Search (113 results, page 2 of 6)

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Abstract

The paper discusses the issue of increasing users' access to library records by their publication in Google. Data from the records, converted into html format, have been indexed by Google. The process covered basic formal description fields of the records, description of the content, supported with a thesaurus, as well as an abstract, if present in the record. In addition to monitoring the end users' statistics, the pilot testing covered visibility of library records in Google search results.

Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

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Abstract

Introducing context to a user query is effective to improve the search effectiveness. In this article we propose a method employing the taxonomy-based search services such as Web directories to facilitate searches in any Web search interfaces that support Boolean queries. The proposed method enables one to convey current search context an taxonomy of a taxonomy-based search service to the searches conducted with the Web search interfaces. The basic idea is to learn the search context in the form of a Boolean condition that is commonly accepted by many Web search interfaces, and to use the condition to modify the user query before forwarding it to the Web search interfaces. To guarantee that the modified query can always be processed by the Web search interfaces and to make the method adaptive to different user requirements an search result effectiveness, we have developed new fast classification learning algorithms.

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Abstract

Although most of the queries submitted to search engines are composed of a few keywords and have a length that ranges from three to six words, more than 15% of the total volume of the queries are verbose, introduce ambiguity and cause topic drifts. We consider verbosity a different property of queries from length since a verbose query is not necessarily long, it might be succinct and a short query might be verbose. This paper proposes a methodology to automatically detect verbose queries and conditionally modify queries. The methodology proposed in this paper exploits state-of-the-art classification algorithms, combines concepts from a large linguistic database and uses a topic gisting algorithm we designed for verbose query modification purposes. Our experimental results have been obtained using the TREC Robust track collection, thirty topics classified by difficulty degree, four queries per topic classified by verbosity and length, and human assessment of query verbosity. Our results suggest that the methodology for query modification conditioned to query verbosity detection and topic gisting is significantly effective and that query modification should be refined when topic difficulty and query verbosity are considered since these two properties interact and query verbosity is not straightforwardly related to query length.

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Abstract

Most existing search engines focus on document retrieval. However, information needs are certainly not limited to finding relevant documents. Instead, a user may want to find relevant entities such as persons and organizations. In this paper, we study the problem of related entity finding. Our goal is to rank entities based on their relevance to a structured query, which specifies an input entity, the type of related entities and the relation between the input and related entities. We first discuss a general probabilistic framework, derive six possible retrieval models to rank the related entities, and then compare these models both analytically and empirically. To further improve performance, we study the problem of feedback in the context of related entity finding. Specifically, we propose a mixture model based feedback method that can utilize the pseudo feedback entities to estimate an enriched model for the relation between the input and related entities. Experimental results over two standard TREC collections show that the derived relation generation model combined with a relation feedback method performs better than other models.

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Abstract

Analyzing and modeling users' online search behaviors when conducting exploratory search tasks could be instrumental in discovering search behavior patterns that can then be leveraged to assist users in reaching their search task goals. We propose a framework for evaluating exploratory search based on implicit features and user search action sequences extracted from the transactional log data to model different aspects of exploratory search namely uncertainty, creativity, exploration, and knowledge discovery. We show the effectiveness of the proposed framework by demonstrating how it can be used to understand and evaluate user search performance and thereby make meaningful recommendations to improve the overall search performance of users. We used data collected from a user study consisting of 18 users conducting an exploratory search task for two sessions with two different topics in the experimental analysis. With this analysis we show that we can effectively model their behavior using implicit features to predict the user's future performance level with above 70% accuracy in most cases. Further, using simulations we demonstrate that our search process based recommendations improve the search performance of low performing users over time and validate these findings using both qualitative and quantitative approaches.

