Discourse marker based topic identification and search results refining

Googling for Health Information

Search engines are an integral part of web information gathering and retrieval process, but they are significantly dependent upon the words or word phrases input by the end user. The search engine usually contributes no additional semantic data toward the information acquisition process. This paper presents an intelligent search agent "Web host access tool" (WHAT) based on support vector machines (SVM), which introduces the notion of queries conducted within a specific contextual meaning. Given a context and associated keywords that personalize the search history and preferences of the user, WHAT performs more intelligent resource filtering than conventional search engines, providing more relevant results while filtering the irrelevant references. Search results obtained in the form of text from different search engines are processed by the SVM based word classifier that arrange the results obtained according to user preference obtained from previous search processes. The text materials are processed by Latent Semantic Indexing (LSI) for creating a document matrix that gives the probability of a word occurrence in a specified context. For simplicity, this paper considers 5 different contexts: business, education, entertainment, news and information and tourism. The LSI coefficients were used by SVM to yield confidence levels for each search result and according to that the results were sorted and presented to the end user. As an alternative, least square support vector machines (LS-SVM) are also studied in this paper. The system is updated by the user that provides feedback regarding the search relevance, based on which the LSI coefficients are updated to ensure increased relevance in future searches. Three different types of kernel function were considered in this paper -Linear. Radial basis function (RBF) and polynomial kernel. Results claim that Linear SVM perform better than the others, not only in terms of classification accuracy but also in terms of training speed.

This chapter aims to master web mining and Information Retrieval (IR) in the digital age, thus describing the overviews of web mining and web usage mining; the significance of web mining in the digital age; the overview of IR; the concept of Collaborative Information Retrieval (CIR); the evaluation of IR systems; and the significance of IR in the digital age. Web mining can contribute to the increase in profits by selling more products and by minimizing costs. Web mining is the application of data mining techniques to discover the interesting patterns from web data in order to better serve the needs of web-based multifaceted applications. Mining web data can improve the personalization, create the selling opportunities, and lead to more profitable relationships with customers in global business. Web mining techniques can be applied with the effective analysis of the clearly understood business needs and requirements. Web mining builds the detailed customer profiles based on the transactional data. Web mining is used to create the personalized search engines which can recognize the individuals' search queries by analyzing and profiling the web user's search behavior. IR is the process of obtaining relevant information from a collection of informational resources. IR has considerably changed with the expansion of the Internet and the advent of modern and inexpensive graphical user interfaces and mass storage devices. The effective IR system, including an active indexing system, not only decreases the chances that information will be misfiled but also expedites the retrieval of information. Regarding IR utilization, the resulting time-saving benefit increases office efficiency and productivity while decreasing stress and anxiety. Most IR systems provide the advanced searching capabilities that allow users to create the sophisticated queries. The chapter argues that applying web mining and IR has the potential to enhance organizational performance and reach strategic goals in the digital age.

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

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.

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.

Advanced Natural Language Processing Tools for Web Information Retrieval, Content Analysis, and Synthesis. The goal of this SBIR was to implement and evaluate several advanced Natural Language Processing (NLP) tools and techniques to enhance the precision and relevance of search results by analyzing and augmenting search queries and by helping to organize the search output obtained from heterogeneous databases and web pages containing textual information of interest to DOE and the scientific-technical user communities in general. The SBIR investigated 1) the incorporation of spelling checkers in search applications, 2) identification of significant phrases and concepts using a combination of linguistic and statistical techniques, and 3) enhancement of the query interface and search retrieval results through the use of semantic resources, such as thesauri. A search program with a flexible query interface was developed to search reference databases with the objective of enhancing search results from web queries or queries of specialized search systems such as DOE's Information Bridge. The DOE ETDE/INIS Joint Thesaurus was processed to create a searchable database. Term frequencies and term co-occurrences were used to enhance the web information retrieval by providing algorithmically-derived objective criteria to organize relevant documents into clusters containing significant terms. A thesaurus provides an authoritative overview and classification of a field of knowledge. By organizing the results of a search using the thesaurus terminology, the output is more meaningful than when the results are just organized based on the terms that co-occur in the retrieved documents, some of which may not be significant. An attempt was made to take advantage of the hierarchy provided by broader and narrower terms, as well as other field-specific information in the thesauri. The search program uses linguistic morphological routines to find relevant entries regardless of whether terms are stored in singular or plural form. Implementation of additional inflectional morphology processes for verbs can enhance retrieval further, but this has to be balanced by the possibility of broadening the results too much. In addition to the DOE energy thesaurus, other sources of specialized organized knowledge such as the Medical Subject Headings (MeSH), the Unified Medical Language System (UMLS), and Wikipedia were investigated. The supporting role of the NLP thesaurus search program was enhanced by incorporating spelling aid and a part-of-speech tagger to cope with misspellings in the queries and to determine the grammatical roles of the query words and identify nouns for special processing. To improve precision, multiple modes of searching were implemented including Boolean operators, and field-specific searches. Programs to convert a thesaurus or reference file into searchable support files can be deployed easily, and the resulting files are immediately searchable to produce relevance-ranked results with builtin spelling aid, morphological processing, and advanced search logic. Demonstration systems were built for several databases, including the DOE energy thesaurus.

