Search

Exact pattern matching algorithms are popular and used widely in several applications, such as molecular biology, text processing, image processing, web search engines, network intrusion detection systems and operating systems. The focus of these algorithms is to achieve time efficiency according to applications but not memory consumption. In this work, we propose a novel idea to achieve both time efficiency and memory consumption by splitting query string for searching in Corpus. For a given text, the proposed algorithm split the query pattern into two equal halves and considers the second (right) half as a query string for searching in Corpus. Once the match is found with second halves, the proposed algorithm applies brute force procedure to find remaining match by referring the location of right half. Experimental results on different S1 Dataset, namely Arabic, English, Chinese, Italian and French text databases show that the proposed algorithm outperforms the existing S1 Algorithm in terms of time efficiency and memory consumption as the length of the query pattern increases. [ABSTRACT FROM AUTHOR]

Presents the author's comments on Internet-based search engine Google. Information on Google Mobile, a new service provided by Google; Demand of the search engine in the U.S.; Goals of Google.

Mobile devices enable people to look for information at the moment when their information needs are triggered. While experiencing complex information needs that require multiple search sessions, users may utilize desktop computers to fulfill information needs started on mobile devices. Under the context of mobile-to-desktop web search, this article analyzes users' behavioral patterns and compares them to the patterns in desktop-to-desktop web search. Then, we examine several approaches of using Mobile Touch Interactions (MTIs) to infer relevant content so that such content can be used for supporting subsequent search queries on desktop computers. The experimental data used in this article was collected through a user study involving 24 participants and six properly designed cross-device web search tasks. Our experimental results show that (1) users'mobile-to-desktop search behaviors do significantly differ from desktop-to-desktop search behaviors in terms of information exploration, sense-making and repeated behaviors. (2) MTIs can be employed to predict the relevance of click-through documents, but applying document-level relevant content based on the predicted relevance does not improve search performance. (3) MTIs can also be used to identify the relevant text chunks at a fine-grained subdocument level. Such relevant information can achieve better search performance than the document-level relevant content. In addition, such subdocument relevant information can be combined with document-level relevance to further improve the search performance. However, the effectiveness of these methods relies on the sufficiency of click-through documents. (4) MTIs can also be obtained from the Search Engine Results Pages (SERPs). The subdocument feedbacks inferred from this set of MTIs even outperform the MTI-based subdocument feedback from the click-through documents. [ABSTRACT FROM AUTHOR]

The evaluation of diversified web search results is a relatively new research topic and is not as well-understood as the time-honoured evaluation methodology of traditional IR based on precision and recall. In diversity evaluation, one topic may have more than one intent, and systems are expected to balance relevance and diversity. The recent NTCIR-9 evaluation workshop launched a new task called INTENT which included a diversified web search subtask that differs from the TREC web diversity task in several aspects: the choice of evaluation metrics, the use of intent popularity and per-intent graded relevance, and the use of topic sets that are twice as large as those of TREC. The objective of this study is to examine whether these differences are useful, using the actual data recently obtained from the NTCIR-9 INTENT task. Our main experimental findings are: (1) The $$\hbox{D}\,\sharp$$ evaluation framework used at NTCIR provides more 'intuitive' and statistically reliable results than Intent-Aware Expected Reciprocal Rank; (2) Utilising both intent popularity and per-intent graded relevance as is done at NTCIR tends to improve discriminative power, particularly for $$\hbox{D}\,\sharp$$-nDCG; and (3) Reducing the topic set size, even by just 10 topics, can affect not only significance testing but also the entire system ranking; when 50 topics are used (as in TREC) instead of 100 (as in NTCIR), the system ranking can be substantially different from the original ranking and the discriminative power can be halved. These results suggest that the directions being explored at NTCIR are valuable. [ABSTRACT FROM AUTHOR]

The Semantic Web, Library Linked (Open) Data and its impact on libraries has recently become a popular and much discussed topic. The aim of this article is to provide a concise overview of these concepts and to point out some use cases for the Austrian context. The first part is dedicated to an introduction to the fundamental concepts of the Semantic Web. Then some recent Linked Library Data-projects and initiatives, especially throughout Germany, their goals and challenges are being presented. Finally some scenarios and chances for the Austrian context, like enhanced search engines or catalogue enrichment, are discussed. [ABSTRACT FROM AUTHOR]

