Formulate Search Queries Research Articles

Information experience of undergraduate students when optimizing google search for their study

Background of the study: Google has become the most popular search engine worldwide and its name has its own merit within information search, as such when people are searching for information they would most often say ‘google it’ rather than ‘look for it’. Purpose: The present research employs information experience perspective that investigates students of Universitas Diponegoro when they are using Google search to help complete their assignments. Method: The research method used to capture the information experience of Universitas Diponegoro students when using Google to look for information associated with their studies was the qualitative research method. Semi-structured interviews were used in this study to explore further information experience of students in exploring information using the Google search engine Findings: The findings revealed that undergraduate students have their own techniques when using Google search. They are well familiar with formulating search queries; the majority being able to take on this stage of task easily. However, students found it challenging when they have to start evaluating which of the search results would give the most relevant information to help with their assignments. Conclusions: Students’ prior knowledge appeared to be the governing factor of how well they are able to determine which of the compiled information sources are best for their study. Every student has their own best approach and experience to optimize Google. Generally, it was found that the students within this study had similar ways in formulating keywords to search for information on Google and were only different in certain details of experiences.

Remote OPAC users’ search query reformulation (SQR) patterns: a transaction log analysis

PurposeUsers' search logs are implicit feedbacks on how searchers interact with online information retrieval (IR) systems. The purpose of this paper is to analyze search query reformulation (SQR) patterns of University of Dar es Salaam remote OPAC users.Design/methodology/approachQualitative and quantitative analysis of transaction logs were employed to ascertain the characteristics of search queries and the patterns in which remote OPAC users reformulate their search queries. The study covered a period of six months, commencing from January to June 2019.FindingsA total of 30,474 search hits were submitted by remote OPAC users during the period under study. Individuals from academic and research institutions, computing consortia, and telecommunication companies are the main users of the system. Most of the searches originated from North America and Europe, with few searches coming from China and India. Besides improving search results, SQRs are linked with the existence of multiple information demands as manifested by the use of heterogeneous headwords within individual search episodes.Research limitations/implicationsData collected covered only six months. Similarly, it was however not possible to analyze users' search query formulation within specific contexts such as task-based information searching.Practical implicationsA query recommendation system should be integrated into the OPAC functionalities to improve users' search experiences. Alternatively, there should be a migration to a new system that offers more advanced search features and functionalities.Originality/valueThe study has contributed new insights in SQR studies particularly on how non-institutional affiliated users translate their information needs into search queries during information searching processes.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-09-2020-0389

Searching differently? How political attitudes impact search queries about political issues

For many, search engines are crucial gateways to (political) information. While extant research is concerned with algorithmic bias, user choices had been largely neglected. Yet, search queries are the key way in which searchers explicate their information need. Building on framing theory and selective exposure, we argue that queries are ingrained with (political) predispositions: issue frames in mind of searchers manifest themselves in search terms and queries. Using Dutch survey data ( N = 1994), and manual coding and latent class analysis, we explore how types of people formulate search queries about immigration and climate change ( RQ1). A regression analysis shows how these searcher types relate to political attitudes and socio-demographic characteristics ( RQ2). Notably, searchers formulate queries in ways that are related to their political positions, but this differs for different issues. These findings imply systematic differences in user choices which future research needs to consider when auditing search engines.

Extracting Searching as Learning Tasks Based on IBRT Approach

With the rapid development of the World Wide Web and information retrieval technology, learning supported by searching engines (such as making travel plans) has boomed over the past years. With the help of search engines, learners can easily retrieve and find large amounts of information on the web. Recent research in the searching as learning (SAL) area has associated web searching with learning. In SAL processes, web learners recursively plan tasks, formulate search queries, obtain information from web pages, and change knowledge structures, to gradually complete their learning goals. To improve the experiences of web learners, it is important to accurately present and extract tasks. Using learning styles and similarity metrics, we first proposed an IBRT model to implement structured representations of the SAL process for each learner. SAL tasks were then extracted from the structures of IBRT. In this study, a series of experiments were carried out against assignment datasets from the Northeastern University (China) UWP Programming Course. Comparison results show that the proposed method can significantly improve the performance of SAL task extraction.

