Query Reformulation

Dans cet article, nous proposons d'exploiter des liens sémantiques entre concepts pour améliorer la recherche d'information. Un thesaurus électronique de langue générale est utilisé pour la reformulation des requêtes utilisateurs en procédant par un processus d'"expansion prudente" en amont d'un moteur de recherche. Ce processus, transparent à l'utilisateur, exploite d'abord la notion de concepts multitermes pour désambiguïser les mots de la requête. Il s'appuie ensuite sur les relations sémantiques entre concepts pour élargir la requête. L'ensemble conduit à une amélioration significative de la pertinence des réponses retournées par le moteur. Cette technique a été évaluée en utilisant le moteur Mercure développé à l'IRIT, WordNet comme base de données lexicales et Clef2001 comme collection de test.

Understanding query reformulation patterns is a key task towards next generation web search engines. If we can do that, then we can build systems able to understand and possibly predict user intent, providing the needed assistance at the right time, and thus helping users locate information more effectively and improving their web-search experience.

With the rapid growth of information on the Web, the study of information searching has let to an increased interest. Information behaviour (IB) researchers and information systems (IS) developers are continuously exploring user - Web search interactions to understand and to help users to provide assistance with their information searching. In attempting to develop models of IB, several studies have identified various factors that govern user's information searching and information retrieval (IR), such as age, gender, prior knowledge and task complexity. However, how users' contextual factors, such as cognitive styles, affect Web search interactions has not been clearly explained by the current models of Web Searching and IR.

This study explores the influence of users' cognitive styles on their Web search behaviour. The main goal of the study is to enhance Web search models with a better understanding of how these cognitive styles affect Web searching. Modelling Web search behaviour with a greater understanding of user's cognitive styles can help information science researchers and IS designers to bridge the semantic gap between the user and the IS. To achieve the aims of the study, a user study with 50 participants was conducted. The study adopted a mixed method approach incorporating several data collection strategies to gather a range of qualitative and quantitative data. The study utilised pre-search and post-search questionnaires to collect the participants' demographic information and their level of satisfaction about the search interactions. Riding's (1991) Cognitive Style Analysis (CSA) test was used to assess the participants' cognitive styles. Participants completed three predesigned search tasks and the whole user - web search interactions, including thinkaloud, were captured using a monitoring program. Data analysis involved several qualitative and quantitative techniques: the quantitative data gave raise to detailed findings about users' Web searching and cognitive styles, the qualitative data enriched the findings with illustrative examples.

The study results provide valuable insights into Web searching behaviour among different cognitive style users. The findings of the study extend our understanding of Web search behaviour and how users search information on the Web. Three key study findings emerged: • Users' Web search behaviour was demonstrated through information searching strategies, Web navigation styles, query reformulation behaviour and information processing approaches while performing Web searches. The manner in which these Web search patterns were demonstrated varied among the users with different cognitive style groups.

• Users' cognitive styles influenced their information searching strategies, query reformulation behaviour, Web navigational styles and information processing approaches. Users with particular cognitive styles followed certain Web search patterns.

• Fundamental relationships were evident between users' cognitive styles and their Web search behaviours; and these relationships can be illustrated through modelling Web search behaviour. Two models that depict the associations between Web search interactions, user characteristics and users' cognitive styles were developed. These models provide a greater understanding of Web search behaviour from the user perspective, particularly how users' cognitive styles influence their Web search behaviour.

The significance of this research is twofold: it will provide insights for information science researchers, information system designers, academics, educators, trainers and librarians who want to better understand how users with different cognitive styles perform information searching on the Web; at the same time, it will provide assistance and support to the users. The major outcomes of this study are 1) a comprehensive analysis of how users search the Web; 2) extensive discussion on the implications of the models developed in this study for future work; and 3) a theoretical framework to bridge high-level search models and cognitive models.

