Search (190 results, page 1 of 10)

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  1. Dunning, A.: Do we still need search engines? (1999) 0.26 
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    Source
    Ariadne. 1999, no.22
  2. Overton, R.: Search engines get faster and faster, but not always better (1996) 0.16 
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    Abstract
    Good article listing the pros and cons of the most popular search engines. Grades search engines and recommends thoch ones to use and not to use. Also provides good table of features
  3. Page, A.: ¬The search is over : the search-engines secrets of the pros (1996) 0.15 
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    Abstract
    Covers 8 of the most popular search engines. Gives a summary of each and has a nice table of features that also briefly lists the pros and cons. Includes a short explanation of Boolean operators too
  4. Stanley, T.: Alta Vista vs. Lycos (1996) 0.13 
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    Abstract
    Very good review of what many people think are the top 2 rated search engines. has extensive narrative and several tables
    Footnote
    Auch unter: http://ukoln.bath.ac.uk/ariadne/issue2/engines/
  5. Bradley, P.: ¬The relevance of underpants to searching the Web (2000) 0.13 
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    Footnote
    Auch unter: http://www.ariadne.ac.uk/issue24/search-engines
  6. Bedathur, S.; Narang, A.: Mind your language : effects of spoken query formulation on retrieval effectiveness (2013) 0.13 
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    Abstract
    Voice search is becoming a popular mode for interacting with search engines. As a result, research has gone into building better voice transcription engines, interfaces, and search engines that better handle inherent verbosity of queries. However, when one considers its use by non- native speakers of English, another aspect that becomes important is the formulation of the query by users. In this paper, we present the results of a preliminary study that we conducted with non-native English speakers who formulate queries for given retrieval tasks. Our results show that the current search engines are sensitive in their rankings to the query formulation, and thus highlights the need for developing more robust ranking methods.
  7. Brin, S.; Page, L.: ¬The anatomy of a large-scale hypertextual Web search engine (1998) 0.10 
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    Abstract
    In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/. To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -- the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want
  8. Wallis, R.; Isaac, A.; Charles, V.; Manguinhas, H.: Recommendations for the application of Schema.org to aggregated cultural heritage metadata to increase relevance and visibility to search engines : the case of Europeana (2017) 0.10 
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    Abstract
    Europeana provides access to more than 54 million cultural heritage objects through its portal Europeana Collections. It is crucial for Europeana to be recognized by search engines as a trusted authoritative repository of cultural heritage objects. Indeed, even though its portal is the main entry point, most Europeana users come to it via search engines. Europeana Collections is fuelled by metadata describing cultural objects, represented in the Europeana Data Model (EDM). This paper presents the research and consequent recommendations for publishing Europeana metadata using the Schema.org vocabulary and best practices. Schema.org html embedded metadata to be consumed by search engines to power rich services (such as Google Knowledge Graph). Schema.org is an open and widely adopted initiative (used by over 12 million domains) backed by Google, Bing, Yahoo!, and Yandex, for sharing metadata across the web It underpins the emergence of new web techniques, such as so called Semantic SEO. Our research addressed the representation of the embedded metadata as part of the Europeana HTML pages and sitemaps so that the re-use of this data can be optimized. The practical objective of our work is to produce a Schema.org representation of Europeana resources described in EDM, being the richest as possible and tailored to Europeana's realities and user needs as well the search engines and their users.
  9. Rajasurya, S.; Muralidharan, T.; Devi, S.; Swamynathan, S.: Semantic information retrieval using ontology in university domain (2012) 0.09 
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    Abstract
    Today's conventional search engines hardly do provide the essential content relevant to the user's search query. This is because the context and semantics of the request made by the user is not analyzed to the full extent. So here the need for a semantic web search arises. SWS is upcoming in the area of web search which combines Natural Language Processing and Artificial Intelligence. The objective of the work done here is to design, develop and implement a semantic search engine- SIEU(Semantic Information Extraction in University Domain) confined to the university domain. SIEU uses ontology as a knowledge base for the information retrieval process. It is not just a mere keyword search. It is one layer above what Google or any other search engines retrieve by analyzing just the keywords. Here the query is analyzed both syntactically and semantically. The developed system retrieves the web results more relevant to the user query through keyword expansion. The results obtained here will be accurate enough to satisfy the request made by the user. The level of accuracy will be enhanced since the query is analyzed semantically. The system will be of great use to the developers and researchers who work on web. The Google results are re-ranked and optimized for providing the relevant links. For ranking an algorithm has been applied which fetches more apt results for the user query.