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Abstract

As the volume and breadth of online information is rapidly increasing, ad hoc search systems become less and less efficient to answer information needs of modern users. To support the growing complexity of search tasks, researchers in the field of information developed and explored a range of approaches that extend the traditional ad hoc retrieval paradigm. Among these approaches, personalized search systems and exploratory search systems attracted many followers. Personalized search explored the power of artificial intelligence techniques to provide tailored search results according to different user interests, contexts, and tasks. In contrast, exploratory search capitalized on the power of human intelligence by providing users with more powerful interfaces to support the search process. As these approaches are not contradictory, we believe that they can re-enforce each other. We argue that the effectiveness of personalized search systems may be increased by allowing users to interact with the system and learn/investigate the problem in order to reach the final goal. We also suggest that an interactive visualization approach could offer a good ground to combine the strong sides of personalized and exploratory search approaches. This paper proposes a specific way to integrate interactive visualization and personalized search and introduces an adaptive visualization based search system Adaptive VIBE that implements it. We tested the effectiveness of Adaptive VIBE and investigated its strengths and weaknesses by conducting a full-scale user study. The results show that Adaptive VIBE can improve the precision and the productivity of the personalized search system while helping users to discover more diverse sets of information.

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Abstract

While the search interfaces used by common academic digital libraries provide easy access to a wealth of peer-reviewed literature, their interfaces provide little support for exploratory browsing. When faced with a complex search task (such as one that requires knowledge discovery), exploratory browsing is an important first step in an exploratory search process. To more effectively support exploratory browsing, we have designed and implemented a novel academic digital library search interface (KLink Search) with two new features: visually linked keywords and an interactive workspace. To study the potential value of these features, we have conducted a controlled laboratory study with 32 participants, comparing KLink Search to a baseline digital library search interface modeled after that used by IEEE Xplore. Based on subjective opinions, objective performance, and behavioral data, we show the value of adding lightweight visual and interactive features to academic digital library search interfaces to support exploratory browsing.

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Abstract

Purpose - This paper seeks to report an investigation into the ways in which end-users perceive a thesaurus-enhanced search interface, in particular thesaurus and search interface usability. Design/methodology/approach - Thirty academic users, split between staff and postgraduate students, carrying out real search requests were observed during this study. Users were asked to comment on a range of thesaurus and interface characteristics including: ease of use, ease of learning, ease of browsing and navigation, problems and difficulties encountered while interacting with the system, and the effect of browsing on search term selection. Findings - The results suggest that interface usability is a factor affecting thesaurus browsing/navigation and other information-searching behaviours. Academic staff viewed the function of a thesaurus as being useful for narrowing down a search and providing alternative search terms, while postgraduates stressed the role of the thesaurus for broadening searches and providing new terms. Originality/value - The paper provides an insight into the ways in which end-users make use of and interact with a thesaurus-enhanced search interface. This area is new since previous research has particularly focused on how professional searchers and librarians make use of thesauri and thesaurus-enhanced search interfaces. The research reported here suggests that end-users with varying levels of domain knowledge are able to use thesauri that are integrated into search interfaces. It also provides design implications for search interface developers as well as information professionals who are involved in teaching online searching.

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Abstract

In the last few years, Twitter has become a powerful tool for publishing and discussing information. Yet, content exploration in Twitter requires substantial efforts and users often have to scan information streams by hand. In this paper, we approach this problem by means of faceted search. We propose strategies for inferring facets and facet values on Twitter by enriching the semantics of individual Twitter messages and present di erent methods, including personalized and context-adaptive methods, for making faceted search on Twitter more effective.

Source

http://fabianabel.de/papers/2011-wis-twitter-faceted-search-sasweb.pdf

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Abstract

Purpose - To evaluate the extent to which familiarity with search topics affects the ways in which users select and browse search terms in a thesaurus-enhanced search setting. Design/methodology/approach - An experimental methodology was adopted to study users' search behaviour in an operational information retrieval environment. Findings - Topic familiarity and subject knowledge influence some search and interaction behaviours. Searches involving moderately and very familiar topics were associated with browsing around twice as many thesaurus terms as was the case for unfamiliar topics. Research limitations/implications - Some search behaviours such as thesaurus browsing and term selection could be used as an indication of user levels of topic familiarity. Practical implications - The results of this study provide design implications as to how to develop personalized search interfaces where users with varying levels of familiarity with search topics can carry out searches. Originality/value - This paper establishes the importance of topic familiarity characteristics and the effects of those characteristics on users' interaction with search interfaces enhanced with semantic tools such as thesauri.

