Search results

Survey-based studies suggest that search engines are trusted more than social media or even traditional news, although cases of false information or defamation are known. The purpose of this paper is to analyze query suggestion features of three search engines to see if these features introduce some bias into the query and search process that might compromise this trust. The authors test the approach on person-related search suggestions by querying the names of politicians from the German Bundestag before the German federal election of 2017.

This study introduces a framework to systematically examine and automatically analyze the varieties in different query suggestions for person names offered by major search engines. To test the framework, the authors collected data from the Google, Bing and DuckDuckGo query suggestion APIs over a period of four months for 629 different names of German politicians. The suggestions were clustered and statistically analyzed with regards to different biases, like gender, party or age and with regards to the stability of the suggestions over time.

By using the framework, the authors located three semantic clusters within the data set: suggestions related to politics and economics, location information and personal and other miscellaneous topics. Among other effects, the results of the analysis show a small bias in the form that male politicians receive slightly fewer suggestions on “personal and misc” topics. The stability analysis of the suggested terms over time shows that some suggestions are prevalent most of the time, while other suggestions fluctuate more often.

This study proposes a novel framework to automatically identify biases in web search engine query suggestions for person-related searches. Applying this framework on a set of person-related query suggestions shows first insights into the influence search engines can have on the query process of users that seek out information on politicians.

The purpose of this study is to generate diversified results for temporally ambiguous queries and the candidate queries are ensured to have a high coverage of subtopics, which are derived from different temporal periods.

Two novel time-aware query suggestion diversification models are developed by integrating semantics and temporality information involved in queries into two state-of-the-art explicit diversification algorithms (i.e. IA-select and xQuaD), respectively, and then specifying the components on which these two models rely on. Most importantly, first explored is how to explicitly determine query subtopics for each unique query from the query log or clicked documents and then modeling the subtopics into query suggestion diversification. The discussion on how to mine temporal intent behind a query from query log is also followed. Finally, to verify the effectiveness of the proposal, experiments on a real-world query log are conducted.

Preliminary experiments demonstrate that the proposed method can significantly outperform the existing state-of-the-art methods in terms of producing the candidate query suggestion for temporally ambiguous queries.

This study reports the first attempt to generate query suggestions indicating diverse interested time points to the temporally ambiguous (input) queries. The research will be useful in enhancing users’ search experience through helping them to formulate accurate queries for their search tasks. In addition, the approaches investigated in the paper are general enough to be used in many domains; that is, experimental information retrieval systems, Web search engines, document archives and digital libraries.

The purpose of this paper is to study the spelling suggestion (SS) problem for extensible markup language (XML) keyword search, which provides users with alternative queries that may better express users search intention.

To return the suggested queries more efficiently, the authors evaluate the quality of the query by estimating the selectivity and quality of each query pattern. The selectivity estimation is based on the XSketch synopsis, which summarizes the structure and value distribution of the original XML data source. The authors propose an approach to generating the top-K query candidates.

Experiments with real datasets verify the effectiveness and efficiency of the authors' approach.

The authors proposed a SS approach based on the XSketch summary.

Prior studies have shown that terminology support can improve health information retrieval but have not taken into account the characteristics of the user performing the search. In this chapter, the impact of translating queries’ terms between lay and medico-scientific terminology, in users with different levels of health literacy and topic familiarity, is evaluated. Findings demonstrate that medico-scientific queries demand more from the users and are mostly aimed at health professionals. In addition, these queries retrieve documents that are less readable and less well understood by users. Despite this, medico-scientific queries are associated with higher precision in the top-10 retrieved documents results and tend slightly to generate knowledge with less incorrect contents, the researchers concluded that search engines should provide query suggestions with medico-scientific terminology, whenever the user is able to digest it, that is, in users above the lowest levels of health literacy and topic familiarity. On the other hand, retrieval systems should provide lay alternative queries in users with inadequate health literacy or in those unfamiliar with a topic. In fact, the quantity of incorrect contents in the knowledge that emerges from a medico-scientific session tends to decrease with topic familiarity and health literacy. In terms of topic familiarity, the opposite happens with Graded Average Precision. Moreover, users most familiar with a topic tend to have higher motivational relevance with medico-scientific queries than with lay queries. This work is the first to consider user context features while studying the impact of a query processing technique in several aspects of the retrieval process, including the medical accuracy of the acquired knowledge.

Previous work highlights two key challenges in searching for information about individual entities (such as persons, places and organisations) over semantic data: query ambiguity and redundant attributes. The purpose of this paper is to consider these challenges and proposes the Attribute Importance Model (AIM) for clustering and ranking aggregated entity search to improve the overall users’ experience of finding and navigating entities over the Web of Data.

The proposed model describes three distinct techniques for augmenting semantic search: first, presenting entity type-based query suggestions; second, clustering aggregated attributes; and third, ranking attributes based on their importance to a given query. To evaluate the model, 36 subjects were recruited to experience entity search with and without AIM.

The experimental results show that the model achieves significant improvements over the default method of semantic aggregated search provided by Sig.ma, a leading entity search and navigation tool.

This proposal develops more informative views for aggregated entity search and exploration to enhance users’ understanding of semantic data. The user study is the first to evaluate user interaction with Sig.ma's search capabilities in a systematic way.