Inverted index data structures are the key to fast text search engines. We first investigate one of the predominant operation on inverted indexes, which asks for intersecting two sorted lists of document IDs of different lengths. We explore compression and performance of different inverted list data structures. In particular, we present Lookup, a new data structure that allows intersection in expected time linear in the smaller list. Based on this result, we present the algorithmic core of a full text data base that allows fast Boolean queries, phrase queries, and document reporting using less space than the input text. The system uses a carefully choreographed combination of classical data compression techniques and inverted-index-based search data structures. Our experiments show that inverted indexes are preferable over purely suffix-array-based techniques for in-memory (English) text search engines. A similar system is now running in practice in each core of the distributed data base engine TREX of SAP. [ABSTRACT FROM AUTHOR]

Although many studies have identified search tactics, few studies have explored tactic transitions. This study investigated the transitions of search tactics during the Web-based search process. Bringing their own 60 search tasks, 31 participants, representing the general public with different demographic characteristics, participated in the study. Data collected from search logs and verbal protocols were analyzed by applying both qualitative and quantitative methods. The findings of this study show that participants exhibited some unique Web search tactics. They overwhelmingly employed accessing and evaluating tactics; they used fewer tactics related to modifying search statements, monitoring the search process, organizing search results, and learning system features. The contributing factors behind applying most and least frequently employed search tactics are in relation to users' efforts, trust in information retrieval (IR) systems, preference, experience, and knowledge as well as limitation of the system design. A matrix of search-tactic transitions was created to show the probabilities of transitions from one tactic to another. By applying fifth-order Markov chain, the results also presented the most common search strategies representing patterns of tactic transition occurring at the beginning, middle, and ending phases within one search session. The results of this study generated detailed and useful guidance for IR system design to support the most frequently applied tactics and transitions, to reduce unnecessary transitions, and support transitions at different phases. [ABSTRACT FROM AUTHOR]

This study investigated a relatively unexamined query type, queries composed of URLs. The extent, variation, and user click-through behavior was examined to determine the intent behind URL queries. The study made use of a search log from which URL queries were identified and selected for both qualitative and quantitative analyses. It was found that URL queries accounted for ∼17% of the sample. There were statistically significant differences between URL queries and non-URL queries in the following attributes: mean query length; mean number of tokens per query; and mean number of clicks per query. Users issuing such queries clicked on fewer result list items higher up the ranking compared to non-URL queries. Classification indicated that nearly 86% of queries were navigational in intent with informational and transactional queries representing about 7% of URL queries each. This is in contrast to past research that suggested that URL queries were 100% navigational. The conclusions of this study are that URL queries are relatively common and that simply returning the page that matches a user's URL is not an optimal strategy. [ABSTRACT FROM AUTHOR]

When crawling resources, for example, number of machines, crawl-time, and so on, are limited, so a crawler has to decide an optimal order in which to crawl and recrawl Web pages. Ideally, crawlers should request only those Web pages that have changed since the last crawl; in practice, a crawler may not know whether a Web page has changed before downloading it. In this article, we identify features of Web pages that are correlated to their change frequency. We design a crawling algorithm that clusters Web pages based on features that correlate to their change frequencies obtained by examining past history. The crawler downloads a sample of Web pages from each cluster, and depending upon whether a significant number of these Web pages have changed in the last crawl cycle, it decides whether to recrawl the entire cluster. To evaluate the performance of our incremental crawler, we develop an evaluation framework that measures which crawling policy results in the best search results for the end-user. We run experiments on a real Web data set of about 300,000 distinct URLs distributed among 210 Web sites. The results demonstrate that the clustering-based sampling algorithm effectively clusters the pages with similar change patterns, and our clustering-based crawling algorithm outperforms existing algorithms in that it can improve the quality of the user experience for those who query the search engine. [ABSTRACT FROM AUTHOR]

This article critically examines four Google search products (Google Advanced Search, Google News Advanced Search, Google Books Advanced Search, and Google Advanced Scholar Search) and shows how each uses metadata to enhance or improve search results. In addition, the article shows how metadata can increase search precision and recall in information discovery systems. From a library perspective, this article analyzes some of the metadata-enabled features of Google's advanced search pages and compares these features to those found in a typical online library catalog. From a serials perspective, Google News Advanced Search demonstrates how Google indexes news websites, sites that are essentially continuing resources. As Google incorporates more and more metadata functionality into its advanced search pages, they increasingly begin to function more like online library catalogs and less like search pages found in a traditional Internet search engine. The simple search box has many limitations, and like libraries, Google is increasingly creating and offering metadata-enabled search features that improve search precision and recall in its products. [ABSTRACT FROM AUTHOR]