The Web of Data is an open environment consisting of a great number of large inter-linked RDF datasets from various do-mains. In this environment, organizations and companies adopt the Linked Data practices utilizing Semantic Web (SW) tech-nologies, in order to publish their data and offer SPARQL endpoints (i.e., SPARQL-based search services). On the other hand, the dominant standard for information exchange in the Web today is XML. Additionally, many international standards (e.g., Dublin Core, MPEG-7, METS, TEI, IEEE LOM) in several domains (e.g., Digital Libraries, GIS, Multimedia, e-Learning) have been expressed in XML Schema. The aforementioned have led to an increasing emphasis on XML data, accessed using the XQuery query language. The SW and XML worlds and their developed infrastructures are based on different data models, semantics and query languages. Thus, it is crucial to develop interoperability mechanisms that allow the Web of Data users to access XML datasets, using SPARQL, from their own working environments. It is unrealistic to expect that all the existing legacy data (e.g., Relational, XML, etc.) will be transformed into SW data. Therefore, publishing legacy data as Linked Data and providing SPARQL endpoints over them has become a major research challenge. In this direction, we introduce the SPARQL2XQuery Framework which creates an interoperable environment, where SPARQL queries are automatically translated to XQuery queries, in order to access XML data across the Web. The SPARQL2XQuery Framework provides a mapping model for the expression of OWL–RDF/S to XML Schema mappings as well as a method for SPARQL to XQuery translation. To this end, our Framework supports both manual and automatic mapping specification between ontologies and XML Schemas. In the automatic mapping specification scenario, the SPARQL2XQuery exploits the XS2OWL component which transforms XML Sche-mas into OWL ontologies. Finally, extensive experiments have been conducted in order to evaluate the schema transfor-mation, mapping generation, query translation and query evaluation efficiency, using both real and synthetic datasets.

SPARQL is today the standard access language for Semantic Web data. In the recent years XML databases have also acquired industrial importance due to the widespread applicability of XML in the Web. In this paper we present a framework that bridges the heterogeneity gap and creates an interoperable environment where SPARQL queries are used to access XML databases. Our approach assumes that fairly generic mappings between ontology constructs and XML Schema constructs have been automatically derived or manually specified. The mappings are used to automatically translate SPARQL queries to semantically equivalent XQuery queries which are used to access the XML databases. We present the algorithms and the implementation of SPARQL2XQuery framework, which is used for answering SPARQL queries over XML databases.

"In the context of the emergent Web of Data, a large number of organizations, institutes and companies (e.g., DBpedia, Geonames, PubMed ACM, IEEE, NASA, BBC) adopt the Linked Data practices and publish their data utilizing Semantic Web (SW) technologies. On the other hand, the dominant standard for information exchange in the Web today is XML. Many international standards (e.g., Dublin Core, MPEG-7, METS, TEI, IEEE LOM) have been expressed in XML Schema resulting to a large number of XML datasets. The SW and XML worlds and their developed infrastructures are based on different data models, semantics and query languages. Thus, it is crucial to provide interoperability and integration mechanisms to bridge the gap between the SW and XML worlds.
In this chapter, we give an overview and a comparison of the technologies and the standards adopted by the XML and SW worlds. In addition, we outline the latest efforts from the W3C groups, including the latest working drafts and recommendations (e.g., OWL 2, SPARQL 1.1, XML Schema 1.1). Moreover, we present a survey of the research approaches which aim to provide interoperability and integration between the XML and SW worlds. Finally, we present the SPARQL2XQuery and XS2OWL Frameworks, which bridge the gap and create an interoperable environment between the two worlds. These Frameworks provide mechanisms for: (a) Query translation (SPARQL to XQuery translation); (b) Mapping specification and generation (Ontology to XML Schema mapping); and (c) Schema transformation (XML Schema to OWL transformation)."

Purpose: To investigate and identify the patterns of interaction between searchers and search engine during Web searching. Design: We examined 2,465,145 interactions from 534,507 users of Dogpile.com submitted on May 6, 2005. We compared query reformulation patterns. We investigated the type of query modifications and query modification transitions within sessions. Findings: We identified three strong query reformulation transition patterns: between (1) specialization and generalization, between (2) video and audio, and between (3) content change and system assistance. In addition, our findings show that Web and images content were the most popular media collections. Value: This research sheds light on the more complex aspects of Web searching involving query modifications.

