Query Reformulation

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.

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.

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.

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.

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

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

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.