Current research topics in relation to health information searching focus on challenges faced by health consumers and domains used to perform the health search. Health consumers may not be capable of successfully searching for a health task due to limited medical knowledge. As such search assisting features provided on health domains are important in assisting health consumers during a search session. The purpose of this paper is to perform a preliminary exploratory research study to understand if search assisting features are visible to searchers and the usage of search assisting features when searching on a personal health task.

A convenience sampling method in a university setting and an observational type study was used. MedlinePlus is used as the search domain for this research study. While participants of this research study were first time users of MedlinePlus, they were not first time medical searchers.

Results of this research study indicate health consumers do not utilize search assisting features when searching for a personal health task. This is because health consumers are comfortable with their search skills. In other cases health consumers found the search assisting features irrelevant or had no confidence in the search assisting features presented. Key contributions of this research study indicate health consumers do not utilize search assisting features when searching for a personal health task. This is because health consumers are comfortable with their search skills. In other cases health consumers found the search assisting features irrelevant or had no confidence in the search assisting features presented.

Results of this research study has implications for health domain and human computer designers in relation to the development of specialized search assisting features and the placement of these features. Theoretical contributions indicate health searchers use search assisting features minimally when searching on a personal health task.

Results of this research study indicate health consumers do not utilize search assisting features when searching for a personal health task. This is because health consumers are comfortable with their search skills. In other cases health consumers found the search assisting features irrelevant or had no confidence in the search assisting features presented.

Much previous research on improving information retrieval applications has focused on developing entirely new systems with advanced searching features. Unfortunately, most users seldom utilize these advanced features. This research explores the use of a software agent that assists the user during the search process. The agent was developed as a separate, stand‐alone component to be integrated with existing information retrieval systems. The performance of an information retrieval system with the integrated agent was subjected to an evaluation with 30 test subjects. The results indicate that agents developed using both results from previous user studies and rapidly modeling user information needs can result in an improvement in precision. Implications for information retrieval system design and directions for future research are outlined.

The purpose of this paper is to highlight the retrieval effectiveness of search engines taking into consideration both precision and relative recall.

The study is based on search engines that are selected on the basis of Alexa (Actionable Analytics for the web) Rank. Alexa listed top 500 sites, namely, search engines, portals, directories, social networking sites, networking tools, etc. But the scope of study is confined to only general search engines on the basis of language which was confined to English. Therefore only two general search engines are selected for the study . Alexa reports Google.com as the most visited website worldwide and Yahoo.com as the fourth most visited website globally. A total of 15 queries were selected randomly from PG students of Department of Library and Information Science during a period of eight days (from May 8 to May 15, 2014) which are classified manually into navigational, informational and transactional queries. However, queries are largely distributed on the two selected search engines to check their retrieval effectiveness as a training data set in order to define some characteristics of each type. Each query was submitted to the selected search engines which retrieved a large number of results but only the first 30 results were evaluated to limit the study in view of the fact that most of the users usually look up under the first hits of a query.

The study estimated the precision and relative recall of Google and Yahoo. Queries using concepts in the field of Library and Information Science were tested and were divided into navigational queries, informational queries and transactional queries. Results of the study showed that the mean precision of Google was high with (1.10) followed by Yahoo with (0.88). While as, mean relative recall of Google was high with (0.68) followed by Yahoo with (0.31), respectively.

The study highlights the retrieval effectiveness of only two search engines.

The research work is authentic and does not contain any plagiarized work.

With the increasing number of online multilingual resources, cross-language information retrieval (CLIR) has drawn much attention from the information retrieval (IR) research community. However, few studies have examined how and why multilingual searchers seek information in two or more languages, specifically how they switch and mix language in queries to get satisfying results. The purpose of this paper is to focus on Chinese–English bilinguals’ intra-sentential code-switching behaviors in online searches. The scenarios and reasons of code-switching, factors that may affect code-switching, the patterns of mixed language query formulation and reformulation and how current IR systems and other search tools can facilitate such information needs were examined.

In-depth semi-structured interviews were used as the research method. In total, 30 participants were recruited based on their English proficiency, location and profession, using a purposive sampling method.

Four scenarios and four reasons for using Chinese–English mixed language queries to cover information needs were identified, and results suggest that linguistic and cultural/social factors are of equivalent importance in code-switching behaviors. English terms and Chinese terms in queries play different roles in searches, and mixed language queries are irreplaceable by either single language queries or other search facilitating features. Findings also suggest current search engines and tools need greater emphasis in the user interface and more user education is required.

This study presents a qualitative analysis of bilinguals’ code-switching behaviors in online searches. Findings are expected to advance the theoretical understanding of bilingual users’ search strategies and interactions with IR systems, and provide insights for designing more effective IR systems and tools to discover multilingual online resources, including cross-language controlled vocabularies, personalized CLIR tools and mixed language query assistants.

This work aims to investigate the sensitivity of ranking performance with respect to the topic distribution of queries selected for ranking evaluation.

The authors reweight queries used in two TREC tasks to make them match three real background topic distributions, and show that the performance rankings of retrieval systems are quite different.

It is found that search engines tend to perform similarly on queries about the same topic; and search engine performance is sensitive to the topic distribution of queries used in evaluation.

Using experiments with multiple real‐world query logs, the paper demonstrates weaknesses in the current evaluation model of retrieval systems.