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Abstract

Purpose - The growing volumes of semantic data available in the web result in the need for handling the information overload phenomenon. The potential of this amount of data is enormous but in most cases it is very difficult for users to visualize, explore and use this data, especially for lay-users without experience with Semantic Web technologies. The paper aims to discuss these issues. Design/methodology/approach - The Visual Information-Seeking Mantra "Overview first, zoom and filter, then details-on-demand" proposed by Shneiderman describes how data should be presented in different stages to achieve an effective exploration. The overview is the first user task when dealing with a data set. The objective is that the user is capable of getting an idea about the overall structure of the data set. Different information architecture (IA) components supporting the overview tasks have been developed, so they are automatically generated from semantic data, and evaluated with end-users. Findings - The chosen IA components are well known to web users, as they are present in most web pages: navigation bars, site maps and site indexes. The authors complement them with Treemaps, a visualization technique for displaying hierarchical data. These components have been developed following an iterative User-Centered Design methodology. Evaluations with end-users have shown that they get easily used to them despite the fact that they are generated automatically from structured data, without requiring knowledge about the underlying semantic technologies, and that the different overview components complement each other as they focus on different information search needs. Originality/value - Obtaining semantic data sets overviews cannot be easily done with the current semantic web browsers. Overviews become difficult to achieve with large heterogeneous data sets, which is typical in the Semantic Web, because traditional IA techniques do not easily scale to large data sets. There is little or no support to obtain overview information quickly and easily at the beginning of the exploration of a new data set. This can be a serious limitation when exploring a data set for the first time, especially for lay-users. The proposal is to reuse and adapt existing IA components to provide this overview to users and show that they can be generated automatically from the thesaurus and ontologies that structure semantic data while providing a comparable user experience to traditional web sites.

Date

20. 1.2015 18:30:22

Series

Special issue: Semantic search

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Abstract

The performance of 8 ranking algorithms was evaluated with respect to their effectiveness in ranking terms for query expansion. The evaluation was conducted within an investigation of interactive query expansion and relevance feedback in a real operational environment. Focuses on the identification of algorithms that most effectively take cognizance of user preferences. user choices (i.e. the terms selected by the searchers for the query expansion search) provided the yardstick for the evaluation of the 8 ranking algorithms. This methodology introduces a user oriented approach in evaluating ranking algorithms for query expansion in contrast to the standard, system oriented approaches. Similarities in the performance of the 8 algorithms and the ways these algorithms rank terms were the main focus of this evaluation. The findings demonstrate that the r-lohi, wpq, enim, and porter algorithms have similar performance in bringing good terms to the top of a ranked list of terms for query expansion. However, further evaluation of the algorithms in different (e.g. full text) environments is needed before these results can be generalized beyond the context of the present study

Date

22. 2.1996 13:14:10

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Abstract

A substantial fraction of web search queries contain references to entities, such as persons, organizations, and locations. Recently, methods that exploit named entities have been shown to be more effective for query expansion than traditional pseudorelevance feedback methods. In this article, we introduce a supervised learning approach that exploits named entities for query expansion using Wikipedia as a repository of high-quality feedback documents. In contrast with existing entity-oriented pseudorelevance feedback approaches, we tackle query expansion as a learning-to-rank problem. As a result, not only do we select effective expansion terms but we also weigh these terms according to their predicted effectiveness. To this end, we exploit the rich structure of Wikipedia articles to devise discriminative term features, including each candidate term's proximity to the original query terms, as well as its frequency across multiple article fields and in category and infobox descriptors. Experiments on three Text REtrieval Conference web test collections attest the effectiveness of our approach, with gains of up to 23.32% in terms of mean average precision, 19.49% in terms of precision at 10, and 7.86% in terms of normalized discounted cumulative gain compared with a state-of-the-art approach for entity-oriented query expansion.

Date

22. 8.2014 17:07:50

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Abstract

The growing availability of data in electronic form, the expansion of the World Wide Web and the accessibility of computational methods for large-scale data processing have allowed researchers in Information Retrieval (IR) to design systems which can effectively and efficiently constrain search within the boundaries given by context, thus transforming classical search into contextual search. Contextual Search: A Computational Framework introduces contextual search within a computational framework based on contextual variables, contextual factors and statistical models. It describes how statistical models can process contextual variables to infer the contextual factors underlying the current search context. It also provides background to the subject by: placing it among other surveys on relevance, interaction, context, and behaviour; providing a description of the contextual variables used for implementing the statistical models which represent and predict relevance and contextual factors; and providing an overview of the evaluation methodologies and findings relevant to this subject. Contextual Search: A Computational Framework is a highly recommended read, both for beginners who are embarking on research in this area and as a useful reference for established IR researchers.