Topical query classification, as one step toward understanding users' search intent, is gaining increasing attention in information retrieval. Previous works on this subject primarily focused on enrichment of query features, for example, by augmenting queries with search engine results. In this work, we investigate a completely orthogonal approach—instead of improving feature representation, we aim at drastically increasing the amount of training data. To this end, we propose two semisupervised learning methods that exploit user click-through data. In one approach, we infer class memberships of unlabeled queries from those of labeled ones according to their proximities in a click graph; and then use these automatically labeled queries to train classifiers using query terms as features. In a second approach, click graph learning and query classifier training are conducted jointly with an integrated objective. Our methods are evaluated in two applications, product intent and job intent classification. In both cases, we expand the training data by over two orders of magnitude, leading to significant improvements in classification performance. An additional finding is that with a large amount of training data obtained in this fashion, a classifier based on simple query term features can outperform those using state-of-the-art, augmented features. [ABSTRACT FROM AUTHOR]

PageRank is one of the most important ranking techniques used in today's search engines. A recent very interesting research track focuses on exploiting efficient numerical methods to speed up the computation of PageRank, among which the Arnoldi-type algorithm and the GMRES algorithm are competitive candidates. In essence, the former deals with the PageRank problem from an eigenproblem, while the latter from a linear system, point of view. However, there is little known about the relations between the two approaches for PageRank. In this article, we focus on a theoretical and numerical comparison of the two approaches. Numerical experiments illustrate the effectiveness of our theoretical results. [ABSTRACT FROM AUTHOR]

Study of ranking in Google and Yahoo of big Spanish municipal corporation websites while searching by city tourist brand. Analysis of whether city tourist brands are promoting properly in the internet. Results of the daily ranking monitoring in Google and Yahoo are shown, for the searches made in 2009 by touristic brands of Spanish provincial capitals. The results show that 66% of official websites are situated after number 40 and, also, that in 46% of the searches made by touristic brands of province capital there is an official website within the top 10 positions. Empirical evidence about the weight of backlinks and keywords for SEO has also been detected. [ABSTRACT FROM AUTHOR]

Information retrieval research has demonstrated that system performance does not always correlate positively with user performance, and that users often assign positive evaluation scores to search systems even when they are unable to complete tasks successfully. This research investigated the relationship between objective measures of system performance and users' perceptions of that performance. In this study, subjects evaluated the performance of four search systems whose search results were manipulated systematically to produce different orderings and numbers of relevant documents. Three laboratory studies were conducted with a total of eighty-one subjects. The first two studies investigated the effect of the order of five relevant and five nonrelevant documents in a search results list containing ten results on subjects' evaluations. The third study investigated the effect of varying the number of relevant documents in a search results list containing ten results on subjects' evaluations. Results demonstrate linear relationships between subjects' evaluations and the position of relevant documents in a search results list and the total number of relevant documents retrieved. Of the two, number of relevant documents retrieved was a stronger predictor of subjects' evaluation ratings and resulted in subjects using a greater range of evaluation scores. [ABSTRACT FROM AUTHOR]

A powerful search for ‘curiosity’ or its related topics at the online American Psychological Association PsycNET database will produce comparatively disappointing meagreness of resource. This reflects our scanty knowledge in this field though curiosity is widely recognised as one of the most important factors that contribute to motivation and effectiveness in education and scientific discovery. Lack of understanding in curiosity may be due partly to limitation of research methodology and technology. This paper suggests using Google Trends as a tool for the study of curiosity and its measurement. The observation based on this tool discusses external stimuli, accessible resource and surprisingness that are closely related to curiosity. [ABSTRACT FROM AUTHOR]

Abstract: Currently, there are large collections of drawings from which users can select the desired ones to insert in their documents. However, to locate a particular drawing among thousands is not easy. In our prior work we proposed an approach to index and retrieve vector drawings by content, using topological and geometric information automatically extracted from figures. In this paper, we present a new approach to enrich the topological information by integrating spatial proximity in the topology graph, through the use of weights in adjacency links. Additionally, we developed a web search engine for clip art drawings, where we included the new technique. Experimental evaluation reveals that the use of topological proximity results in better retrieval results than topology alone. However, the increase in precision was not as high as we expected. To understand why, we analyzed sketched queries performed by users in previous experimental sessions and we present here the achieved conclusions. [Copyright &y& Elsevier]