The “Bitcoin Generator” Scam

The “Bitcoin Generator Scam” (BGS) is a cyberattack in which scammers promise to provide victims with free cryptocurrencies in exchange for a small mining fee. In this paper, we present a data-driven system to detect, track, and analyze the BGS. It works as follows: we first formulate search queries related to BGS and use search engines to find potential instances of the scam. We then use a crawler to access these pages and a classifier to differentiate actual scam instances from benign pages. Last, we automatically monitor the BGS instances to extract the cryptocurrency addresses used in the scam. A unique feature of our system is that it proactively searches for and detects the scam pages. Thus, we can find addresses that have not yet received any transactions.Our data collection project spanned 16 months, from November 2019 to February 2021. We uncovered more than 8,000 cryptocurrency addresses directly associated with the scam, hosted on over 1,000 domains. Overall, these addresses have received around 8.7 million USD, with an average of 49.24 USD per transaction.Over 70% of the active addresses that we are capturing are detected before they receive any transactions, that is, before anyone is victimized. We also present some post-processing analysis of the dataset that we have captured to aggregate attacks that can be reasonably confidently linked to the same attacker or group.Our system is one of the first academic feeds to the APWG eCrime Exchange database. It has been actively and automatically feeding the database since November 2020.

Web Search Training and Query Formulation Behaviour of Undergraduates of Kano State University of Science and Technology (KUST) Wudil, Kano State, Nigeria

The Web is predominant source of information upon which most university students depend for accomplishing their academic tasks. This study investigated the influence of web search training program on query formulation behaviour of undergraduates of Kano State University of Science and Technology (KUST), Wudil, Kano State, Nigeria. The study was set to answer two research questions and tested a hypothesis. Quasi-experimental design of the pretest and posttest type was used for the study. All 20,679 undergraduates of KUST made up the target population. Convenience sampling technique was used to recruit only 35 students, who participated in a six-week training program and lab sessions. A structured and validated questionnaire was used for data collection at both pretest and posttest levels. Cronbach’s alpha reliability coefficient for the constructs ranged 0.78 to 0.88. Two adapted search task tests were used to assess students’ query formulation behaviour on the web before and after the training. Data were analyzed with descriptive statistics and t-test. Findings revealed that web search training had significant effect on the query formulation behaviour of participants (t(34) = 17.680; p <0.05,η2= 0.857). The study concluded that web search training is essential for all undergraduate students of KUST Wudil to enable them to develop appropriate web search query formulation behaviour and also accomplish their academic search tasks. The study recommended that academic librarians should include practical web search sessions in their use of library classes for freshmen. Keywords: Boolean logic, Query formulation behavior, Undergraduate students, Web Searching, Web search training

The use of OPAC query logs to support evidence-informed information literacy (IL) training at The University of Dar es Salaam

This paper reports the results of the study which analysed OPAC users’ searching behaviour through the use of search queries. The study analysed how OPAC search queries can be used to assess the effectiveness of the information literacy programme offered by the University of Dar es Salaam. Data were collected unobtrusively and stored in the OPAC web server’s log file. OPAC search logs were qualitatively and quantitatively analysed to determine search patterns and search query formulation. Information literacy course contents were also qualitatively analysed and compared with users’ search activities. The results suggest that search query logs are essential and high quality evidence that can be used to inform information literacy pedagogical methods and course contents improvement. A comparison between OPAC users’ search behaviours and information lteracy course content identified some areas for readjusting the way information literacy instructions are delivered and improving OPAC search functionalities. The study has contributed to the conceptual understanding and the use of evidence to support evidence-informed practices (EIPs) in delivering information literacy courses.

Artificial intelligence for patent prior art searching

This study explored how artificial intelligence (AI) could assist patent examiners as part of the prior art search process. The proof-of-concept allowed experimentation with different AI techniques to suggest search terms, retrieve most relevant documents, rank them and visualise their content. The study suggested that AI is less effective in formulating search queries but can reduce the time and cost of the process of sifting through a large number of patents. The study highlighted the importance of the humanin-the-loop approach and the need for better tools for human-centred decision and performance support in prior art searching.

