Intelligent search for scientific information in e-learning systems

Googling for Health Information

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

Search engines are web-based systems used for retrieving information from the Internet. They have economic power because of their positioning between information providers and information seekers. Search engines can influence the flow of information – possible business transactions – by the way information is indexed, stored, and portrayed as search results. If a search engine provides good coverage of website content from one group of information providers (grouped by country or language) to the detriment of another group, it will have economic implications for both groups. It is known that certain developed countries and the language(s) of these countries, have better coverage than other developed countries and their languages. This study investigates for the first time the website coverage of a developing country, South Africa, and one of its indigenous languages, Afrikaans. What does the existence of search engine country bias and/or linguistic bias imply for developing countries such as South Africa? South African information providers would have reason for concern if information seekers’ attention were continually routed abroad by biased search engines. South African information seekers would also be done an injustice if they cannot find local web content (or content in their indigenous languages) due to poor search engine coverage. Biased search results guide them away from cheaper, more convenient local information due to poor coverage of local content. Search engines are negatively impacted in turn, when users become tired of the poor local coverage and unwanted international search results and turn to other tools, such as local search engines, for information retrieval. How severe are the effects of search engine bias on developing countries such as South Africa? The body of knowledge discussing search engine bias is very limited, and this study is motivated by stating that, given the possibility of negative economic implications of such bias for developing economies, more research on this topic is justified and urgently needed. The study revealed that Western website content enjoys better coverage than South African website content. After further investigation it was also found that English website content enjoys better coverage than website content in Afrikaans. There is, therefore, a proven search engine bias in favour of Western developed countries and the English language. Website visibility is also studied as a possible cause of search engine bias. It would seem plausible that a relationship may exist between the coverage of websites by search engines and how visible these websites are to a search engine’s crawlers. For the determination of website visibility, the number of in links towards each sample domain was determined. This study shows that the higher visibility of websites from developed countries is a cause of search engine bias in favour of these websites. With website visibility proven as a cause of bias, the study indicates that South Africa, and possibly other developing countries, is lagging far behind in the race to create highly visible websites surrounded by well-covered hyperlink structures – the kind of websites most likely to be covered by search engine crawlers. It is the responsibilty of information providers from developing countries to create more hyperlinks between websites from their countries, as well as creating visible website content in their indigenous languages. Search engine coverage bias has negative economic implications for developing countries such as South Africa. This paper investigates the severity of country related coverage bias against websites from the South African domain(s) and the correlation between website coverage and website visibility. Other possible causes of coverage bias found in literature include indexing algorithms, ranking algorithms and lexicons struggling with non-English content. Information providers’ lack of knowledge about website coverage and search engine tools is discussed as another possible cause of country bias.

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.

Most research oriented search queries consist of multiple topics belonging to one or more domains of knowledge. The objective of such search queries is to find the relationship or impact that each topic has on the other(s). Though web search engines provide easy means to retrieve information off the web, search engines are mainly key word oriented and do not consider the different topics and relationships between such topics present in the results. This project presents a software solution, `SearchDroid' that acts as an intermediate user between a search engine and the end user, refining search results based on different topics identified in the search query and their presence and relationships depicted in search results. A discourse parsing approach has been used to build discourse structures that represent the rhetorical relations of text in search results; this is used to re-rank results based on topics identified in the search query. The project combines linguistics research under discourse parsing with web information retrieval techniques. The lack of literature combining discourse parsing techniques with web information retrieval has been compensated for by introducing a fresh algorithmic approach. An abstract information retrieval mechanism has been created with the use of discourse parsing techniques, and can be integrated into any web information retrieval approach. The system was evaluated by Linguistic Experts, Technical Experts and End users. All experts agreed that a discourse parsing approach was suitable for addressing the problem at hand and that the project had high research value in the aspect of linking linguistics research with web information retrieval research. Hands-on testing of the system by End Users produced high user acceptance of the proposed 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.

Nowadays, the well-known search engines, such as Google, Yahoo, MSN, etc, have provided the users with good search results based on special search strategies. However there still exist some problems unsolved for traditional search engines, including: (1) the gap between userpsilas intention and searched results is not easy to narrow down under the global search space, and (2) userpsilas interested pages hidden in the local website are not associated with the search results. To deal with such problems, in this paper, we propose a novel approach for personalized page ranking and recommendation by integrating association mining and PageRank so as to meet userpsilas search goals. Moreover, by mining the userspsila browsing behaviors, we can successfully bridge the gap between global search results and local preferences. The effectiveness of our proposed approach was verified through experimental evaluations.

