Smart search engine using artificial intelligence

Googling for Health Information

Attribute Based Encryption (ABE) allows fine-grained access control along with data confidentiality. Anonymous Attribute Based Encryption (AABE) supports hiding the access control policies within ciphertext so that the receiver's information remains unknown to public. Keyword-based query search over encrypted storage has become a challenging research problem when (i) the search query should not reveal the user identity and (ii) the search procedure should be able to verify whether the access policy associated with cipher document matches with the user's access rights. In this paper, we present an anonymous attribute based searchable encryption (A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> BSE) scheme that allows user to retrieve only a subset of documents, containing the keyword(s) that satisfies the user's search query and for which the user is having sufficient access rights. In A BSE scheme, the search operation does not reveal user's identity. The A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> BSE scheme is secure under the selective ciphertext-policy with chosen plaintext attack and under the selective ciphertext-policy with chosen keyword attack. The A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> BSE scheme is efficient in comparison to existing AABE schemes.

Variation in Results of Three Biology‐Focused Search Engines: A Case Study Using North American Tree Species

Problem statement: Ontological user profile generation was a semantic approach to derive richer concept based user profiles. It depends on the semantic relationship of concepts. This study focuses on ontology to derive concept oriented user profile based on user search queries and clicked documents.This study proposes concept based on topic ontology which derives the concept based user profiles more independently. It was possible to improve the search engine processes more efficiently. Approach: This process consists of individual user’s interests, topical categories of user interests and identifies the relationship among the concepts. The proposed approach was based on topic ontology for concept based user profile generation from search engine logs. Spreading activation algorithm was used to optimize the relevance of search engine results. Topic ontology was constructed to identify the user interest by assigning activation values and explore the topics similarity of user preferences. Results: To update and maintain the interest scores, spreading activation algorithm was proposed. User interest may change over the period of time which was reflected to user profiles. According to profile changes, search engine was personalized by assigning interest scores and weight to the topics. Conclusion: Experiments illustrate the efficacy of proposed approach and with the help of topic ontology user preferences can be identified correctly. It improves the quality of the search engine personalization by identifying the user’s precise needs.

We would like to respond to the article entitled ‘‘Validity and reliability of a systematic database search strategy to identify publications resulting from pharmacy residency research projects.’’ We agree with the authors’ novel approach to confirm publication via a survey and to identify resident publications via a systematic approach to validate the utility of the database. The authors suggest that this approach can be used to compare publication success between residency programs. Additionally, this information can be used for marketing purposes for the individual residency program. One limitation noted by the authors was the use of only PubMed and EMBASE as the search engines to identify resident publications in the study and suggest other search engines, such as International Pharmaceutical Abstracts (IPA) and Google Scholar, may be used as alternatives. We aim to describe the methods and results of our project in which we assessed the utility of 5 different search engines to identify resident publications. Briefly, we identified the publication rate of abstracts presented at the 2003 (n 1⁄4 171), 2005 (n 1⁄4 221), and 2007 (n 1⁄4 273) Great Lakes Pharmacy Resident Conference (GLPRC) was 11.4% (n 1⁄4 76). We used a similar search strategy in Kwak et al to identify resident publication using the search engines (1) Scopus, (2) IPA, and (3) MEDLINE (PubMed). For this project, we used information from the abstracts that were previously confirmed for publications (n 1⁄4 76) to identify the proportion of these publications found in each of the following search engines: (1) Scopus, (2) IPA, (3) MEDLINE (PubMed), (4) EMBASE, and (5) Google Scholar. Two investigators (SMV and PMS) identified published research projects, and any disagreement between investigators was reviewed for consensus. A kappa statistic was calculated to determine interrater reliability for each search engine. The proportion of the published research was compared for each of the 5 search engines. Additionally, all pairs of search engines were assessed to determine the proportion published using combinations of searches. The proportions of published works were identified in the following search engines: Scopus (90.8%), IPA (75%), MEDLINE (PubMed; 80.3%), EMBASE (88.2%), and Google Scholar (90.8%; Table 1). Additionally, a higher proportion of nonpharmacy journals were identified using the search strategy compared to pharmacy journals (Table 1) in each individual search engine. After assessing various combinations, only Scopus plus IPA or Google Scholar plus IPA identified all 76 publications (Table 2). Based on our results, no single search engine was able to identify all publications. All 9 potential pairs were assessed. Only the combination use of either Scopus and IPA or Google Scholar and IPA resulted in identifying all 76 publications. The use of MEDLINE (PubMed) and EMBASE resulted in the poorest combination of search engines, as it only identified 88.2% (n 1⁄4 67) of publications. Kwak et al used the combination of MEDLINE (PubMed) and EMBASE with great success; however, the utility of this combination may differ in a general population of residency programs. Residents of certain geographic areas may submit to journals indexed to different search engines which may have contributed to the differences between our study and Kwak et al, as we assessed abstracts from the Great Lakes region. Additionally, confirmed publications in our previous search strategy used Scopus, IPA, and MEDLINE (PubMed) and not a validated measure which may have influenced our results. In conclusion, we affirm that a validated measure approach, as used by Kwak et al, is the gold standard for confirming the utility of various search engines; however, it may be difficult to replicate in a larger, generalized population of residents. The use of a single search engine is likely not adequate to identify all published residents’ studies. Regional differences may exist in the use of search engines, as the combination of MEDLINE (PubMed) and EMBASE resulted in a high sensitivity in Kwak

