Search (284 results, page 1 of 15)

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Abstract

Major Web search engines, such as AItaVista, are essential tools in the quest to locate online information. This article reports research that used transaction log analysis to examine the characteristics and changes in AItaVista Web searching that occurred from 1998 to 2002. The research questions we examined are (1) What are the changes in AItaVista Web searching from 1998 to 2002? (2) What are the current characteristics of AItaVista searching, including the duration and frequency of search sessions? (3) What changes in the information needs of AItaVista users occurred between 1998 and 2002? The results of our research show (1) a move toward more interactivity with increases in session and query length, (2) with 70% of session durations at 5 minutes or less, the frequency of interaction is increasing, but it is happening very quickly, and (3) a broadening range of Web searchers' information needs, with the most frequent terms accounting for less than 1% of total term usage. We discuss the implications of these findings for the development of Web search engines.

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Abstract

Chen et alia report on the design of FEATURES, a web search engine with adaptive features based on minimal relevance feedback. Rather than developing user profiles from previous searcher activity either at the server or client location, or updating indexes after search completion, FEATURES allows for index and user characterization files to be updated during query modification on retrieval from a general purpose search engine. Indexing terms relevant to a query are defined as the union of all terms assigned to documents retrieved by the initial search run and are used to build a vector space model on this retrieved set. The top ten weighted terms are presented to the user for a relevant non-relevant choice which is used to modify the term weights. Documents are chosen if their summed term weights are greater than some threshold. A user evaluation of the top ten ranked documents as non-relevant will decrease these term weights and a positive judgement will increase them. A new ordering of the retrieved set will generate new display lists of terms and documents. Precision is improved in a test on Alta Vista searches.

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Abstract

Queries submitted to the Excite search engine were analyzed for subject content based on the cooccurrence of terms within multiterm queries. More than 1000 of the most frequently cooccurring term pairs were categorized into one or more of 30 developed subject areas. Subject area frequencies and their cooccurrences with one another were tallied and analyzed using hierarchical cluster analysis and multidimensional scaling. The cluster analyses revealed several anticipated and a few unanticipated groupings of subjects, resulting in several well-defined high-level clusters of broad subject areas. Multidimensional scaling of subject cooccurrences revealed similar relationships among the different subject categories. Applications that arise from a better understanding of the topics users search and their relationships are discussed

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Abstract

Presents Inquirus, a WWW meta search engine which works by downloading and analysing the individual documents. It makes improvements over existing search engines in a number of areas: more useful document summaries incorporating query term context, identification of both pages which no longer exist and pages which no longer contain the query terms, advanced detection of duplicate pages, improved document ranking using proximity information, dramatically improved precision for certain queries by using specific expressive forms, and quick jump links and highlighting when viewing the full document

Date

1. 8.1996 22:08:06

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Abstract

This paper analyses the development of the knowledge structures provided as aids to users in searching the Internet. Specific focus is given to web directories, thesauri and gateways and portals. The paper assumes that users need to be able to access information in two ways - to locate information on a subject directly in response to a search term and to be able to browse so as to familiarize themselves with a domain or to refine a request. Emphasis is to the browsing aspect. Background and development are addressed. Structures are analyzed, problems are identified, and future directions discussed.

Date

27.12.2008 15:56:22

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Abstract

Summaries of the popular engines extrated from the search sites. Summaries are from: AltaVista, Excite, HotBot, InfoSeek, Ultra, Lycos, OpenText Web Index, and Yahoo. Information covered includes Contents, Searching tips, Results, and Update frequency

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Abstract

In this paper, we present a framework for clustering Web search engine queries whose aim is to identify groups of queries used to search for similar information on the Web. The framework is based on a novel term vector model of queries that integrates user selections and the content of selected documents extracted from the logs of a search engine. The query representation obtained allows us to treat query clustering similarly to standard document clustering. We study the application of the clustering framework to two problems: relevance ranking boosting and query recommendation. Finally, we evaluate with experiments the effectiveness of our approach.