A heuristic algorithm for generating the numerical terms of a linguistic variable

In this paper we describe an easy-to-implement algorithm for automatedgeneration of the linguistic variable term membership functions to allow for information search in a relational database based on qualitative criteria by means of the SQL query language. The proposed algorithm makes it possible to calculate the parameters of the triangular and trapezoid membership functions taking into account the distribution of the variable of interest stored in the database. The algorithm defines the intervals covered by the term bases, so that each interval contains about the same number of values. Upper bounds of the defined intervals are used to calculate the parameters of membership functions. The parameters of the membership functions generated with this algorithm can be easily calculated with the limited computational means of the SQL language. We review the algorithm realizations for the generation of 3 and 5 terms of a linguistic variable based on a sample from a database containing 100 or 500 different values. The membership functions obtained through the algorithm have the required properties of orderliness, completeness, consistency and normality. They do not require further approximation. Unlike the known methods, the algorithm does not require significant computing resources, the use of specialized software, settings configuring, or a training set formation. The algorithm implementation creates opportunities to support fuzzy search queries in relational databases using the means of the SQL language, as limited as they are. Thus, the system’s level of intelligence would be increased, and the user would be provided with the means of search query formulation in a natural language. The linguistic variable terms generated using our algorithm can be used within the framework of a fuzzy rule-based knowledge base of an information system, as well as to perform fuzzy inference.

Search Query Formation by Strategic Consumers

Submitting queries to search engines has become a major way in which consumers search for information and products. The massive amount of search query data available today has the potential to provide valuable information on consumer preferences. In order to unlock this potential, it is necessary to understand how consumers translate their preferences into search queries. In this paper, we argue that if consumers are strategic, then their search queries should not be a direct representation of their preferences: strategic consumers should formulate queries that are likely to retrieve the content they are searching for, rather than being merely similar to that content. We present secondary and primary evidence that is consistent with a strategic view of query formation. Using secondary field data, we illustrate the benefits for consumers from strategically formulating queries which contain only a subset of the terms they are interested in, but which are effective at retrieving other terms of interest. Our incentive-aligned lab experiment confirms that consumers have at least some ability to be strategic when formulating search queries.

Social Semantic Search

When researchers formulate search queries to find relevant content on the Web, those queries typically consist of keywords that can only be matched in the content or its metadata. The Web of Data extends this functionality by bringing structure and giving well-defined meaning to the content and it enables humans and machines to work together using controlled vocabularies. Due the high degree of mismatches between the structure of the content and the vocabularies in different sources, searching over multiple heterogeneous repositories of structured data is considered challenging. Therefore, the authors present a semantic search engine for researchers facilitating search in research related Linked Data. To facilitate high-precision interactive search, they annotated and interlinked structured research data with ontologies from various repositories in an effective semantic model. Furthermore, the authors' system is adaptive as researchers can synchronize using new social media accounts and efficiently explore new datasets.

Effects of task complexity on online search behavior of adolescents

Evaluation of information during information problem‐solving processes already starts when trying to select the appropriate search result on a search engine results page (SERP). Up to now, research has mainly focused on the evaluation of webpages, while the evaluation of SERPs received less attention. Furthermore, task complexity is often not taken into account. A within‐subjects design was used to study the influence of task complexity on search query formulation, evaluation of search results, and task performance. Three search tasks were used: a fact‐finding, cause–effect, and a controversial topic task. To measure perceptual search processes, we used a combination of log files, eye‐tracking data, answer forms, and think‐aloud protocols. The results reveal that an increase in task complexity results in more search queries and used keywords, more time to formulate search queries, and more considered search results on the SERPs. Furthermore, higher ranked search results were considered more often than lower ranked results. However, not all the results for the most complex task were in line with expectations. These conflicting results can be explained by a lack of prior knowledge and the possible interference of prior attitudes.