When we search information on the Web, we usually enter a query that best represents our information need, and then modify it as the search results are shown. Many thus have studied search queries to understand how users express and find their information need on the Web. For example, studies have shown that query length affects search performance (Belkin et al, 2003). Then, researchers have begun to examine image search queries on the Web. They analyzed search logs from general or commercial search engines or studied specific image database systems based on log analysis (Goodrum & Sprink, 2001; Jorgensen & Jorgensen, 2005; Goodrum, Bejune, & Siochi, 2003; Pu, 2005; Cunningham, Bainbridge, & Masoodian, 2004). While these studies provide basic understanding of image query formulation, further characteristics of user's queries in the interactive searching process have not been explored. This study thus builds on previous studies on image search queries by examining the patterns of query formulation and modification in visual information search. Specifically, first, it investigates the effects of tasks and other contextual factors on query length and query changes; and second, it compares the query length and changes with the user's satisfaction of search results. Participants Twenty-nine college students were recruited for data collection during the spring and summer semesters of 2008, mostly from the Department of Media Studies at the Catholic University of America (CUA). They included 22 from Media Studies and 7 from other fields taking courses in the department; 22 were females and 7 males; and an average age of 21 years. Background information collected were years of computer experience, hours per day using the Web, self-rated online searching expertise, and self-rated digital image searching experience. The seven-point Likert scale was used for self-rating variables. Each participant took part in three different search sessions with his or her own search topic at CUA with the researcher's laptop that had an Internet connection. The university's homepage was set as the default home for the Web browser. Each participant filled out a pre-search questionnaire to collect a search task, topic, self-rated topic familiarity using 7-Likert scale. The participants chose their own preferred browser between Internet Explorer and Firefox. The search session was recorded by Camtasia v5.0 as screen capture. After the session, a post-search questionnaire was given to the participants to assess the level of satisfaction and confidence of their searching using 7-Likert scale. There was no time limit to complete a search session. The participants were free to end a search session at any time. Search Tasks The participants brought their own task and topics to find images. There were three types of search tasks identified: academic task, work-related task, and personal interest (Table 1). Search queries were acquired from screen capture log files. A total of 979 queries were obtained as the entire string of terms submitted by the participants. Table 2 shows a distribution of search queries on a general search engine, a search engine's image search, and other specific sites. Findings showed that Google Images Search was the most frequent source for forming a search query. An average word per query was 3.07 during the first session, 2.95 during the second session, and 2.89 for the last session. This finding of short queries is similar to that of previous studies on Web image queries (Goodrum & Spink, 2001; Jorgensen & Jorgensen, 2005). Analysis of data is in progress. A poster will summarize detail findings and provide answers to the following questions: What are the patterns of query modification? Are there any differences of query length and number of query change based on the task and topic familiarities? Is there any relationship between query length and number of query change and users' satisfaction and confidence with search results? Do different stages of a user's particular task affect query length and number of query change? The author would like to thank OCLC/ALISE for their generous funding for this project.

We propose an augmented Web space and its query language to support geographical querying and sequential plan creation utilizing a digital city that is a city-based information space on the Internet. The augmented Web space involves a new approach to integrate the World Wide Web (WWW) and a geographic information system (GIS). The augmented Web space consists of home pages (HP), hyperlinks, and generic links that represent geographical relations between HPs. The generic links are created dynamically using geographical evaluation functions included in a user's search query each time one is issued. A query also includes a path expression showing how to navigate the HPs, hyperlinks, and generic links. Since the path expression is an extended regular expression, we can describe an arbitrary sequence of users' search actions for navigating the augmented Web space. We have applied the proposed augmented Web space to Digital City Kyoto, a city information service system that is accessed through a 3D walk-through implementation and a map-based interface. Each time a user's query is issued through the 3D and 2D interfaces, Digital City Kyoto creates an augmented Web space, and navigates the Web information space based on the path expression in the query.