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Content

Key Features - Importantly, the book enables readers to develop strategies which will continue to be useful despite the rapidly-evolving state of the Internet and Internet technologies - it is not about technological `tricks'. - Enables readers to be aware of and compensate for bias and errors which are ubiquitous an the Internet. - Provides contemporary information an the deficiencies in web skills of novice users as well as practical techniques for teaching such users. The Authors Dr Alison Stacey works at the Learning Resource Centre, Cambridge Regional College. Dr Adrian Stacey, formerly based at Cambridge University, is a software programmer. Readership The book is aimed at a wide range of librarians and other information professionals who need to retrieve information from the Internet efficiently, to evaluate their confidence in the information they retrieve and/or to train others to use the Internet. It is primarily aimed at intermediate to advanced users of the Internet. Contents Fundamentals of information retrieval from the Internet - why learn web searching technique; types of information requests; patterns for information retrieval; leveraging the technology: Search term choice: pinpointing information an the web - why choose queries carefully; making search terms work together; how to pick search terms; finding the 'unfindable': Blas an the Internet - importance of bias; sources of bias; usergenerated bias: selecting information with which you already agree; assessing and compensating for bias; case studies: Query reformulation and longer term strategies - how to interact with your search engine; foraging for information; long term information retrieval: using the Internet to find trends; automating searches: how to make your machine do your work: Assessing the quality of results- how to assess and ensure quality: The novice user and teaching internet skills - novice users and their problems with the web; case study: research in a college library; interpreting 'second hand' web information.

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Abstract

The doctoral thesis of Tatiana Gossen formulates criteria and guidelines on how to design the user interfaces of search engines for children. In her work, the author identifies the conceptual challenges based on own and previous user studies and addresses the changing characteristics of the users by providing a means of adaptation. Additionally, a novel type of search result visualisation for children with cartoon style characters is developed taking children's preference for visual information into account.

Content

Inhalt: Acknowledgments; Abstract; Zusammenfassung; Contents; List of Figures; List of Tables; List of Acronyms; Chapter 1 Introduction ; 1.1 Research Questions; 1.2 Thesis Outline; Part I Fundamentals ; Chapter 2 Information Retrieval for Young Users ; 2.1 Basics of Information Retrieval; 2.1.1 Architecture of an IR System; 2.1.2 Relevance Ranking; 2.1.3 Search User Interfaces; 2.1.4 Targeted Search Engines; 2.2 Aspects of Child Development Relevant for Information Retrieval Tasks; 2.2.1 Human Cognitive Development; 2.2.2 Information Processing Theory; 2.2.3 Psychosocial Development 2.3 User Studies and Evaluation2.3.1 Methods in User Studies; 2.3.2 Types of Evaluation; 2.3.3 Evaluation with Children; 2.4 Discussion; Chapter 3 State of the Art ; 3.1 Children's Information-Seeking Behaviour; 3.1.1 Querying Behaviour; 3.1.2 Search Strategy; 3.1.3 Navigation Style; 3.1.4 User Interface; 3.1.5 Relevance Judgement; 3.2 Existing Algorithms and User Interface Concepts for Children; 3.2.1 Query; 3.2.2 Content; 3.2.3 Ranking; 3.2.4 Search Result Visualisation; 3.3 Existing Information Retrieval Systems for Children; 3.3.1 Digital Book Libraries; 3.3.2 Web Search Engines 3.4 Summary and DiscussionPart II Studying Open Issues ; Chapter 4 Usability of Existing Search Engines for Young Users ; 4.1 Assessment Criteria; 4.1.1 Criteria for Matching the Motor Skills; 4.1.2 Criteria for Matching the Cognitive Skills; 4.2 Results; 4.2.1 Conformance with Motor Skills; 4.2.2 Conformance with the Cognitive Skills; 4.2.3 Presentation of Search Results; 4.2.4 Browsing versus Searching; 4.2.5 Navigational Style; 4.3 Summary and Discussion; Chapter 5 Large-scale Analysis of Children's Queries and Search Interactions; 5.1 Dataset; 5.2 Results; 5.3 Summary and Discussion Chapter 6 Differences in Usability and Perception of Targeted Web Search Engines between Children and Adults 6.1 Related Work; 6.2 User Study; 6.3 Study Results; 6.4 Summary and Discussion; Part III Tackling the Challenges ; Chapter 7 Search User Interface Design for Children ; 7.1 Conceptual Challenges and Possible Solutions; 7.2 Knowledge Journey Design; 7.3 Evaluation; 7.3.1 Study Design; 7.3.2 Study Results; 7.4 Voice-Controlled Search: Initial Study; 7.4.1 User Study; 7.5 Summary and Discussion; Chapter 8 Addressing User Diversity ; 8.1 Evolving Search User Interface 8.1.1 Mapping Function8.1.2 Evolving Skills; 8.1.3 Detection of User Abilities; 8.1.4 Design Concepts; 8.2 Adaptation of a Search User Interface towards User Needs; 8.2.1 Design & Implementation; 8.2.2 Search Input; 8.2.3 Result Output; 8.2.4 General Properties; 8.2.5 Configuration and Further Details; 8.3 Evaluation; 8.3.1 Study Design; 8.3.2 Study Results; 8.3.3 Preferred UI Settings; 8.3.4 User satisfaction; 8.4 Knowledge Journey Exhibit; 8.4.1 Hardware; 8.4.2 Frontend; 8.4.3 Backend; 8.5 Summary and Discussion; Chapter 9 Supporting Visual Searchers in Processing Search Results 9.1 Related Work