New Setting, Same Skill: Teaching Geography Students to Transfer Information Literacy Skills From Familiar to Unfamiliar Contexts

Existing research shows that high school students do not possess information literacy skills adequate to function in a high-tech society that relies so heavily on information. If students are taught these skills, they struggle to apply them. This small-scale intervention focused on helping Geography students at a low-socioeconomic high school in Auckland, New Zealand to transfer information literacy skills from familiar to unfamiliar topics. It tested whether teaching information literacy skills via direct instruction, and then giving students the opportunity to use these skills in structured practice sessions online, would help them transfer those skills. The need for the study arose from a gap in existing research into teaching information literacy for transfer. The findings of this study indicated that it was relatively easy for the students to learn to formulate search queries, but they need more help evaluating information sources. The findings also showed that direct feedback from a teacher is vital to teaching information literacy. The findings are significant in that they add to a growing body of research that teaching information literacy through practical research projects is potentially more effective than decontextualising the skills and teaching them as a standalone topic. Furthermore, the findings support existing research that teacher feedback is a necessary component of teaching information literacy. Keywords: Geography education, Information literacy skills

Exploring barriers of enterprise search implementation: a qualitative user study

Purpose – Despite a growing demand for enterprise search from practice, little is known about its implementation from an academic perspective. As the few available practice-oriented investigations show, enterprise search user satisfaction is rather low. The purpose of this paper is therefore to explore user-centric barriers of enterprise search implementation in order to increase user satisfaction. Design/methodology/approach – Results are built on a qualitative user study in an R & D organization. Findings are gained from think-aloud observations introduced by semi-structured interviews in which ten knowledge workers explore a newly implemented enterprise search tool. Findings – Findings illustrate barriers that knowledge workers have to overcome when adopting enterprise search to find project-relevant documents. Implementation barriers relate to selection for keywords, search query formulation, availability and adequacy of metadata, relevance judging of search results, current search strategies, and overall perception of enterprise search usefulness. Research limitations/implications – Limitations address the piloted enterprise search software, along with its specific configuration and scope, the chosen research approach of generating qualitative findings from a single case, and the size of the involved sample of engineers. Implications address measures to increase enterprise search adoption. Practical implications – This study provides project managers with knowledge to take appropriate actions in the early phases of enterprise search implementation, and even prior to that, to raise the success of enterprise search projects. It contributes to a better understanding of enterprise search engine user needs and assists in concretizing user requirements. Originality/value – While existing studies primarily focus on advancing the technical perspective of search in organizations, the author elaborate on the under-investigated social and organizational aspects. The author furthermore stress the importance of user-centered approaches for enterprise search adoption.

Enhanced Arabic Document Retrieval Using Optimized Query Paraphrasing

Query paraphrasing aims to construct a better formulation of user queries in order to enhance retrieval. Formulating search queries remains complicated for a subset of Web users. In a typical situation, a user will not receive satisfactory results from the submitted search query and will subsequently attempt different query paraphrases. The Arabic vocabulary is rich in synonyms and hyponyms. Such richness of synonyms makes automation of the paraphrasing technique crucial for Arabic information retrieval systems in order to facilitate the process of paraphrasing synonyms. In this article, we propose an enhancement for Arabic information retrieval using a query paraphrasing technique. Furthermore, two query paraphrasing optimization techniques are proposed to overcome the time complexity and exhaustive calculation of existing query paraphrasing techniques. One of these techniques uses a genetic algorithm (GA–QP), and the other employs the artificial bee colony algorithm (ABC–QP). The performance of these two algorithms is compared. ABC–QP shows an improvement in Arabic information retrieval performance compared with the genetic algorithm query paraphrasing system.

An Efficient Searching for Non-FormulatingQueries

Formulating search queries are to determine relation completion (RC) in concert revenant drawback that's central to the success of novel huge knowledge applications like Entity Reconstruction and knowledge Enrichment. Given a linguistic relation ℜ and relation completion makes an attempt at linking entity pairs between 2 entity lists and to accomplish the RC goals, we tend to propose to formulate search queries for every question entity α supported some auxiliary info, so to observe its target entity β from the set of retrieved document .The amount of large available data could decrease the chance of finding appropriate target entities exactly and also an alternative method, is to propose CoRE method that uses context terms and to extract data from database. Finally, the experimental results supported many real-world collections demonstrate that CoRE reaches a way higher accuracy than PaRE for the aim of RC

A Context-Aware Relation Extraction Methodfor Relation Completion

Identify relation completion (RC) as one recurring problem that is central to the success of novel big data applications such as Entity Reconstruction and Data Enrichment. Given a semantic relation R, RC attempts at linking entity pairs between two entity lists under the relation R. To accomplish the RC goals, propose to formulate search queries for each query entity a based on some auxiliary information, so that to detect its target entity b from the set of retrieved documents. For instance, a pattern-based method (PaRE) uses extracted patterns as the auxiliary information in formulating search queries. However, high-quality patterns may decrease the probability of finding suitable target entities. As an alternative, propose CoRE method that uses context terms learned surrounding the expression of a relation as the auxiliary information in formulating queries. The experimental results based on several real-world web data collections demonstrate that CoRE reaches a much higher accuracy than PaRE for the purpose of RC.