This study analyzed the user search queries of an experiment conducted on a full‐text botanical retrieval system. Search terms were extracted and categorized. Term frequencies in each category were calculated. Search terms were compared with target documents. The results reveal preferences for the kinds of information used in search queries, that search queries and document representations use very different format and vocabulary, and that the domain knowledge used in user search queries is lost when such queries are interpreted by the system.

Search engines are turning out to be the greatest tools for gaining valuable data from the internet. Search engines return the search result to the user query which can be an important result or non-important result. Because, the users naturally look only at the first few pages of search results, and search engine ranking can introduce significant bias to their understanding of the internet and their information gain. When a search query is delivered to several search engines, each individual returns a list of pages based on the ranking. Scientists have confirmed that merging search results in a meta-search engine makes a substantial progress in a search result. Current meta-search engines use several search engines for fetching the results but do not emphasize on the semantic relation of the query for finding the best result. In order tod overcome this limitation, a new approach is proposed. The proposed approach can optimize meta-search results using the combination of linear search and semantic search.

Web search engines have become indispensable tools for finding information online effectively. As the range of information, context and users of Internet searches has grown, the relationship between the search query, search interest and user has become more tenuous. Not all users are seeking the same information, even if they use the same query term. Thus, the quality of search results has, at least potentially, been decreasing. Search engines have begun to respond to this problem by trying to personalise search in order to deliver more relevant results to the users. A query is now evaluated in the context of a user’s search history and other data compiled into a personal profile and associated with statistical groups. This, at least, is the promise stated by the search engines themselves. This paper tries to assess the current reality of the personalisation of search results. We analyse the mechanisms of personalisation in the case of Google web search by empirically testing three commonly held assumptions about what personalisation does. To do this, we developed new digital methods which are explained here. The findings suggest that Google personal search does not fully provide the much-touted benefits for its search users. More likely, it seems to serve the interest of advertisers in providing more relevant audiences to them.

Rapid growth of Internet access from mobile users put much emphasis on location specific information. Some of researchers tried to provide prompt information through the tour guide access log. However, evaluating ubiquitous computing systems and their impact on a target population is difficult and costly because it requires analyzing real users in a real context. In this paper, we propose a guide system that combines the positioning technique and location-awareness service to provide the surrounding information for users. The guide system not only accepts the user's search query to find the target but also receives the information from other users who took notes during the tour guide. That is, users can download the related notes as references that were previously recorded by other users. Under such a design concept the contents of our guide system would keep on improving such that an interactive learning environment would be gradually established. Our purpose is thus to develop a museum's service tour guide system and test its effectiveness in a real-world scenario, at reasonable costs, using available technology. The experimental results show that our proposed model is not only particularly suitable to scenic introductions in the museums, but also can be easily extended to outdoor teaching for students.

Since the 90s, keyword-based search engines have been the only option for people to locate relevant web content through a simple query comprising one to a few keywords. These engines, whether free or paid, retained users' search queries and preferences, often to deliver targeted ads. Additionally, user-uploaded articles for plagiarism detection can further be stored as part of service providers' expanding databases for profit. Essentially, users could not search without exposing their queries to these providers. We present a new solution here: a method for searching the internet using a full article as a query without disclosing the content. Our Sapiens Aperio Veritas Engine (S.A.V.E.) uses an encoding scheme and an FM-index search, borrowed from next-generation human genome sequencing. Each word in a user's query is transformed into one of 12 "amino acids" to create a pseudo-biological sequence (PBS) on the user's device. Plagiarism checks are done by users submitting their locally created PBSs to our cloud service. This detects identical content in our database, which includes all English and Chinese Wikipedia articles and Open Access journals up to April 2021. PBSs, longer than 12 "amino acids", show accurate results with less than 0.8% false positives. Performance-wise, S.A.V.E. runs at a similar genome-mapping speed as Bowtie and is >5 orders faster than BLAST. With both standard and private modes, S.A.V.E. offers a revolutionary, privacy-first search and plagiarism check system. We believe this sets an exciting precedent for future search engines prioritizing user confidentiality. S.A.V.E. can be accessed at https://dyn.life.nthu.edu.tw/SAVE/.