Date

1. 2.2016 18:25:22

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Abstract

In this paper, we present two ways to improve the precision of HITS-based algorithms onWeb documents. First, by analyzing the limitations of current HITS-based algorithms, we propose a new weighted HITS-based method that assigns appropriate weights to in-links of root documents. Then, we combine content analysis with HITS-based algorithms and study the effects of four representative relevance scoring methods, VSM, Okapi, TLS, and CDR, using a set of broad topic queries. Our experimental results show that our weighted HITS-based method performs significantly better than Bharat's improved HITS algorithm. When we combine our weighted HITS-based method or Bharat's HITS algorithm with any of the four relevance scoring methods, the combined methods are only marginally better than our weighted HITS-based method. Between the four relevance scoring methods, there is no significant quality difference when they are combined with a HITS-based algorithm.

Content

Vgl.: http%3A%2F%2Fdelab.csd.auth.gr%2F~dimitris%2Fcourses%2Fir_spring06%2Fpage_rank_computing%2Fp527-li.pdf. Vgl. auch: http://www2002.org/CDROM/refereed/643/.

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Abstract

The dominant method currently used to improve the quality of Internet search systems is often called "digital democracy." Such an approach implies the utilization of the majority opinion of Internet users to determine the most relevant documents: for example, citation index usage for sorting of search results (google.com) or an enrichment of a query with terms that are asked frequently in relation with the query's theme. "Digital democracy" is an effective instrument in many cases, but it has an unavoidable shortcoming, which is a matter of principle: the average intellectual and cultural level of Internet users is very low- everyone knows what kind of information is dominant in Internet query statistics. Therefore, when one searches the Internet by means of "digital democracy" systems, one gets answers that reflect an underlying assumption that the user's mind potential is very low, and that his cultural interests are not demanding. Thus, it is more correct to use the term "digital ochlocracy" to refer to Internet search systems with "digital democracy." Based an the well-known mathematical mechanism of linear programming, we propose a method to solve the indicated problem.