Objectives: To find a better indexing method of search engine for better information retrieval. Methods/Statistical Analysis: Indexing technique is very important part of any search engine or information retrieval system. There are many indexing techniques proposed earlier but they are not accurately retrieving information from the database. In this paper we are trying to propose a new indexing technique for maximizing attempt to get proper information according to search query by the information retrieval system. There are many different parts to a search engine index, such as design factors and data structures. When a search engine index is being built, there are also many different types of data structures to choose from. Some well known data structures are suffix tree, tree, inverted index, Citation index, Ngram index, Term document matrix, which are all used for different type of index designing for search engine. In this paper combination of different existing indexing methods has been used to form a new indexing technique. Findings: The experimental results described in the paper show that the accuracy of search results using S-N Indexing methods is 5% better than existing search engine indexing techniques. Application/Improvements: In this paper we are trying to design an indexing technique which is combination of Ngram index and suffix tree index for better information retrieval process by search engines. So, proposed method of indexing technique can improve searching results with more accurate and more related way. Keywords: Concept based, Concept Based Index, Improved Search Index, New Index, Search Engine, Search Engine Index

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.

The birth of the internet in the 1990‟s did not only change the way of how people communicate but also of how they access information. The amount of information that the internet offers is enormous, it grows by the day and can be retrieved at any time – only technical issues may interrupt its constant availability. That is why the World Wide Web is nowadays the first port for information retrieval. Information retrieval tools are indispensible in this environment. They filter the information mass and allow users to locate the desired information.Search engines are one of the most popular web services.Most of what is known about user search engine behavior comes from query log analysis and clickthrough analysis. Only few studies have so far examined search engine behavior and user satisfaction from a mere user perspective.The number still shrinks when it comes to narrowing down the research focus on users of a specific country or language community. This study investigates user search engine behavior and satisfaction from the perspective of Chinese-speaking users in Malaysia. A survey made among 62 Malaysian Chinese users assesses search engine usage frequency, preferred search engines, and user priorities for search engine selection. It also assesses linguistic aspects such as preferred search language and search engine interface language. The study evaluates the users‟ satisfaction with the search results in general and the search results on the first page, with the search results of English and Chinese search queries, andthe necessity for query rephrasing. Where possible, the study compares the results with existing studies conducted in Malaysia.

The purpose of the Information Retrieval (IR) is to find a set of documents that are relevant for a specific information need of a user. Traditional Information Retrieval model commonly used in commercial search engine is based on keyword indexing system and Boolean logic queries. One big drawback of traditional information retrieval is that they typically retrieve information without an explicitly defined domain of interest to the users so that a lot of no relevance information returns to users, which burden the user to pick up useful answer from these no relevance results. In order to tackle this issue, many semantic web information retrieval models have been proposed recently. The main advantage of Semantic Web is to enhance search mechanisms with the use of Ontology’s mechanisms. In this paper, we present our approach to personalize web search engine based on ontology. In addition, key techniques are also discussed in our paper. Compared to previous research, our works concentrate on the semantic similarity and the whole process including query submission and information annotation.

The purpose of this research is to discuss some challenges of information retrieval, especially Web information retrieval, in digital ecosystems from a userpsilas perspective. As a dominant search tool, search engines usually return millions of search results in a long flat list in which many or even most of the results can be irrelevant. The long flat list conveys nothing about knowledge structure related to the retrieved results and personal search preferences and interests are not explored. Although some search engines try to cluster the Web results, the automatically formed titles and knowledge hierarchy is prone to mismatching the searcherpsilas human mental model. In digital ecosystems, while many different search tools are available, they are not integrated. To address these issues, a search framework which combines categorization, clustering, ontology, and personalization is proposed, and thus the quality of search results in digital ecosystems is expected to be boosted.

Web search results enjoy an increasing importance in our daily lives. But what can be said about their quality, especially when querying a controversial issue? The traditional information retrieval metrics of precision and recall do not provide much insight in the case of web information retrieval. In this paper we examine new ways of evaluating quality in search results: coverage and independence. We give examples on how these new metrics can be calculated and what their values reveal regarding the two major search engines, Google and Yahoo. We have found evidence of low coverage for commercial and medical controversial queries, and high coverage for a political query that is highly contested. Given the fact that search engines are unwilling to tune their search results manually, except in a few cases that have become the source of bad publicity, low coverage and independence reveal the efforts of dedicated groups to manipulate the search results.

Web information retrieval is significantly more challenging than traditional well-controlled, small document collection information retrieval. One main difference between traditional information retrieval and Web information retrieval is the Web's hyperlink structure. This structure has been exploited by several of today's leading Web search engines, particularly Google and Teoma. In this survey paper, we focus on Web information retrieval methods that use eigenvector computations, presenting the three popular methods of HITS, PageRank, and SALSA.

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.