Intentional services have been proposed to bridge the gap between low level, technical software-service descriptions and high level, strategic expressions of business needs for services. This proposal leverages on the SOA to an intentional level, and introduces the iSOA architecture. Derived from business specifications by service providers, intentional services are referenced in an extended registry using ontologies and semantic annotations. We present in this paper a query reformulation process to help in matching user's requirements formulated in business terms as goals, with the intentions of services published in the extended registry. Reformulating user queries and enriching them with more concepts increases the possibility of matching relevant intentional services that could satisfy user's business needs.

Web searcher is an important area of research for designing more helpful searching systems and targeting content to particular users. Methods explored by other researchers include both qualitative (i.e., the use of human judges to manually analyze query patterns on usually small samples) and nondeterministic algorithms, typically using large amounts of training data to predict query modification during sessions. In this article, we explore three alternative methods for detection of session boundaries. All three methods are computationally straightforward and therefore easily implemented for detection of session changes. We examine 2,465,145 interactions from 534,507 users of Dogpile.com on May 6, 2005. We compare session analysis using (a) Internet Protocol address and cookie; (b) Internet Protocol address, cookie, and a temporal limit on intrasession interactions; and (c) Internet Protocol address, cookie, and query reformulation patterns. Overall, our analysis shows that defining sessions by query reformulation along with Internet Protocol address and cookie provides the best measure, resulting in an 82% increase in the count of sessions. Regardless of the method used, the mean session length was fewer than three queries, and the mean session duration was less than 30 min. Searchers most often modified their query by changing query terms (nearly 23% of all query modifications) rather than adding or deleting terms. Implications are that for measuring searching traffic, unique sessions may be a better indicator than the common metric of unique visitors. This research also sheds light on the more complex aspects of Web searching involving query modifications and may lead to advances in searching tools. examined navigation data using a session-level analysis to cluster search sessions.

Search engine logs store detailed information on Web users interactions. Thus, as more and more people use search engines on a daily basis, important trails of users common knowledge are being recorded in those files. Previous research has shown that it is possible to extract concept taxonomies from full text documents, while other scholars have proposed methods to obtain similar queries from query logs. We propose a mixture of both lines of research, that is, mining query logs not to find related queries nor query hierarchies but actual term taxonomies. In this first approach we have researched the feasibility of finding hyponymy relations between terms or noun-phrases by exploiting specialization search patterns in topical sessions, obtaining encouraging preliminary results.

In the context of the emergent Web of Data, a large number of organizations, institutes and companies (e.g., DBpedia, Geonames, PubMed ACM, IEEE, NASA, BBC) adopt the Linked Data practices and publish their data utilizing Semantic Web (SW) technologies. On the other hand, the dominant standard for information exchange in the Web today is XML. Many international standards (e.g., Dublin Core, MPEG-7, METS, TEI, IEEE LOM) have been expressed in XML Schema resulting to a large number of XML datasets. The SW and XML worlds and their developed infrastructures are based on different data models, semantics and query languages. Thus, it is crucial to provide interoperability and integration mechanisms to bridge the gap between the SW and XML worlds.

The purpose of this analysis was to evaluate an existing set of search tasks in terms of their effectiveness as part of a " shared infrastructure " for conducting interactive IR research. Twenty search tasks that varied in their cognitive complexity and domain were assigned to 47 study participants; the 3,101 moves used to complete those tasks were then analyzed in terms of frequency of each type of move and the sequential patterns they formed. The cognitive complexity of the tasks influenced the number of moves used to complete the tasks, with the most complex (i.e., Create) tasks requiring more moves than tasks at other levels of complexity. Across the four domains, the Commerce tasks elicited more search moves per search. When sequences of moves were analyzed, seven patterns were identified; some of these patterns were associated with particular task characteristics. The findings suggest that search tasks can be designed to elicit particular types of search behaviors and, thus, allow researchers to focus attention on particular aspects of IR interactions.

Designing good user interfaces to information retrieval systems is a complex activity. The design space is large and evaluation methodologies that go beyond the classical precision and recall figures are not well established. In this paper we present an evaluation of an intelligent interface that covers also the user-system interaction and measures user's satisfaction. More specifically, we describe an experiment that evaluates: (i) the added value of the semiautomatic query reformulation implemented in a prototype system; (ii) the importance of technical, terminological, and strategic supports and (iii) the best way to provide them. The interpretation of results leads to guidelines for the design of user interfaces to information retrieval systems and to some observations on the evaluation issue.

