Design Of Search Engine Research Articles

The design of advertising text keyword recommendation for internet search engines

As the growth of internet technology, human life is full of various advertisements. It is possible for individuals to obtain the advertising information they require, whether in an online or offline context. A research proposal is presented with the objective of enhancing the precision of online advertising recommendations. The proposal is based on the design of internet search engine advertising text keyword recommendation models, which integrate entity naming recognition models to facilitate tasks such as text classification and feature extraction. A recommendation algorithm based on content similarity is used to achieve keyword recommendation. Under the similarity calculation method of continuous bag-of-words model, when K is 100, the model weighted precision of the feature extraction method based on graph sorting and inverse text frequency index is 0.88, the weighted recall is 0.76, and the weighted F1-score is 0.82. In offline simulation testing, 85 % of the keyword recommendation model's recommendation time is less than 1 s, 99 % of the recommendation time is less than 2 s, and the recommendation cost can be significantly reduced by 75 %. In practical applications, the recommendation efficiency of this method can reach 96.3 %, and the recommendation precision can reach 95.8 %. The recommended satisfaction rate can reach 99.5 %. The results demonstrate that this method can provide highly accurate keyword recommendations and reduce the cost of advertising placement. Furthermore, it has been recognized and praised by users.

WSREB Mechanism: Web Search Results Exploration Mechanism for Blind Users

In the contemporary digital landscape, web search functions as a pivotal conduit for information dissemination. Nevertheless, blind users (BUs) encounter substantial barriers in leveraging online services, attributable to intrinsic deficiencies in the information structure presented by online platforms. A critical analysis reveals that a considerable segment of BUs perceive online service access as either challenging or unfeasible, with only a fraction of search endeavors culminating successfully. This predicament stems largely from the linear nature of information interaction necessitated for BUs, a process that mandates sequential content relevancy assessment, consequently imposing cognitive strain and fostering information disorientation. Moreover, the prevailing evaluative metrics for web service efficacy—precision and recall—exhibit a glaring oversight of the nuanced behavioral and usability facets pertinent to BUs during search engine design. Addressing this, our study introduces an innovative framework to facilitate information exploration, grounded in the cognitive principles governing BUs. This framework, piloted using the Wikipedia dataset, seeks to revolutionize the search result space through categorical organization, thereby enhancing accessibility for BUs. Empirical and usability assessments, conducted on a cohort of legally blind individuals (N = 25), underscore the framework’s potential, demonstrating notable improvements in web content accessibility and system usability, with categorical accuracy standing at 84% and a usability quotient of 72.5%. This research thus holds significant promise for redefining web search paradigms to foster inclusivity and optimized user experiences for BUs.

A COVID-19 Search Engine (CO-SE) with Transformer-based architecture

Coronavirus disease (COVID-19) is an infectious disease, which is caused by the SARS-CoV-2 virus. Due to the growing literature on COVID-19, it is hard to get precise, up-to-date information about the virus. Practitioners, front-line workers, and researchers require expert-specific methods to stay current on scientific knowledge and research findings. However, there are a lot of research papers being written on the subject, which makes it hard to keep up with the most recent research. This problem motivates us to propose the design of the COVID-19 Search Engine (CO-SE), which is an algorithmic system that finds relevant documents for each query (asked by a user) and answers complex questions by searching a large corpus of publications. The CO-SE has a retriever component trained on the TF–IDF vectorizer that retrieves the relevant documents from the system. It also consists of a reader component that consists of a Transformer-based model, which is used to read the paragraphs and find the answers related to the query from the retrieved documents. The proposed model has outperformed previous models, obtaining an exact match ratio score of 71.45% and a semantic answer similarity score of 78.55%. It also outperforms other benchmark datasets, demonstrating the generalizability of the proposed approach.

Understanding the seeking-encountering tension: Roles of foreground and background task urgency

Echoing the urge for in-depth research on the mechanisms of the seeking-encountering tension, i.e., foreground and background tasks co-exist and switch between each other, this study conducted a true experiment based on 2*2 factorial design to examine the roles of task urgency in such tension. The experiment used an eye tracker and the build-in screen recorder to capture 39 participants’ eye movements and clicking behavior when they performed foreground search tasks in the laboratory, while the information related to the background task assigned in advance as coursework was embedded in the search results. It was found that the information seeking and encountering processes were interwoven and competed persistently with each other for users’ attention and actions, and the seeking-encountering tension led to a lose-lose outcome. While there existed negative relationships between foreground task urgency and multiple search measures, positive relationships were revealed between background task urgency and the examining of the encountered information. In particular, the high foreground task urgency suppressed the noticing of the stimulus related to the background task. The results not only enrich the understanding of multitasking in human information acquisition, but also generate useful practical implications for the design of search engines.