Literature examining information judgments and Internet search behaviors notes a number of major research gaps, including how users actually make these judgments outside of experiments or researcher-defined tasks, and how search behavior is impacted by a user's judgment of online information. Using the medical setting, where doctors face real consequences in applying the information found, we examine how information judgments employed by doctors to mitigate risk impact their cognitive search. Diaries encompassing 444 real clinical information search incidents, combined with semistructured interviews across 35 doctors, were analyzed via thematic analysis. Results show that doctors, though aware of the need for information quality and cognitive authority, rarely make evaluative judgments. This is explained by navigational bias in information searches and via predictive judgments that favor known sites where doctors perceive levels of information quality and cognitive authority. Doctors' mental models of the Internet sites and Web experience relevant to the task type enable these predictive judgments. These results suggest a model connecting online cognitive search and information judgment literatures. Moreover, this implies a need to understand cognitive search through longitudinal- or learning-based views for repeated search tasks, and adaptations to medical practitioner training and tools for online search. [ABSTRACT FROM AUTHOR]

Search effectiveness metrics are used to evaluate the quality of the answer lists returned by search services, usually based on a set of relevance judgments. One plausible way of calculating an effectiveness score for a system run is to compute the inner-product of the run’s relevance vector and a “utility” vector, where the ith element in the utility vector represents the relative benefit obtained by the user of the system if they encounter a relevant document at depth i in the ranking. This paper uses such a framework to examine the user behavior patterns—and hence utility weightings—that can be inferred from a web query log. We describe a process for extrapolating user observations from query log clickthroughs, and employ this user model to measure the quality of effectiveness weighting distributions. Our results show that for measures with static distributions (that is, utility weighting schemes for which the weight vector is independent of the relevance vector), the geometric weighting model employed in the rank-biased precision effectiveness metric offers the closest fit to the user observation model. In addition, using past TREC data as to indicate likelihood of relevance, we also show that the distributions employed in the BPref and MRR metrics are the best fit out of the measures for which static distributions do not exist. [ABSTRACT FROM AUTHOR]

This research investigated several important issues in using implicit feedback techniques to assist searchers with difficulties in formulating effective search strategies. It focused on examining the relationship between types of behavioral evidence that can be captured from Web searches and searchers' interests. A carefully crafted observation study was conducted to capture, examine, and elucidate the analytical processes and work practices of human analysts when they simulated the role of an implicit feedback system by trying to infer searchers' interests from behavioral traces. Findings provided rare insight into the complexities and nuances in using behavioral evidence for implicit feedback and led to the proposal of an implicit feedback model for Web search that bridged previous studies on behavioral evidence and implicit feedback measures. A new level of analysis termed an analytical lens emerged from the data and provides a road map for future research on this topic. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes and evaluates a unified approach to phrasal query suggestions in the context of a high-precision search engine. The search engine performs ranked extended-Boolean searches with the proximity operator near being the default operation. Suggestions are offered to the searcher when the length of the result list falls outside predefined bounds. If the list is too long, the engine specializes the query through the use of super phrases; if the list is too short, the engine generalizes the query through the use of proximal subphrases. We describe methods for generating both types of suggestions and present algorithms for ranking the suggestions. Specifically, we present the problem of counting proximal subphrases for specialization and the problem of counting unordered super phrases for generalization. The uptake of our approach was evaluated by analyzing search log data from before and after the suggestion feature was added to a commercial version of the search engine. We looked at approximately 1.5 million queries and found that, after they were added, suggestions represented nearly 30% of the total queries. Efficacy was evaluated through a controlled study of 24 participants performing nine searches using three different search engines. We found that the engine with phrasal query suggestions had better high-precision recall than both the same search engine without suggestions and a search engine with a similar interface but using an Okapi BM25 ranking algorithm. [Copyright &y& Elsevier]

The article presents a comparison of two implementations that combine taxonomy and keyword approaches which are the Bayesian and the Boolean. The weakness of taxonomy approach is the difficulty of traversing the taxonomy hierarchy to find the subject term, while the weakness of the keyword approach is the lack of the ability to use controlled subject terms. It also mentions that Lucene-based search engine has two modes of searching which are taxonomy subject categories and keywords while the Perfect Search-based search system allows the user to mix and match preferred taxonomy descriptors and keywords. It also presents the outcome of combining the two search implementations provided by the National Information Center for Educational Media (NICEM) database search implementations.