Web-search queries are known to be short, but little else is known about their structure. In this paper we investigate the applicability of part-of-speech tagging to typical Englishlanguage web search-engine queries and the potential value of these tags for improving search results. We begin by identifying a set of part-of-speech tags suitable for search queries and quantifying their occurrence. We find that proper-nouns constitute 40% of query terms, and proper nouns and nouns together constitute over 70% of query terms. We also show that the majority of queries are nounphrases, not unstructured collections of terms. We then use a set of queries manually labeled with these tags to train a Brill tagger and evaluate its performance. In addition, we investigate classification of search queries into grammatical classes based on the syntax of part-of-speech tag sequences. We also conduct preliminary investigative experiments into the practical applicability of leveraging query-trained part-of-speech taggers for information-retrieval tasks. In particular, we show that part-of-speech information can be a significant feature in machine-learned searchresult relevance. These experiments also include the potential use of the tagger in selecting words for omission or substitution in query reformulation, actions which can improve recall. We conclude that training a partof-speech tagger on labeled corpora of queries significantly outperforms taggers based on traditional corpora, and leveraging the unique linguistic structure of web-search queries can improve search experience.

The Web of Data encourages organizations and companies to publish their data according to the Linked Data practices and offer SPARQL endpoints. On the other hand, the dominant standard for information exchange is XML. The SPARQL2XQuery Framework focuses on the automatic translation of SPARQL queries in XQuery expressions in order to access XML data across the Web. In this paper, we outline our ongoing work on supporting update queries in the RDF–XML integration scenario.

Abstract. We propose a framework that supports a federated environment based on a Mediator Architecture in the Semantic Web. The Mediator supports mappings between the OWL Ontology of the Mediator and the other ontologies in the federated sites. SPARQL queries submitted to the Mediator are decomposed and reformulated to SPARQL queries to the federated sites. The evaluated results return to the Mediator. In this paper we describe the mappings definition and encoding. We also discuss briefly the reformulation approach that is used by the Mediator system that we are currently implementing.

This dissertation is dedicated to my family: my wife, Lhamo, my son, Tenzi Jurmin Lhendup, and my daughter, Kinley Om, for their love and support. (x) (xi) Lastly, a special thank you to my wife, Lhamo and kids, Tenzi Lhendup and Kinley Om, for supporting and sticking with me throughout my PhD journey.

Recent findings suggest that Information Retrieval (IR)-based bug localization techniques do not perform well if the bug report lacks rich structured information (e.g., relevant program entity names). Conversely, excessive structured information (e.g., stack traces) in the bug report might not always help the automated localization either. In this paper, we propose a novel technique - BLIZZARD - that automatically localizes buggy entities from project source using appropriate query reformulation and effective information retrieval. In particular, our technique determines whether there are excessive program entities or not in a bug report (query), and then applies appropriate reformulations to the query for bug localization. Experiments using 5,139 bug reports show that our technique can localize the buggy source documents with 7% - 56% higher Hit@10, 6% - 62% higher MAP@10 and 6% - 62% higher MRR@10 than the baseline technique. Comparison with the state-of-the-art techniques and their variants report that our technique can improve 19% in MAP@10 and 20% in MRR@10 over the state-of-the-art, and can improve 59% of the noisy queries and 39% of the poor queries.

Problem Statement: The huge number of information on the web as well as the growth of new inexperienced users creates new challenges for information retrieval. It has become increasingly difficult for these users to find relevant documents that satisfy their individual needs. Certainly the current search engines (such as Google, Bing and Yahoo) offer an efficient way to browse the web content. However, the result quality is highly based on uses queries which need to be more precise to find relevant documents. This task still complicated for the majority of inept users who cannot express their needs with significant words in the query. For that reason, we believe that a reformulation of the initial user's query can be a good alternative to improve the information selectivity. This study proposes a novel approach and presents a prototype system called PRESY (Profile-based REformulation SYstem) for information retrieval on the web. Approach: It uses an incremental approach to categorize users by constructing a contextual base. The latter is composed of two types of context (static and dynamic) obtained using the users' profiles. The architecture proposed was implemented using .Net environment to perform queries reformulating tests. Results: The experiments gives at the end of this article show that the precision of the returned content is effectively improved. The tests were performed with the most popular searching engine (i.e. Google, Bind and Yahoo) selected in particular for their high selectivity. Among the given results, we found that query reformulation improve the first three results by 10.7% and 11.7% of the next seven returned elements. So as we can see the reformulation of users' initial queries improves the pertinence of returned content.