Over the past decade, the volume of information available digitally over the Internet has grown enormously. Technical developments in the area of search, such as Google's Page Rank algorithm, have proved so good at serving relevant results that Internet search has become integrated into daily human activity. One can endlessly explore topics of interest simply by querying and reading through the resulting links. Yet, although search engines are well known for providing relevant results based on users' queries, users do not always receive the results they are looking for. Google's Director of Research describes clickstream evidence of frustrated users repeatedly reformulating queries and searching through page after page of results. Given the general quality of search engine results, one must consider the possibility that the frustrated user's query is not effective; that is, it does not describe the essence of the user's interest. Indeed, extensive research into human search behavior has found that humans are not very effective at formulating good search queries that describe what they are interested in. Ideally, the user should simply point to a portion of text that sparked the user's interest, and a system should automatically formulate a search query that captures the essence of the text. In this paper, we describe an implemented system that provides this capability. We first describe how our work differs from existing work in automatic query formulation, and propose a new method for improved quantification of the relevance of candidate search terms drawn from input text using phrase‐level analysis. We then propose an implementable method designed to provide relevant queries based on a user's text input. We demonstrate the quality of our results and performance of our system through experimental studies. Our results demonstrate that our system produces relevant search terms with roughly two‐thirds precision and recall compared to search terms selected by experts, and that typical users find significantly more relevant results (31% more relevant) more quickly (64% faster) using our system than self‐formulated search queries. Further, we show that our implementation can scale to request loads of up to 10 requests per second within current online responsiveness expectations (<2‐second response times at the highest loads tested).

Due to the increasing information on the Web it becomes very difficult for a single search engine to provide comprehensive Web coverage, thus increasing the difficulty for an average user to search information on the web. We have proposed an idea for smart Meta search engine which will have a more comprehensive coverage of the web by targeting different search engines for different search categories. The user's search query will be modified using a knowledge base and appropriate search engine will be selected. The results returned from these search engines will be merged and ranked. These results will be classified into various categories using a classification algorithm and then the result will be displayed sorted into categories. It will be beneficial to the user who has an obscure idea about the information he/she wanted to search.

CYBER NEWS

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.

Web searching has become a necessary activity for most Internet users. People can reach a specific website or collect desired information by using search engines. Nevertheless, when a user is unfamiliar with his or her search target, e.g., learning a previously unknown topic, browsing and understanding the web pages in the corresponding search result requires extra effort. In such an informational web search, the user accumulates information cues by reading web pages to approach the search target. For example, a user can gradually understand ”cloud computing” after obtaining some information about relevant terms, e.g., distributed computing, Google App Engine, and Amazon EC2. In this work, we propose an ”immediacy of interest” indicator, measured in terms of information freshness, to evaluate the possible user interest in a web page contained in a search result. Moreover, to address relevance in search sessions, the topic map is also utilized in our proposed scheme. Empirical studies show that informational web searching tasks can be completed efficiently and effectively with our approach.

The present study demonstrates how the emotional content of search terms and their eventual results affects the breadth of a users’ search for information. We observed the quantity of results selected by users. In a random sample of queries from the Microsoft LiveSearch search engine, 7,021 queries were evaluated using a dictionary with valence and arousal ratings. The number of search results selected was regressed on the valence and arousal of the search terms. We additionally observed users’ selection of results based on the position in the search results. Using the same sample, result placement was regressed on the valence and arousal level of the search terms. Results from quantity of search results selected shows that negative search terms result in an overall larger number of selections made than positive search terms. For position-based selections, we found that the selection of the first result is affected by an interaction of valence and arousal. Specifically, users were unaffected by the arousal level of negative search terms, but appeared to be more likely to search deeper on the page when they searched for less arousing positive information. These results suggest that the emotional content associated with a search query may lead users to be more or less discriminating in their acceptance of information and may influence the impact of placement on result selection.

Search queries can be used to infer preferences and interests of users. While search engines use this information for, among others, targeted advertising and personalization, these tasks can violate user’s privacy. In 2006, after AOL disclosed the search queries of 650,000 users and some of them were re-identified, many Privacy Enhancement Technologies (PETs) have sought to solve this problem. The Dissociating Privacy Agent (DisPA), is a browser extension that acts as a proxy between the user and the search engine and semantically dissociates queries on real time. We show that DisPA increases the privacy of the user and hinders re-identification. We also propose an algorithm to measure and evaluate the privacy properties offered by DisPA.

I. Introduction Studies reveal that often group members collaborate when searching for information even if they were not explicitly asked to collaborate [1]. The activity that involves a group of people engaging in a common information seeking task is called Collaborative Information Seeking (CIS). Over the past few years, CIS research has focused on providing solutions and frameworks to support the process [2]. However, work in this field to date has always assumed that information seekers engaged in CIS activity are using the same access modality, the visual modality. The attention on this modality has failed to address the needs of users who employ different access modalities such as haptic and/or audio. Visual Impaired (VI) employees in a workplace may often have to collaborate with their sighted team members when searching the web. Given that the VI individual's search behaviour is challenged by poor web design and the shortcomings of current assistive technology [3][4]; collaboratively engaging in w...