In this research we investigate the effect of search engine brand on the evaluation of searching performance. Our research is motivated by the large amount of search traffic directed to a handful of Web search engines, even though many have similar interfaces and performance. We conducted a laboratory experiment with 32 participants using a 42 factorial design confounded in four blocks to measure the effect of four search engine brands (Google, MSN, Yahoo!, and a locally developed search engine) while controlling for the quality and presentation of search engine results. We found brand indeed played a role in the searching process. Brand effect varied in different domains. Users seemed to place a high degree of trust in major search engine brands; however, they were more engaged in the searching process when using lesser-known search engines. It appears that branding affects overall Web search at four stages: (a) search engine selection, (b) search engine results page evaluation, (c) individual link evaluation, and (d) evaluation of the landing page. We discuss the implications for search engine marketing and the design of empirical studies measuring search engine performance. [ABSTRACT FROM AUTHOR]

The article reports on the launching of the new union catalog known as WorldCat.org platform by Orbis Cascade Alliance in the U.S. As stated, the Summit catalog known as WorldCat and is said to be more dynamic and more obiquitous than any of the old local catalogs. It has also a unique materials and are generally open to search engines, allowing people anywhere on the globe to find and use their content. In addition, it also aggregates supply and demand for books among 30 plus academic libraries which served as a strong example of regional collaboration.

The article discusses a multi-time scale approach to modeling complex information-seeking systems. The time scales considered can be broadly defined as encompassing social, rational, psychological, or biological phenomena. Topics include human-information interaction, Web navigation choices, three theses foundational to information-seeking support systems (ISSS), and information-seeking task classes including sensemaking, exploratory search, and social search. The author's treatment demonstrates that ISSS has evolved beyond simple Internet searching.

This article shows how a fuzzy ontology-based approach can improve semantic documents retrieval. After formally defining a fuzzy ontology and a fuzzy knowledge base, a special type of new fuzzy relationship called (semantic) correlation, which links the concepts or entities in a fuzzy ontology, is discussed. These correlations, first assigned by experts, are updated after querying or when a document has been inserted into a database. Moreover, in order to define a dynamic knowledge of a domain adapting itself to the context, it is shown how to handle a tradeoff between the correct definition of an object, taken in the ontology structure, and the actual meaning assigned by individuals. The notion of a fuzzy concept network is extended, incorporating database objects so that entities and documents can similarly be represented in the network. Information retrieval (IR) algorithm, using an object-fuzzy concept network (O-FCN), is introduced and described. This algorithm allows us to derive a unique path among the entities involved in the query to obtain maxima semantic associations in the knowledge domain. Finally, the study has been validated by querying a database using fuzzy recall, fuzzy precision, and coefficient variant measures in the crisp and fuzzy cases. [ABSTRACT FROM AUTHOR]

As information becomes richer and more complex, alternative information-organization methods are needed to more effectively and efficiently retrieve information from various systems, including the Web. The objective of this study is to explore how a Topic Maps-based ontology approach affects users' searching performance. Forty participants participated in a task-based evaluation where two dependent variables, recall and search time, were measured. The results of this study indicate that a Topic Maps-based ontology information retrieval (TOIR) system has a significant and positive effect on both recall and search time, compared to a thesaurus-based information retrieval (TIR) system. These results suggest that the inclusion of a Topic Maps-based ontology is a beneficial approach to take when designing information retrieval systems. [ABSTRACT FROM AUTHOR]

Place names are often used to describe and to enquire about geographical information. It is common for users to employ vernacular names that have vague spatial extent and which do not correspond to the official and administrative place name terminology recorded within typical gazetteers. There is a need therefore to enrich gazetteers with knowledge of such vague places and hence improve the quality of place name-based information retrieval. Here we describe a method for modelling vague places using knowledge harvested from Web pages. It is found that vague place names are frequently accompanied in text by the names of more precise co-located places that lie within the extent of the target vague place. Density surface modelling of the frequency of co-occurrence of such names provides an effective method of representing the inherent uncertainty of the extent of the vague place while also enabling approximate crisp boundaries to be derived from contours if required. The method is evaluated using both precise and vague places. The use of the resulting approximate boundaries is demonstrated using an experimental geographical search engine. [ABSTRACT FROM AUTHOR]