People have different mental strengths and weakness, which can be measured according to cognitive ability. Learning about strengths and preferences in terms of search behavior, and looking for patterns between behaviors and cognitive abilities, creates the opportunity to make search tools and systems more effectively meet user needs and preferences. While we know that different cognitive abilities exist and that people form and reform search queries in a variety of ways, we do not know how these two elements interact, or if the interaction is predictable or significant.

This paper performs secondary analysis of data collected during a study of cognitive ability, adding in the element of query reformulation moves. It assesses the effect of these cognitive abilities on study participants' search formulation behaviors. Analysis showed that the most common search move was adding a concept to a query, followed by deleting concepts and manipulating search terms. Of the cognitive abilities, the only statistically significant differences between high and low groups were found in the visualization ability. Those in the high skill group made significantly more moves, and significantly more term manipulation moves, than their low skill counterparts.

a b s t r a c t Improving the retrieval accuracy of MEDLINE documents is still a challenging issue due to low retrieval precision. Focusing on a query expansion technique based on pseudo-relevance feedback (PRF), this paper addresses the problem by systematically examining the effects of expansion term selection and adjustment of the term weights of the expanded query using a set of MEDLINE test documents called OHSUMED. Implementing a baseline information retrieval system based on the Okapi BM25 retrieval model, we compared six well-known term ranking algorithms for useful expansion term selection and then compared traditional term reweighting algorithms with our new variant of the standard Rocchio's feedback formula, which adopts a group-based weighting scheme. Our experimental results on the OHSUMED test collection showed a maximum improvement of 20.2% and 20.4% for mean average precision and recall measures over unexpanded queries when terms were expanded using a co-occurrence analysis-based term ranking algorithm in conjunction with our term reweighting algorithm (pvalue < 0.05). Our study shows the behaviors of different query reformulation techniques that can be utilized for more effective MEDLINE document retrieval.

Understanding query reformulation patterns is a key task towards next generation web search engines. If we can do that, then we can build systems able to understand and possibly predict user intent, providing the needed assistance at the right time, and thus helping users locate information more effectively and improving their web-search experience.

We propose a framework that supports a federated environment based on a Mediator Architecture in the Semantic Web. The Mediator supports mappings between the OWL Ontology of the Mediator and the other ontologies in the federated sites. SPARQL queries submitted to the Mediator are decomposed and reformulated to SPARQL queries to the federated sites. The evaluated results return to the Mediator. In this paper we describe the mappings definition and encoding. We also discuss briefly the reformulation approach that is used by the Mediator system that we are currently implementing

A web search engine log is a very rich source of semantic knowledge. In this paper we focus on the extraction of hyponymy relations from individual user sessions by examining, search behavior. The results obtained allow us to identify specific reformulation models as ones that more frequently represent hyponymy relations. The extracted relations reflect the knowledge that the user is employing while searching the web. Simultaneously, this study leads to a better understanding of web user search behavior.

Mining user web search activity potentially has a broad range of applications including web result pre-fetching, automatic search query reformulation, click spam detection, estimation of document relevance and prediction of user satisfaction. This analysis is difficult because the data recorded by search engines while users interact with them, although abundant, is very noisy. In this work, we explore the utility of mining search behavior of users, represented by observed variables including the time the user spends on the page, and whether the user reformulated his or her query. As a case study, we examine the contribution this data makes to predicting the relevance of a document in the absence of document content models. To this end, we first propose a method for grouping the interactions of a particular user according to the different tasks he or she undertakes. With each task corresponding to a distinct information need, we then propose a Bayesian Network to holistically model these interactions. The aim is to identify distinct patterns of search behaviors. Finally, we join these patterns to a list of custom features and we use gradient boosted decision trees to predict the relevance of a set of query document pairs for which we have relevance assessments. The experimental results confirm the potential of our model, with significant improvements in precision for predicting the relevance of documents based on a model of the user's search and click behavior, over a baseline model using only click and query features, with no Bayesian Network input.