Designing An Information Framework For Semantic Search

New generation information retrieval procedures provide complex tools to remodel the design of search engines. Even though semantic analysis is gradually adopted by corporations, complex behavior of knowledge behind the information entails subsequent data learning models. Text models are currently in use through lexical features. Search engines with lexical methods lack contextual and semantic information. This barrier has been overcome with the development of deep learning methods. More accurate results can be retrieved by obtaining contextual information of different types of content such as text, image, video with neural models. In this study, a broad perspective of search engines was considered through lexical and semantic features. Semantic search methods were experimented then compared with lexical methods in data sets consisting of scientific documents. Since scientific documents are relatively well-formatted datasets and do not contain irrelevant content, the focus was on comparing semantic search methods and neural models throughout the study, without dealing with out-of-context data and semantic conflicts. As a result, semantic search methods performed better than lexical search. We conclude that current search-retrieval tasks require new perspectives in semantics where multimodal information is handled with deep learning strategies.

Children’s query formulation and search result exploration

Our research aims at understanding children’s information search and their use of information search tools during educational pursuits. We conducted an observation study with 50 New Zealand school children between the ages of 9 and 13 years old. In particular, we studied the way that children constructed search queries and interacted with the Google search engine when undertaking a range of educationally appropriate inquiry tasks. As a result of this in situ study, we identified typical query-creation and query-reformulation strategies that children use. The children worked through 250 tasks, and created a total of 550 search queries. 64.4% of the successful queries made were natural language queries compared to only 35.6% keyword queries. Only three children used the related searches feature of the search engine, while 46 children used query suggestions. We gained insights into the information search strategies children use during their educational pursuits. We observed a range of issues that children encountered when interacting with a search engine to create searches as well as to triage and explore information in the search engine results page lists. We found that search tasks posed as questions were more likely to result in query constructions based on natural language questions, while tasks posed as instructions were more likely to result in query constructions using natural language sentences or keywords. Our findings have implications for both educators and search engine designers.

Design of Digital Library Data Search Engine Based on Cloud Computing in Big Data Era

In the big data environment, people’s acquisition of knowledge is no longer limited to the contents of books, but more data information from the Internet. Intelligent search engine implements information retrieval based on knowledge level, with strong natural language understanding and knowledge processing ability, showing good personalized information service characteristics. he theory of information ecology uses the theory, viewpoint and method of ecology to study the constituent factors of information ecosystem. Excess information and meeting the needs of readers have become the main contradictions in library services. As an architecture model suitable for library application, cloud computing can integrate scattered internet information data, realize the intensification of digital libraries, and provide a new solution for building and sharing digital resources and intelligent search engines. This paper discusses the construction mode of digital library service platform based on cloud computing technology, and analyzes the application and design method of intelligent search engine of cloud service digital library under the background of big data.

The mehod of design and optimization of spatial search engine based on MongoDB

The mehod of design and optimization of spatial search engine based on MongoDB

Performance evaluation of web search engines in image retrieval: An experimental study

This experimental study used a checklist to evaluate the performance of seven search engines consisting of four Image General Search Engines (IGSEs) (namely, Google, Yahoo DuckDuckGo and Bing), and three Image Specialized Search Engines (ISSEs) (namely, Flicker, PicSearch, and GettyImages) in image retrieval. The findings indicated that the recall average of Image General Search Engines and Image Specialized Search Engines was found to be 76.32% and 24/51% with the precision average of 82/08% and 32/21%, respectively. As the results showed, Yahoo, Google and DuckDuckGo ranked at the top in image retrieval with no significant difference. However, a remarkable superiority with almost 50% difference was observed between the general and specialized image search engines. It was also found that an intense competition existed between Google, Yahoo and DuckDuckGo in image retrieval. The overall results can provide valuable insights for new search engine designers and users in choosing the appropriate search engines for image retrieval. Moreover, the results obtained through the applied equations could be used in assessing and evaluating other search tools, including search engines.