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Abstract

Content characteristics of a webpage include factors such as keyword position in a webpage, keyword duplication, layout, and their combination. These factors may impact webpage visibility in a search engine. Four hypotheses are presented relating to the impact of selected content characteristics on webpage visibility in search engine results lists. Webpage visibility can be improved by increasing the frequency of keywords in the title, in the full-text and in both the title and full-text.

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Abstract

Web searchers commonly have difficulties crafting queries to fulfill their information needs; even after they are able to craft a query, they often find it challenging to evaluate the results of their Web searches. Sources of these problems include the lack of support for constructing and refining queries, and the static nature of the list-based representations of Web search results. WordBars has been developed to assist users in their Web search and exploration tasks. This system provides a visual representation of the frequencies of the terms found in the first 100 document surrogates returned from an initial query, in the form of a histogram. Exploration of the search results is supported through term selection in the histogram, resulting in a re-sorting of the search results based on the use of the selected terms in the document surrogates. Terms from the histogram can be easily added or removed from the query, generating a new set of search results. Examples illustrate how WordBars can provide valuable support for query refinement and search results exploration, both when vague and specific initial queries are provided. User evaluations with both expert and intermediate Web searchers illustrate the benefits of the interactive exploration features of WordBars in terms of effectiveness as well as subjective measures. Although differences were found in the demographics of these two user groups, both were able to benefit from the features of WordBars.

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Abstract

Peer-to-Peer (P2P) networks provide a new distributed computing paradigm an the Internet for file sharing. The decentralized nature of P2P networks fosters cooperative and non-cooperative behaviors in sharing resources. Searching is a major component of P2P file sharing. Several studies have been reported an the nature of queries of World Wide Web (WWW) search engines, but studies an queries of P2P networks have not been reported yet. In this report, we present our study an the Gnutella network, a decentralized and unstructured P2P network. We found that the majority of Gnutella users are located in the United States. Most queries are repeated. This may be because the hosts of the target files connect or disconnect from the network any time, so clients resubmit their queries. Queries are also forwarded from peers to peers. Findings are compared with the data from two other studies of Web queries. The length of queries in the Gnutella network is longer than those reported in the studies of WWW search engines. Queries with the highest frequency are mostly related to the names of movies, songs, artists, singers, and directors. Terms with the highest frequency are related to file formats, entertainment, and sexuality. This study is important for the future design of applications, architecture, and services of P2P networks.

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Abstract

Adapting the consumer behavior selectivity model to the Web environment, this paper's key contribution is the introduction of a self-concept orientation model of Web information seeking. This model, which addresses gender, effort, and information content factors, questions the commonly assumed equivalence of sex and gender by specifying the measurement of gender-related selfconcept traits known as self- and other-orientation. Regression analyses identified associations between self-orientation, other-orientation, and self-reported search frequencies for content with identical subject domain (e.g., medical information, government information) and differing relevance (i.e., important to the individual personally versus important to someone close to him or her). Self- and other-orientation interacted such that when individuals were highly self-oriented, their frequency of search for both self- and other-relevant information depended on their level of other-orientation. Specifically, high-self/high-other individuals, with a comprehensive processing strategy, searched most often, whereas high-self/low-other respondents, with an effort minimization strategy, reported the lowest search frequencies. This interaction pattern was even more pronounced for other-relevant information seeking. We found no sex differences in search frequency for either self-relevant or other-relevant information.

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Abstract

Ethical aspects of the employment of Web crawlers for information science research and other contexts are reviewed. The difference between legal and ethical uses of communications technologies is emphasized as well as the changing boundary between ethical and unethical conduct. A review of the potential impacts on Web site owners is used to underpin a new framework for ethical crawling, and it is argued that delicate human judgment is required for each individual case, with verdicts likely to change over time. Decisions can be based upon an approximate cost-benefit analysis, but it is crucial that crawler owners find out about the technological issues affecting the owners of the sites being crawled in order to produce an informed assessment.