Retrieval efficiencies of paper-based references in journals and other serials containing 10 scientific names of fossil amphibians were determined for seven major search engines. Retrievals were compared to the number of references obtained covering the period 1895-2006 by a Comprehensive Search. The latter was primarily a traditional library-based search which involved intensive work from 2002-2007. Only a few references originally obtained by search engines were included. Retrieval efficiencies were calculated by comparison to the number obtained through the Comprehensive Search (assumed to be 100%). All percent retrievals were quite low (mean = 11.3%, range = 4.0-22.9%). For all seven search engines combined (duplicate references excluded), the percent retrieval was only 40.7%. There was also retrieval inefficiency and bias of the search engines in: sampling over time, the top five journals containing the most references, the top five most prolific authors, and non-English references. Consequently, all seven search engines were unsatisfactory for retrieval of references for our scientific research. [ABSTRACT FROM AUTHOR]

We consider the following autocompletion search scenario: imagine a user of a search engine typing a query; then with every keystroke display those completions of the last query word that would lead to the best hits, and also display the best such hits. The following problem is at the core of this feature: for a fixed document collection, given a set D of documents, and an alphabetical range W of words, compute the set of all word-in-document pairs (w, d) from the collection such that w ϵ W and d ϵ D. We present a new data structure with the help of which such autocompletion queries can be processed, on the average, in time linear in the input plus output size, independent of the size of the underlying document collection. At the same time, our data structure uses no more space than an inverted index. Actual query processing times on a large test collection correlate almost perfectly with our theoretical bound. [ABSTRACT FROM AUTHOR]

Purpose - This paper aims to report a number of factors that are perceived by web users as influential elements in their search procedure. The paper introduces a new conceptual measure called "web search efficacy" (hereafter WSE) to evaluate the performance of searches mainly based on users' perceptions. Design/methodology/approach - A rich dataset of a wider study was inductively re-explored to identify different categories that are perceived influential by web users on the final outcome of their searches. A selective review of the literature was carried out to discover to what extent previous research supports the findings of the current study. Findings - The analysis of the dataset led to the identification of five categories of influential factors. Within each group different factors have been recognized. Accordingly, the concept of WSE has been introduced. The five "Ss" which determine WSE are searcher's performance, search tool's performance, search strategy, search topic, and search situation. Research limitations/implications - The research body is scattered in different areas and it is difficult to carry out a comprehensive review. The WSE table, which is derived from the empirical data and was supported by previous research, can be employed for further research in various groups of web users. Originality/value - The paper contributes to the area of information seeking on the web by providing researchers with a new conceptual framework to evaluate the efficiency of each search session and identify the underlying factors on the final outcome of web searching. [ABSTRACT FROM AUTHOR]

Abstract: Although search engines are very useful for obtaining information from the World Wide Web, users still have problems obtaining the most relevant information when processing their web queries. Prior research has attempted to use different types of knowledge to improve web querying processing with various levels of success. This research presents a methodology for processing web queries that employs semantic knowledge about different application domains from ResearchCyc, as well as linguistic knowledge from WordNet. An analysis of different queries from different application domains using the semantic and linguistic knowledge illustrates how more relevant results can be obtained. [Copyright &y& Elsevier]

This paper presents the preliminary findings of a co-link analysis of 95 (out of a total of 142) institutions of higher education in eastern and southern Africa. Data was collected using a uniform search strategy, i.e. two search queries were used to extract relevant data from the Yahoo! search engine. Data was captured and stored in Microsoft Excel spreadsheets as 2-dimensional matrices. UCINET version 6 (comprising of several analytic technologies) was primarily used to analyse the data in order to: find out the number of external inlinks for each institution; determine the most colinked institutions; map the colinkages; measure the strengths of colink ties; examine colink relationships; and establish the motivations for colinking. For the purposes of presenting the findings, 40 institutions which recorded a normalised colink count of 1.5 and more were selected. Results indicate that most South African institutions have the highest number of inlink and colink counts. Insitutions belonging to the same geographic region established closer relationships amongst themselves than institutions located in different geographic regions. The institutions which yielded fewer inlinks and higher colink counts produced stronger colink ties. Other findings, conclusions and recommendations are provided. [ABSTRACT FROM AUTHOR]

The huge increment of the available information on the web added to a progressive augment of its use as main source of data has caused that the utilization of information retrieval systems -IRS- in the web (also known as search engines), takes on a higher relevance. Even though these tools carry out an essential work, no less necessary is the task of evaluating them and studying the data that they provide us. This research proposes to analyze the main search engines of the market through a direct comparison of the URLs returned from a heterogeneous set of queries to in- tend to find out the current degree of similarity of the responses. For this purpose we have developed a metasearch engine which will let us make our search experiment and calculate the distances in a quick and interactive way. Finally, we will try to get valid conclusions of the results contrasting them with data from other researches and from our own experiment, what will demonstrate the high degree of divergence in the responses of the IRS. [ABSTRACT FROM AUTHOR]