Content

Table of contents 1. Introduction 2. Query Intent 3. Personal Interest 4. Document Quality 5. Contextual Search Evaluation 6. Conclusions Acknowledgements References A. Implementations

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Abstract

Exploratory search is an increasingly important activity yet challenging for users. Although there exists an ample amount of research into understanding exploration, most of the major information retrieval (IR) systems do not provide tailored and adaptive support for such tasks. One reason is the lack of empirical knowledge on how to distinguish exploratory and lookup search behaviors in IR systems. The goal of this article is to investigate how to separate the 2 types of tasks in an IR system using easily measurable behaviors. In this article, we first review characteristics of exploratory search behavior. We then report on a controlled study of 6 search tasks with 3 exploratory-comparison, knowledge acquisition, planning-and 3 lookup tasks-fact-finding, navigational, question answering. The results are encouraging, showing that IR systems can distinguish the 2 search categories in the course of a search session. The most distinctive indicators that characterize exploratory search behaviors are query length, maximum scroll depth, and task completion time. However, 2 tasks are borderline and exhibit mixed characteristics. We assess the applicability of this finding by reporting on several classification experiments. Our results have valuable implications for designing tailored and adaptive IR systems.

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Abstract

Query expansion is the process of supplementing or replacing the original query terms with additional terms either at the search formulation or search reformulation stages. Different approaches to implementing query expansion are considered in three online catalogs

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Abstract

This study explores how experts and novices in pedagogics expanded queries supported by the ERIC thesaurus, and how this was connected to the search effectiveness in an easy and a difficult search task. The expert group consisted of 15 undergraduates in pedagogy and the novice group of 15 students with no previous studies in this field. Their search logs were recorded and a pre- and post-search interview was conducted. The results show that the number and type of terms selected from the thesaurus for expansion by experts improved search effectiveness, whereas there were no connections between the use of thesaurus and improvement of effectiveness among novices. The effectiveness of expansions varied between the facets of the queries. Thus, a vital condition for benefiting from a thesaurus in query expansion to improve search results is sufficient familiarity with the search topic. The results suggest also that it is not in the first place the number of terms used in expansion, but their type and quality that are crucial for search success.

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Abstract

This paper provides a review of the literature related to the application of domain-specific thesauri in the search and retrieval process. Focusing an studies that adopt a user-centred approach, the review presents a survey of the methodologies and results from empirical studies undertaken an the use of thesauri as sources of term selection for query formulation and expansion during the search process. It summarises the ways in which domain-specific thesauri from different disciplines have been used by various types of users and how these tools aid users in the selection of search terms. The review consists of two main sections: first, studies an thesaurus-aided search term selection; and second, studies dealing with query expansion using thesauri. Both sections are illustrated with case studies that have adopted a user-centred approach.

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Abstract

The use of thesaurus-enhanced search tools is an the increase. This paper provides an insight into end-users interaction with and perceptions of such tools. In particular the overlap between users' initial query formulation and thesaurus structures is investigated. This investigation involved the performance of genuine search tasks an the CAB Abstracts database by academic users in the domain of veterinary medicine. The perception of these users regarding the nature and usefulness of the terms suggested from the thesaurus during the search interaction is reported. The results indicated that around 80% of terms entered were matched either exactly or partially to thesaurus terms. Users found over 90% of the terms suggested to be close to their search topics and where terms were selected they indicated that around 50% were to support a 'narrowing down' activity. These findings have implications for the design of thesaurus-enhanced interfaces.

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Abstract

The authors present a model of information searching in thesaurus-enhanced search systems, intended as a reference model for system developers. The model focuses on user-system interaction and charts the specific stages of searching an indexed collection with a thesaurus. It was developed based on literature, findings from empirical studies, and analysis of existing systems. The model describes in detail the entities, processes, and decisions when interacting with a search system augmented with a thesaurus. A basic search scenario illustrates this process through the model. Graphical and textual depictions of the model are complemented by a concise matrix representation for evaluation purposes. Potential problems at different stages of the search process are discussed, together with possibilities for system developers. The aim is to set out a framework of processes, decisions, and risks involved in thesaurus-based search, within which system developers can consider potential avenues for support.