answers that are partial; that is, not all variables in the query are bound by such an answer. Partial answers make it necessary to refine the model of query evaluation. The first modification relates to the satisfaction of constraints: in some circumstances we consider constraints involving unbound variables as satisfied. Second, in order to prevent a proliferation of answers, we only accept answers that are maximal in the sense that there are no assignments that bind more variables and satisfy the constraints of the query. Our model of query evaluation consists of two phases, a search phase and a filter phase. Semistructured databases are essentially labeled directed graphs. In the search phase, we use a query graph containing variables to match a maximal portion of the database graph. We investigate three different semantics for query graphs, which give rise to three variants of matching. For each variant, we provide algorithms and complexity results. In the filter phase, the maximal matchings resulting from the search phase are subjected to constraints, which may be weak or strong. Strong constraints require all their variables to be bound, while weak constraints do not. We describe a polynomial algorithm for evaluating a special type of queries with filter constraints, and assess the complexity of evaluating other queries for several kinds of constraints. In the final part, we investigate the containment problem for queries consisting only of search constraints under the different semantics.