Problem Statement: The huge number of information on the web as well as the growth of new inexperienced users creates new challenges for information retrieval. It has become increasingly difficult for these users to find relevant documents that satisfy their individual needs. Certainly the current search engines (such as Google, Bing and Yahoo) offer an efficient way to browse the

This report firstly summarises the work thus far on the XMAP data integration algorithm and on middleware with regard to Grid query processing services, secondly, proposes an architecture for data integration-enabled query processing on the Grid, and finally, presents a roadmap for its implementation with a view to producing an extended set of e-Services. These services will allow users to submit queries over a single database and receive the results from multiple databases that are semantically correlated with the former one.

Data Grids rely on the coordinated sharing of and interaction across multiple autonomous database management systems. They provide transparent access to heterogeneous and autonomous data resources stored on Grid nodes. Data sharing tools for Grids must include both distributed query processing and data integration functionality. This paper presents the implementation of a data sharing system that (i) is tailored to data Grids, (ii) supports well established and widely spread relational DBMSs, and (iii) adopts a hybrid architecture by relying on a peer model for query reformulation to retrieve semantically equivalent expressions, and on a wrapper-mediator integration model for accessing and querying distributed data sources. The system builds upon the infrastructure provided by the OGSA-DQP distributed query processor and the XMAP query reformulation algorithm. The paper discusses the implementation methodology, and presents empirical evaluation results.

tournées. ABSTRACT. This paper presents SHIRI-Querying, an approach for semantic search in semi- structured documents. We propose a solution to tackle incompleteness and imprecision of an- notations at querying time. This solution relies on two elementary reformulation types that use the notion of aggregation and the documents structure. We present the dynamic algorithm (DREQ) which combines these elementary transformations to

Understanding query reformulation patterns is a key step towards next generation web search engines: it can help improving users' web-search experience by predicting their intent, and thus helping them to locate information more effectively.

This paper proposes an approach for query reformulation based on the generation of appropriate query-biased concepts. Query-biased concepts are generated from retrieved documents using their content and structure. In this paper, we focus on three aspects of the concept generation; the selection of query-biased concepts from retrieved documents, the effect of the structure, and the number of retrieved documents used for generating the concepts.

We present and analyze an algorithm for equivalent rewriting of XQuery queries using XQuery views, which is complete for a large class of XQueries featuring nested FLWR blocks, XML construction and join equalities by value and identity. These features pose significant challenges which lead to fundamental extension of prior work on the problems of rewriting conjunctive and tree pattern queries. Our solution exploits the Nested XML Tableaux (NEXT) notation which enables a logical foundation for specifying XQuery semantics. We present a tool which inputs XQuery queries and views and outputs an XQuery rewriting, thus being usable on top of any of the existing XQuery processing engines. Our experimental evaluation shows that the tool scales well for large numbers of views and complex queries.

We state and solve the query reformulation problem for XML publishing in a general setting that allows mixed (XML and relational) storage for the proprietary data and exploits redundancies (materialized views, indexes and caches) to enhance performance. The correspondence between published and proprietary schemas is specified by views in both directions, and the same algorithm performs rewriting-with-views, composition-with-views, or the combined effect of both, unifying the Global-As-View and Local-As-View approaches to data integration. We prove a completeness theorem which guarantees that under certain conditions, our algorithm will find a minimal reformulation if one exists. Moreover, we identify conditions when this algorithm achieves optimal complexity bounds. We solve the reformulation problem for constraints by exploiting a reduction to the problem of query reformulation.

This paper examines the reliability of implicit feedback generated from clickthrough data and query reformulations in WWW search. Analyzing the users' decision process using eyetracking and comparing implicit feedback against manual relevance judgments, we conclude that clicks are informative but biased. While this makes the interpretation of clicks as absolute relevance judgments difficult, we show that relative preferences derived from clicks are reasonably accurate on average. We find that such relative preferences are accurate not only between results from an individual query, but across multiple sets of results within chains of query reformulations. · 3 1994, ]. First, our study provides detailed insight into the users' decision-making process through the use of eyetracking. Second, we evaluate relative preference signals derived from user behavior. This is in contrast to previous studies that primarily evaluated absolute feedback.