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Abstract

Today, when searching for information on the WWW, one usually performs a query through a term-based search engine. These engines return, as the query's result, a list of Web pages whose contents matches the query. For broad-topic queries, such searches often result in a huge set of retrieved documents, many of which are irrelevant to the user. However, much information is contained in the link-structure of the WWW. Information such as which pages are linked to others can be used to augment search algorithms. In this context, Jon Kleinberg introduced the notion of two distinct types of Web pages: hubs and authorities. Kleinberg argued that hubs and authorities exhibit a mutually reinforcing relationship: a good hub will point to many authorities, and a good authority will be pointed at by many hubs. In light of this, he dervised an algoirthm aimed at finding authoritative pages. We present SALSA, a new stochastic approach for link-structure analysis, which examines random walks on graphs derived from the link-structure. We show that both SALSA and Kleinberg's Mutual Reinforcement approach employ the same metaalgorithm. We then prove that SALSA is quivalent to a weighted in degree analysis of the link-sturcutre of WWW subgraphs, making it computationally more efficient than the Mutual reinforcement approach. We compare that results of applying SALSA to the results derived through Kleinberg's approach. These comparisions reveal a topological Phenomenon called the TKC effectwhich, in certain cases, prevents the Mutual reinforcement approach from identifying meaningful authorities.

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Abstract

Search Engines have become an integral part of our day to day life. Our reliance on search engines increases with every passing day. With the amount of data available on Internet increasing exponentially, it becomes important to develop new methods and tools that help to return results relevant to the queries and reduce the time spent on searching. The results should be diverse but at the same time should return results focused on the queries asked. Relation Based Page Rank [4] algorithms are considered to be the next frontier in improvement of Semantic Web Search. The probability of finding relevance in the search results as posited by the user while entering the query is used to measure the relevance. However, its application is limited by the complexity of determining relation between the terms and assigning explicit meaning to each term. Trust Rank is one of the most widely used ranking algorithms for semantic web search. Few other ranking algorithms like HITS algorithm, PageRank algorithm are also used for Semantic Web Searching. In this paper, we will provide a comparison of few ranking approaches.

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Abstract

Purpose - The purpose of the study was to compare an internet search engine, Google, with appropriate library databases and systems, in order to assess the relative value, strengths and weaknesses of the two sorts of system. Design/methodology/approach - A case study approach was used, with detailed analysis and failure checking of results. The performance of the two systems was assessed in terms of coverage, unique records, precision, and quality and accessibility of results. A novel form of relevance assessment, based on the work of Saracevic and others was devised. Findings - Google is superior for coverage and accessibility. Library systems are superior for quality of results. Precision is similar for both systems. Good coverage requires use of both, as both have many unique items. Improving the skills of the searcher is likely to give better results from the library systems, but not from Google. Research limitations/implications - Only four case studies were included. These were limited to the kind of queries likely to be searched by university students. Library resources were limited to those in two UK academic libraries. Only the basic Google web search functionality was used, and only the top ten records examined. Practical implications - The results offer guidance for those providing support and training for use of these retrieval systems, and also provide evidence for debates on the "Google phenomenon". Originality/value - This is one of the few studies which provide evidence on the relative performance of internet search engines and library databases, and the only one to conduct such in-depth case studies. The method for the assessment of relevance is novel.

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Abstract

Measuring the information retrieval effectiveness of World Wide Web search engines is costly because of human relevance judgments involved. However, both for business enterprises and people it is important to know the most effective Web search engines, since such search engines help their users find higher number of relevant Web pages with less effort. Furthermore, this information can be used for several practical purposes. In this study we introduce automatic Web search engine evaluation method as an efficient and effective assessment tool of such systems. The experiments based on eight Web search engines, 25 queries, and binary user relevance judgments show that our method provides results consistent with human-based evaluations. It is shown that the observed consistencies are statistically significant. This indicates that the new method can be successfully used in the evaluation of Web search engines.