Link-based ranking algorithms are among the most important techniques to improve web search. In particular, the PageRank algorithm has been successfully used in the Google search engine and has been attracting much attention recently. However, we find that PageRank has a "zero-one gap" problem which, to the best of our knowledge, has not been addressed in any previous work. This problem can be potentially exploited to spam PageRank results and make the state-of-the-art link-based antispamming techniques ineffective. The zero-one gap problem arises as a result of the current ad hoc way of computing transition probabilities in the random surfing model. We therefore propose a novel DirichletRank algorithm which calculates these probabilities using Bayesian estimation with a Dirichlet prior. DirichletRank is a variant of PageRank, but does not have the problem of zero-one gap and can be analytically shown substantially more resistant to some link spams than PageRank. Experiment results on TREC data show that DirichletRank can achieve better retrieval accuracy than PageRank due to its more reasonable allocation of transition probabilities. More importantly, experiments on the TREC dataset and another real web dataset from theWebgraph project show that, compared with the original PageRank, DirichletRank is more stable under link perturbation and is significantly more robust against both manually identified web spams and several simulated link spams. DirichletRank can be computed as efficiently as PageRank, and thus is scalable to large-scale web applications. [ABSTRACT FROM AUTHOR]

This article discusses a novel approach developed for static index pruning that takes into account the locality of occurrences of words in the text. We use this new approach to propose and experiment on simple and effective pruning methods that allow a fast construction of the pruned index. The methods proposed here are especially useful for pruning in environments where the document database changes continuously, such as large-scale web search engines. Extensive experiments are presented showing that the proposed methods can achieve high compression rates while maintaining the quality of results for the most common query types present in modern search engines, namely, conjunctive and phrase queries. In the experiments, our locality-based pruning approach allowed reducing search engine indices to 30% of their original size, with almost no reduction in precision at the top answers. Furthermore, we conclude that even an extremely simple localitybased pruning method can be competitive when compared to complex methods that do not rely on locality information. [ABSTRACT FROM AUTHOR]

In the age of Google, data appear to be plentiful. The secret is knowing where to obtain useful and relevant data, how to access them, and how to draw meaningful conclusions from them. [ABSTRACT FROM AUTHOR]

The aim of this work is an evaluation of Internet and Worldwide web not only as the sources of scientific information but also as a prospective way for considering and solving the interdisciplinary problems. In order to give this evaluation quantitative character, the notions of “Expert Panel” and “Internet Expert Panel” were analyzed using some selected search engines and databases. Using the real example: “surface tension of solids” as a typical case of intersect of many natural sciences, we demonstrated the possibility of creating the virtual “Online Internet Expert Panel” for solving the interdisciplinary problems. [ABSTRACT FROM AUTHOR]

This article reports on the development of a novel method for the analysis of Web logs. The method uses techniques that look for similarities between queries and identify sequences of “query transformation”. It allows sequences of query transformations to be represented as graphical networks, thereby giving a richer view of search behavior than is possible with the usual sequential descriptions. We also perform a basic analysis to study the correlations between observed transformation codes, with results that appear to show evidence of behavior habits. The method was developed using transaction logs from the Excite search engine to provide a tool for an ongoing research project that is endeavoring to develop a greater understanding of Web-based searching by the general public. [ABSTRACT FROM AUTHOR]

Human memory and Internet search engines face a shared computational problem, needing to retrieve stored pieces of information in response to a query. We explored whether they employ similar solutions, testing whether we could predict human performance on a fluency task using PageRank, a component of the Google search engine. In this task, people were shown a letter of the alphabet and asked to name the first word beginning with that letter that came to mind. We show that PageRank, computed on a semantic network constructed from word-association data, outperformed word frequency and the number of words for which a word is named as an associate as a predictor of the words that people produced in this task. We identify two simple process models that could support this apparent correspondence between human memory and Internet search, and relate our results to previous rational models of memory. [ABSTRACT FROM AUTHOR]