В данной статье обсуждается важность адаптации рекламной индустрии к современным условиям информационного общества. Отмечается, что интернет-реклама является одним из наиболее перспективных направлений в этой области, благодаря своей эффективности и возможностям таргетирования. Контекстная реклама, основанная на поисковых запросах пользователей, считается одним из наиболее популярных видов интернет-рекламы, позволяющим быстро привлекать целевую аудиторию и максимально эффективно использовать рекламный бюджет. В последующих абзацах представлены методы интернет-рекламы, которые подтверждают важность ее использования в предпринимательской деятельности. Размещение бесплатных ссылок в веб-каталогах, регистрация на поисковых машинах, размещение ссылок на тематических веб-серверах и других серверах партнеров по бизнесу позволяют увеличить видимость сайта в интернете и привлечь больше потенциальных клиентов. Платные рекламные объявления на популярных веб-ресурсах также представляют собой эффективный способ монетизации сайта. Отмечается, что использование интернет-рекламы является необходимым инструментом в продвижении бизнеса в онлайн-среде и может значительно увеличить успех предпринимательской деятельности. В статье также отмечается, что Интернет-реклама имеет свои негативные стороны, которые необходимо учитывать при разработке рекламной кампании. В частности, пользователи могут блокировать рекламу, что может уменьшить эффективность кампании, а также технический охват не всегда достигает желаемого уровня. Доступ к Интернету может быть платным, что может отпугнуть потенциальных пользователей, а также теневые формы активизации потребительского спроса могут оказывать негативное влияние на рекламу. Тем не менее, при правильной стратегии, Интернет-реклама может быть эффективным инструментом в продвижении бизнеса в онлайн-среде. This article discusses the importance of adapting the advertising industry to the modern conditions of the information society. The author notes that online advertising is one of the most promising areas in this area, due to its effectiveness and targeting capabilities. Contextual advertising based on user search queries is considered one of the most popular types of online advertising, which allows you to quickly attract the target audience and maximize the use of the advertising budget. The following paragraphs present methods of Internet advertising, which confirm the importance of using Internet advertising in business activities. Placing free links in web directories, registering on search engines, placing links on thematic web servers and other servers of business partners can increase the visibility of the site on the Internet and attract more potential customers. Paid advertisements on popular web resources are also an effective way to monetize a site. It is noted that the use of online advertising is a necessary tool in promoting business in an online environment and can significantly increase the success of entrepreneurial activity. The article also notes that online advertising has its negative aspects, which must be taken into account when developing an advertising campaign. In particular, users can block ads, which can reduce the effectiveness of the campaign, as well as technical coverage does not always reach the desired level. Internet access may be paid, which may scare off potential users, as well as shadow forms of activation of consumer demand may have a negative impact on advertising. Nevertheless, with the right strategy, online advertising can be an effective tool in promoting business in an online environment.

The advantages of using machine learning in search are that the search engine can learn and thus lead to more personalized answers, rather than the most popular results. In well-known search engines, such algorithms have been used for a long time and are constantly being improved, but they are focused on the average user and take into account the commercial component. Given this, the search for scientific information is rather difficult. For machine learning in search engines, it is necessary to have a history of user actions that have many variables such as: geolocation, date and time, device type, personalization data, keywords and more. Also a necessary component is to understand the query context and motivation, to understand what the user means. Taking this into account, for the distance learning system, which has become even more relevant in the context of a pandemic, an intelligent search for scientific material has been developed, depending on the interests of the user. The search algorithm forms a group of relevant interests depending on previous search queries and the history of open tabs and forms the search result in scientific journals. Each article contains keywords, so the interest group is formed from the keywords of the articles that interest the user. To check the relevance of user interest groups, the algorithm checks the current search queries for compliance with past interest groups, if the user's interests have changed, forms a new interest group for the user. Forms its knowledge base (article link and short description) depending on what information from the search result was useful to the user, and displays these results to other users on a similar search query. To solve these problems, the existing database of the remote system was expanded.

Vector-based image search, which represents images and search queries as vectors in a high-dimensional space, performs image search by identifying images whose vector representations closely match the vector representation of the query. Search engine optimization has been traditionally achieved by using relevant keywords to increase organic search engine discoverability. For images to be surfaced as a result of a search query, these relevant keywords have been inserted into the page that hosts the image or embedded directly into an image as metadata that can be decoded and indexed by a text-based search engine. However, when a search engine uses vectors to operate, the static nature of an image's vector representation prevents adaptation of content to increase discoverability. In this paper, we present a novel approach to alter an image in a way that enhances its discoverability with a known vector-based search engine while minimizing the visual impact of these alterations for the average user. The alteration is guided by the maximization of intended search queries and the minimization of unwanted search queries.