Content Bias in Online Health Search

Search engines help people answer consequential questions. Biases in retrieved and indexed content (e.g., skew toward erroneous outcomes that represent deviations from reality), coupled with searchers' biases in how they examine and interpret search results, can lead people to incorrect answers. In this article, we seek to better understand biases in search and retrieval, and in particular those affecting the accuracy of content in search results, including the search engine index, features used for ranking, and the formulation of search queries. Focusing on the important domain of online health search, this research broadens previous work on biases in search to examine the role of search systems in contributing to biases. To assess bias, we focus on questions about medical interventions and employ reliable ground truth data from authoritative medical sources. In the course of our study, we utilize large-scale log analysis using data from a popular Web search engine, deep probes of result lists on that search engine, and crowdsourced human judgments of search result captions and landing pages. Our findings reveal bias in results, amplifying searchers' existing biases that appear evident in their search activity. We also highlight significant bias in indexed content and show that specific ranking signals and specific query terms support bias. Both of these can degrade result accuracy and increase skewness in search results. Our analysis has implications for bias mitigation strategies in online search systems, and we offer recommendations for search providers based on our findings.

The impact of users’ verbal/imagery cognitive styles on their Web search behavior

Purpose – The purpose of this paper is to investigate the effects of verbal-imagery cognitive styles of information searching behavior of users in using the Web. Design/methodology/approach – In all, 44 participants were recruited for this study. The participants’ cognitive styles were measured by using Riding's Cognitive Style Analysis test. Three search tasks were designed based on Kim's search task definitions. Moreover, an individual lab session was arranged and then participants’ memos were analyzed using content analysis. Findings – In all, 48 strategies in four categories of behaviors in searching the Web were identified. There were associations between users’ cognitive styles and their information searching behavior. The participants’ selection of the search initiation behaviors varied, so that imagers suffered from more varied initial behavior than verbalizers. The verbalizers tended to search in a narrow area, then broadening the area and following structured navigation and reading behavior to process information, while imagers tended to search in a general area, then narrowing down the search and adopting mixed navigational styles and mixed behaviors to process information. This study revealed that there was a difference in search performance of verbalizers and imagers descriptively, as verbalizers spent more time compared to imagers and imagers visited more nodes than verbalizers for the tasks completion. In addition, the task was an important variable influencing the search performance. Based on the key findings (search initiation behaviors, formulating search queries, navigational behaviors, information processing behaviors), a conceptual pattern of Web searching and cognitive styles is presented. Research limitations/implications – The study provides a new understanding of Web users’ information search behavior based on cognitive styles which contributes to the theoretical basis of Web search research. It also raises various questions within the context of user studies Originality/value – The paper adopted a mixed approach in the area of information searching on the Web. A valuable contribution lies in the methods developed.

CoRE: A Context-Aware Relation Extraction Method for Relation Completion

We identify relation completion (RC) as one recurring problem that is central to the success of novel big data applications such as Entity Reconstruction and Data Enrichment. Given a semantic relation ℜ, RC attempts at linking entity pairs between two entity lists under the relation ℜ. To accomplish the RC goals, we propose to formulate search queries for each query entity α based on some auxiliary information, so that to detect its target entity β from the set of retrieved documents. For instance, a pattern-based method (PaRE) uses extracted patterns as the auxiliary information in formulating search queries. However, high-quality patterns may decrease the probability of finding suitable target entities. As an alternative, we propose CoRE method that uses context terms learned surrounding the expression of a relation as the auxiliary information in formulating queries. The experimental results based on several real-world web data collections demonstrate that CoRE reaches a much higher accuracy than PaRE for the purpose of RC.