In dynamic environments, such as the World Wide Web, a changing document collection, query population, and set of search services demands frequent repetition of search effectiveness (relevance) evaluations. Reconstructing static test collections, such as in TREC, requires considerable human effort, as large collection sizes demand judgments deep into retrieved pools. In practice it is common to perform shallow evaluations over small numbers of live engines (often pairwise, engine A vs. engine B) without system pooling. Although these evaluations are not intended to construct reusable test collections, their utility depends on conclusions generalizing to the query population as a whole. We leverage the bootstrap estimate of the reproducibility probability of hypothesis tests in determining the query sample sizes required to ensure this, finding they are much larger than those required for static collections. We propose a semiautomatic evaluation framework to reduce this effort. We validate this framework against a manual evaluation of the top ten results of tenWeb search engines across 896 queries in navigational and informational tasks. Augmenting manual judgments with pseudo-relevance judgments mined from Web taxonomies reduces both the chances of missing a correct pairwise conclusion, and those of finding an errant conclusion, by approximately 50%. [ABSTRACT FROM AUTHOR]

We explore a syntactic approach to sentence compression in the biomedical domain, grounded in the context of result presentation for related article search in the PubMed search engine. By automatically trimming inessential fragments of article titles, a system can effectively display more results in the same amount of space. Our implemented prototype operates by applying a sequence of syntactic trimming rules over the parse trees of article titles. Two separate studies were conducted using a corpus of manually compressed examples from MEDLINE: an automatic evaluation using BLEU and a summative evaluation involving human assessors. Experiments show that a syntactic approach to sentence compression is effective in the biomedical domain and that the presentation of compressed article titles supports accurate ''interest judgments'', decisions by users as to whether an article is worth examining in more detail. [ABSTRACT FROM AUTHOR]

Although considerable research has been conducted in the field of hierarchical text categorization, little has been done on automatically collecting labeled corpus for building hierarchical taxonomies. In this paper, we propose an automatic method of collecting training samples to build hierarchical taxonomies. In our method, the category node is initially defined by some keywords, the web search engine is then used to construct a small set of labeled documents, and a topic tracking algorithm with keyword-based content normalization is applied to enlarge the training corpus on the basis of the seed documents. We also design a method to check the consistency of the collected corpus. The above steps produce a flat category structure which contains all the categories for building the hierarchical taxonomy. Next, linear discriminant projection approach is utilized to construct more meaningful intermediate levels of hierarchies in the generated flat set of categories. Experimental results show that the training corpus is good enough for statistical classification methods. [ABSTRACT FROM AUTHOR]

The article reports on Google, an internet search engine company with emphasis on the dynamics of its mechanism which allows its users to access information on any topic anywhere and anytime. The author argues that the growing number of information available in the internet render internet users to rely on search engines for their information need. However, the author contends that one of the most significant issue concerning electronic information is its quality rather than its quantity as proven by experts in information technology.

About 20% of the world's population uses the Web, and a large majority thereof uses Web search engines to find information. As a result, many Web researchers are devoting much effort to improving the speed and capability of search technology. [ABSTRACT FROM AUTHOR]

This is the first part of a two-part article that reviews 25 years of published research findings on end-user searching in online information retrieval (IR) systems. In Part 1 (Markey, 2007), the author seeks to answer the following questions: What characterizes the queries that end users submit to online IR systems? What search features do people use? What features would enable them to improve on the retrievals they have in hand? What features are hardly ever used? What do end users do in response to the system's retrievals? Are end users satisfied with their online searches? Summarizing searches of online IR systems by the search features people use everyday makes information retrieval appear to be a very simplistic one-stop event. In Part 2, the author examines current models of the information retrieval process, demonstrating that information retrieval is much more complex and involves changes in cognition, feelings, and/or events during the information seeking process. She poses a host of new research questions that will further our understanding about end-user searching of online IR systems. [ABSTRACT FROM AUTHOR]

Previous studies have examined various aspects of user behavior on the Web, including general information-seeking patterns, search engine use, and revisitation habits. Little research has been conducted to study how users navigate and interact with their Web browser across different information-seeking tasks. We have conducted a field study of 21 participants, in which we logged detailed Web usage and asked participants to provide task categorizations of their Web usage based on the following categories: Fact Finding, Information Gathering, Browsing, and Transactions. We used implicit measures logged during each task session to provide usage measures such as dwell time, number of pages viewed, and the use of specific browser navigation mechanisms. We also report on differences in how participants interacted with their Web browser across the range of information-seeking tasks. Within each type of task, we found several distinguishing characteristics. In particular, Information Gathering tasks were the most complex; participants spent more time completing this task, viewed more pages, and used the Web browser functions most heavily during this task. The results of this analysis have been used to provide implications for future support of information seeking on the Web as well as direction for future research in this area. [ABSTRACT FROM AUTHOR]