Search evolution for ease and speed: A call to action for what’s been lost

In recent years, leading website search engines have abandoned vital search features supporting complex information needs, evolving instead for the marketplace and for users seeking speedy answers to easy questions. The consequences are troubling, for researchers and for information science educators, with concerns ranging from the very relevance of search results and the unknowing of what is missing, to the novice searcher’s waning ability to frame potent queries and to learn ways to refine results. We report on a grounded theory study of search experiences of information professionals and graduate students (n = 20) that contributes a holistic understanding of web searching, using its findings both to frame what is lacking in the design evolution of search engines for complex information needs and to outline a way forward. One goal of the study was to evaluate an established model of web searching, called Net Lenses, a theoretical framework shown to be highly relevant during the study’s grounded theory secondary literature review. The original Net Lenses research used phenomenography to identify variation in the web search experiences of university students (n = 41), evidencing four categories according to the characteristics of searcher awareness, approach to learning, response to obstacles and search outcomes. This study validated the model and led to an expanded version, Net Lenses 2.0, with five categories of search experience, reflecting the complex information needs of more advanced searchers. This resultant Net Lenses 2.0 model is discussed with its implications for search engine design, for advanced searchers and also for learning-to-search modes, much needed by searchers seeking to develop their abilities. The study’s implications coalesce in a call to action for more inclusive search interface design, and an agenda is put forth for how information researchers, educators and literacy advocates can move forward in their intersecting domains.

Leopoldina online - in site digital content aggregating service

Increasing amount of digitised content is a very promising fact. It promises a wide and easy access to digitised pictures of valuable content and artefacts. But reality shows us it may be contrary – we encounter problems searching digitised content. Metadata aggregating services solve the problem. Splendid example of such is Europeana providing universal access to European digital cultural heritage resources. Locally we may know digital libraries metadata aggregators, such as Polish FBC (Federation of Digital Libraries). Very functional they have limitation, though. They provide access to limited, in terms of formats, sources. Organisations aiming towards digital transformation face a new challenge – combining variety of available digital content formats. An attempt to address problem of access to different digital resources in a Leopoldina online platform. Developed by University of Wroclaw (UWr), in cooperation with PSNC – Polish national meta data aggregator for Europeana, it aims to aggregate and deliver digital resources of various UWr units. In current paper we present goals of the project, short description of different digital resources available, metadata handling and common, universal search engine design.

Eye-Tracking Studies of Web Search Engines: A Systematic Literature Review

This paper analyzes peer-reviewed empirical eye-tracking studies of behavior in web search engines. A framework is created to examine the effectiveness of eye-tracking by drawing on the results of, and discussions concerning previous experiments. Based on a review of 56 papers on eye-tracking for search engines from 2004 to 2019, a 12-element matrix for coding procedure is proposed. Content analysis shows that this matrix contains 12 common parts: search engine; apparatus; participants; interface; results; measures; scenario; tasks; language; presentation, research questions; and findings. The literature review covers results, the contexts of web searches, a description of participants in eye-tracking studies, and the types of studies performed on the search engines. The paper examines the state of current research on the topic and points out gaps in the existing literature. The review indicates that behavior on search engines has changed over the years. Search engines’ interfaces have been improved by adding many new functions and users have moved from desktop searches to mobile searches. The findings of this review provide avenues for further studies as well as for the design of search engines.

Design of optimal search engine using text summarization through artificial intelligence techniques

Natural language processing is the trending topic in the latest research areas, which allows the developers to create the human-computer interactions to come into existence. The natural language processing is an integration of artificial intelligence, computer science and computer linguistics. The research towards natural Language Processing is focused on creating innovations towards creating the devices or machines which operates basing on the single command of a human. It allows various Bot creations to innovate the instructions from the mobile devices to control the physical devices by allowing the speech-tagging. In our paper, we design a search engine which not only displays the data according to user query but also performs the detailed display of the content or topic user is interested for using the summarization concept. We find the designed search engine is having optimal response time for the user queries by analyzing with number of transactions as inputs. Also, the result findings in the performance analysis show that the text summarization method has been an efficient way for improving the response time in the search engine optimizations.

Laypeople's source selection in online health information‐seeking process

AbstractFor laypeople, searching online health information resources can be challenging due to topic complexity and the large number of online sources with differing quality. The goal of this article is to examine, among all the available online sources, which online sources laypeople select to address their health‐related information needs, and whether or how much the severity of a health condition influences their selection. Twenty‐four participants were recruited individually, and each was asked (using a retrieval system called HIS) to search for information regarding a severe health condition and a mild health condition, respectively. The selected online health information sources were automatically captured by the HIS system and classified at both the website and webpage levels. Participants' selection behavior patterns were then plotted across the whole information‐seeking process. Our results demonstrate that laypeople's source selection fluctuates during the health information‐seeking process, and also varies by the severity of health conditions. This study reveals laypeople's real usage of different types of online health information sources, and engenders implications to the design of search engines, as well as the development of health literacy programs.