Peers in a peer-to-peer data management system often have heterogeneous schemas and no mediated global schema. To translate queries across peers, we assume each peer provides correspondences between its schema and a small number of other peer schemas. We focus on query reformulation in the presence of heterogeneous XML schemas, including data-metadata conflicts. We develop an algorithm for inferring precise mapping rules from informal schema correspondences. We define the semantics of query answering in this setting and develop query translation algorithm. Our translation handles an expressive fragment of XQuery and works both along and against the direction of mapping rules. We describe the HePToX heterogeneous P2P XML data management system which incorporates our results. We report the results of extensive experiments on HePToX on both synthetic and real datasets. We demonstrate our system utility and scalability on different P2P distributions.

Learning to rank plays an important role in information retrieval. In most of the existing solutions for learning to rank, all the queries with their returned search results are learnt and ranked with a single model. In this paper, we demonstrate that it is highly beneficial to divide queries into multiple groups and conquer search ranking based on query difficulty. To this end, we propose a method which first characterizes a query using a variety of features extracted from user search behavior, such as the click entropy, the query reformulation probability. Next, a classification model is built on these extracted features to assign a score to represent how difficult a query is. Based on this score, our method automatically divides queries into groups, and trains a specific ranking model for each group to conquer search ranking. Experimental results on RankSVM and RankNet with a largescale evaluation dataset show that the proposed method can achieve significant improvement in the task of web search ranking.

We consider Cooperative Information Systems (CIS) that are multidatabase systems (MDBMS), with a common object-oriented model, based on the ODMG standard, together with local databases that may be relational, object-oriented, or dedicated data servers. The MDBMS interface (or mediator interface) that describes this CIS could be di erent from the union of the local interfaces, that describe each local database. In particular, the mediator interface may be de ned by semantic knowledge, that includes views over particular local databases, integrity constraints, and knowledge about data replication in local databases. We present a methodology for query reformulation which is based on the uniform representation of all semantic knowledge in the form of integrity assertions and mapping rules. A reformulation algorithm exploits this semantic knowledge, and performs semantic rewriting based on pattern-matching, to obtain a query on the union of the local interfaces. A decomposition algorithm then produces a composite query, and local sub-queries, one for each local interface. The reformulation is general enough to re-use the results of previously computed queries in the CIS. We have implemented this reformulation technique in our Flora compiler prototype which we used for validation and experimentation with O2 databases.

Examines the use of query reformulation, and particularly the use of relevance feedback by users of the Excite Web search engine. A total of 985 user search sessions from a data set of 18,113 user search sessions containing 51,473 queries were examined. Includes a qualitative and quantitative analysis of 191 user sessions including more than one query, to examine patterns of user query reformulation; and second, all 804 user sessions including relevance feedback were examined. Results show limited use of query reformulation and relevance feedback by Excite users ± only one in five users reformulated queries. Most relevance feedback sessions were successful. Identifies the most common pattern of searching and discusses implications for Web search system design.

Inter-business collaborative contexts prefigure a distributed scenario where companies organize and coordinate themselves to develop common and shared opportunities. Traditional business intelligence systems do not provide support to this end. Peer Data Management Systems (PDMSs) have been proposed as architectures to support sharing of operational data across networks of peers while guaranteeing peers' autonomy, based on semantic mappings that mediate between the heterogeneous schemata exposed by peers. In line with the PDMS infrastructure, in this paper we envision a peer-to-peer data warehousing architecture based on a network of heterogeneous peers, each exposing query answering functionalities aimed at sharing business information. To enhance the decision making process, an OLAP query expressed on a peer needs be properly reformulated on the other peers. In this direction, we present a language for the definition of mappings between the multidimensional schemata of peers, and we introduce a query reformulation framework that relies on the translation of these mappings towards relational schemata. Finally, we sketch the query reformulation algorithm by outlining the reformulation steps of typical OLAP queries.

Knowledge-intensive applications pose new challenges to metadata management, including distribution, access control, uniformity of access, and evolution in time. The authors identify general requirements for metadata management and describe a simple model and service that focuses on Resource Description Framework (RDF) metadata to address these requirements.