Design of a vertical search engine for synchrotron data: a big data approach using Hadoop ecosystem

A synchrotron as an experimental physics facility can provide the opportunity of a multi-disciplinary research and collaboration between scientists in various fields of study such as physics, chemistry, etc. During the construction and operation of such facility valuable data regarding the design of the facility, instruments and conducted experiments are published and stored. It takes researchers a long time going through different results from generalized search engines to find their needed scientific information so that the design of a domain specific search engine can help researchers to find their desired information with greater precision. It also provides the opportunity to use the crawled data to create a knowledgebase and also to generate different datasets required by the researchers. There have been several other vertical search engines that are designed for scientific data search such as medical information. In this paper we propose the design of such search engine on top of the Apache Hadoop framework. Usage of Hadoop ecosystem provides the necessary features such as scalability, fault tolerance and availability. It also abstracts the complexities of search engine design by using different open source tools as building blocks, among them Apache Nutch for the crawling block and Apache Solr for indexing and query processing. Our primary results obtained by implementing the proposed method in single node mode, the index of over a hundred thousand pages was created with the average fetch interval of 30 days having 28 segments and approximately 570 MB size. The performance factors such as the usage of available bandwidth and system load were logged using Linux’s sysstat package.

Understanding task preparation and resumption behaviors in cross‐device search

AbstractIt is now common for individuals to have multiple computing devices, such as laptops, smart phones, and tablets. This multidevice environment increases the popularity of cross‐device search activities. Cross‐device search can be seen as a special case of cross‐session search. Previous studies regarded re‐finding behaviors in cross‐session search as task resumption. Based on this, this article proposes considering 2 phases of cross‐device search: task preparation and task resumption and to explore their features by modeling. A within‐subject user experiment was designed to collect data. Four groups of features were captured from specific behaviors of querying, clicking, gazing, and cognition. This article tested 3 machine‐learning methods and found that the C5.0 decision tree performed best. Five features were included in the task preparation behavior model, and 3 in the task resumption behavior model. The difference and relationship between task preparation and task resumption were investigated by comparing their behavioral features. It is concluded that information need remains blurred in task preparation and becomes clear in task resumption. The changing states of information need suggest an exploratory process in cross‐device search. We also identify some implications for search engine designers.

Evaluation of Google question-answering quality

PurposeThe purpose of this paper is to evaluate Google question-answering (QA) quality.Design/methodology/approachGiven the large variety and complexity of Google answer boxes in search result pages, existing evaluation criteria for both search engines and QA systems seemed unsuitable. This study developed an evaluation criteria system for the evaluation of Google QA quality by coding and analyzing search results of questions from a representative question set. The study then evaluated Google’s overall QA quality as well as QA quality across four target types and across six question types, using the newly developed criteria system. ANOVA and Tukey tests were used to compare QA quality among different target types and question types.FindingsIt was found that Google provided significantly higher-quality answers to person-related questions than to thing-related, event-related and organization-related questions. Google also provided significantly higher-quality answers to where- questions than to who-, what- and how-questions. The more specific a question is, the higher the QA quality would be.Research limitations/implicationsSuggestions for both search engine users and designers are presented to help enhance user experience and QA quality.Originality/valueParticularly suitable for search engine QA quality analysis, the newly developed evaluation criteria system expanded and enriched assessment metrics of both search engines and QA systems.

基于Lucene的石墨烯中文文献搜索引擎设计与实现

基于Lucene的石墨烯中文文献搜索引擎设计与实现

Factors influencing search engine usage behavior

We analyzed the effect of individual factors, contextual factors, and perception of search engine advertising on users' search engine usage behavior. The sample comprised 404 Chinese who used search engines in the context of their paid employment. Results showed that (a) perceived search skills and perceived search engine reliance significantly and positively impacted users' general search engine usage, (b) perceived advertising clutter reduced the beneficial effects of perceived search skills on users' general search engine usage, (c) users with higher perceived search engine reliance preferred search engines to other online search methods, and (d) prior negative experience reduced the positive link between perceived search engine reliance and users' specific search engine usage. Our findings suggest that search engine designers and operators should focus on individual and contextual factors influencing search engine usage behavior, and should consider users' perception of advertising on search engine programs.

Improving performance an artificial bee colony optimisation on CloudSim

The major aspiration of this proposed improvement is to identify an accurate data search and to generate data that comes from anywhere. Furthermore, the data itself may be very large to store on a single machine such that the computers are interconnected with each other by the massive internet storage technologies. This approach focuses on design of search engines and its infrastructure grave. Improved micro partitioning is a modularised approach of cloud computing framed to overcome the pitfalls in the traditional search engine and also the manipulation of large information stored. Artificial bee colony (ABC) count is an improvement figuring the watchful scavenging behaviour of honey bees. ABC computation is associated with enhancing the arranging of virtual machine (VM) on Cloud. The essential duty of this work is to analyse VM stack conforming count to reduce the make span of data planning time and that an arranging framework was replicated using CloudSim.