Nous présentons certains des nouveaux résultats du projet RAP (Recherche, Analyse et Propose) qui s'inscrit dans le cadre de la recherche et de l'extraction ciblée d'informations sur le Web. RAP propose à ses utilisateurs d'organiser leurs recherches suivant différents points de vue sémantiques. Ce travail a été testé en premier lieu avec des points de vue utilisant des relations causales. Depuis, nous avons intégré d'autres points de vue comme celui de la citation. Dans ce papier, nous rappelons le processus de recherche de pages Web de RAP qui s'appuie sur la reformulation des requêtes de l'utilisateur en fonction du point de vue choisi. Nous illustrons à l'aide d'exemples ce qui peut être obtenu avec un outil comme RAP en nous appuyant essentiellement sur le point de vue qui permet le repérage et l'extraction de la citation.

Inter-business collaborative contexts prefigure a distributed scenario where companies organize and coordinate themselves to develop common and shared opportunities. Traditional business intelligence systems do not provide support to this end. Peer Data Management Systems (PDMSs) have been proposed as architectures to support sharing of operational data across networks of peers while guaranteeing peers' autonomy, based on semantic mappings that mediate between the heterogeneous schemata exposed by peers. In line with the PDMS infrastructure, in this paper we envision a peer-to-peer data warehousing architecture based on a network of heterogeneous peers, each exposing query answering functionalities aimed at sharing business information. To enhance the decision making process, an OLAP query expressed on a peer needs be properly reformulated on the other peers. In this direction, we present a language for the definition of mappings between the multidimensional schemata of peers, and we introduce a query reformulation framework that relies on the translation of these mappings towards relational schemata. Finally, we sketch the query reformulation algorithm by outlining the reformulation steps of typical OLAP queries.

Relevance feedback (RFB) involves requesting some user judgments for an initial set of search results and then using these judgments to improve search results. Typical queries may have multiple possible interpretations or facets, , but top search results may be dominated by one interpretation or facet. Thus, if the user is only given the top results to inspect, none of them may be relevant. One way to solve this is to intentionally diversify the top few results to cover multiple interpretations. This paper proposes the use of density-based clustering for the purpose of results diversification in the context of RFB. Other traditional clustering algorithms are also used for a comparative study. Clustering is compared to a baseline that nominates the top results from an initial ranked list and compared to using the top results after re-ranking using Maximal Marginal Relevance. The results show that densitybased clustering achieves the best results with a statistically significant improvement of 12% over the baseline.

As Internet resources become accessible to more and more countries, there is a need to develop efficient methods for information retrieval across languages. In the present paper, we focus on query expansion techniques to improve the effectiveness of an information retrieval. A combination to a dictionary-based translation and statistical-based disambiguation is indispensable to overcome translation's ambiguity. We propose a model using multiple sources for query reformulation and expansion to select expansion terms and retrieve information needed by a user. Relevance feedback, thesaurus-based expansion, as well as a new feedback strategy, based on the extraction of domain keywords to expand user's query, are introduced and evaluated. We tested the effectiveness of the proposed combined method, by an application to a French-English Information Retrieval. Experiments using CLEF data collection proved a great effectiveness of the proposed combined query expansion techniques.

Query recommendations are an integral part of modern search engines. Their goal is to facilitate users' search tasks, as well as help them discover and explore concepts related to their information needs. In this paper, we present a formal treatment of the problem of query recommendation. In our framework we model the user-querying behavior by a probabilistic reformulation graph, or query-flow graph [Boldi et al. CIKM 2008], so that the sequence of queries submitted by a user can be seen as a path on this graph. Assigning score values to queries allows us to define suitable utility functions and to consider the expected utility achieved by performing a random walk on the query-flow graph. Furthermore, providing recommendations can be seen as adding shortcuts in the query-flow graph that "nudge" the reformulation paths of users, in such a way that users are more likely to follow paths with larger expected utility.

Long queries form a difficult, but increasingly important segment for web search engines. Query reduction, a technique for dropping unnecessary query terms from long queries, improves performance of ad-hoc retrieval on TREC collections. Also, it has great potential for improving long web queries (upto 25% improvement in NDCG@5). However, query reduction on the web is hampered by the lack of accurate query performance predictors and the constraints imposed by search engine architectures and ranking algorithms.