  10. Zhao, Y.; Ma, F.; Xia, X.: Evaluating the coverage of entities in knowledge graphs behind general web search engines : Poster (2017) 0.09 
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    Abstract
    Web search engines, such as Google and Bing, are constantly employing results from knowledge organization and various visualization features to improve their search services. Knowledge graph, a large repository of structured knowledge represented by formal languages such as RDF (Resource Description Framework), is used to support entity search feature of Google and Bing (Demartini, 2016). When a user searchs for an entity, such as a person, an organization, or a place in Google or Bing, it is likely that a knowledge cardwill be presented on the right side bar of the search engine result pages (SERPs). For example, when a user searches the entity Benedict Cumberbatch on Google, the knowledge card will show the basic structured information about this person, including his date of birth, height, spouse, parents, and his movies, etc. The knowledge card, which is used to present the result of entity search, is generated from knowledge graphs. Therefore, the quality of knowledge graphs is essential to the performance of entity search. However, studies on the quality of knowledge graphs from the angle of entity coverage are scant in the literature. This study aims to investigate the coverage of entities of knowledge graphs behind Google and Bing.
  11. Christensen, A.: Wissenschaftliche Literatur entdecken : was bibliothekarische Discovery-Systeme von der Konkurrenz lernen und was sie ihr zeigen können (2022) 0.09 
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    Abstract
    In den letzten Jahren ist das Angebot an Academic Search Engines für die Recherche nach Fachliteratur zu allen Wissenschaftsgebieten stark angewachsen und ergänzt die beliebten kommerziellen Angebote wie Web of Science oder Scopus. Der Artikel zeigt die wesentlichen Unterschiede zwischen bibliothekarischen Discovery-Systemen und Academic Search Engines wie Base, Dimensions oder Open Alex auf und diskutiert Möglichkeiten, wie beide von einander profitieren können. Diese Entwicklungsperspektiven betreffen Aspekte wie die Kontextualisierung von Wissen, die Datenmodellierung, die automatischen Datenanreicherung sowie den Zuschnitt von Suchräumen.
  12. Lossau, N.: Search engine technology and digital libraries : libraries need to discover the academic internet (2004) 0.09 
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    Abstract
    With the development of the World Wide Web, the "information search" has grown to be a significant business sector of a global, competitive and commercial market. Powerful players have entered this market, such as commercial internet search engines, information portals, multinational publishers and online content integrators. Will Google, Yahoo or Microsoft be the only portals to global knowledge in 2010? If libraries do not want to become marginalized in a key area of their traditional services, they need to acknowledge the challenges that come with the globalisation of scholarly information, the existence and further growth of the academic internet
  13. Summann, F.; Lossau, N.: Search engine technology and digital libraries : moving from theory to practice (2004) 0.08 
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    Abstract
    This article describes the journey from the conception of and vision for a modern search-engine-based search environment to its technological realisation. In doing so, it takes up the thread of an earlier article on this subject, this time from a technical viewpoint. As well as presenting the conceptual considerations of the initial stages, this article will principally elucidate the technological aspects of this journey. The starting point for the deliberations about development of an academic search engine was the experience we gained through the generally successful project "Digital Library NRW", in which from 1998 to 2000-with Bielefeld University Library in overall charge-we designed a system model for an Internet-based library portal with an improved academic search environment at its core. At the heart of this system was a metasearch with an availability function, to which we added a user interface integrating all relevant source material for study and research. The deficiencies of this approach were felt soon after the system was launched in June 2001. There were problems with the stability and performance of the database retrieval system, with the integration of full-text documents and Internet pages, and with acceptance by users, because users are increasingly performing the searches themselves using search engines rather than going to the library for help in doing searches. Since a long list of problems are also encountered using commercial search engines for academic use (in particular the retrieval of academic information and long-term availability), the idea was born for a search engine configured specifically for academic use. We also hoped that with one single access point founded on improved search engine technology, we could access the heterogeneous academic resources of subject-based bibliographic databases, catalogues, electronic newspapers, document servers and academic web pages.
  14. Fife, E.D.; Husch, L.: ¬The Mathematics Archives : making mathematics easy to find on the Web (1999) 0.08 
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    Abstract
    Do a search on AltaVista for "algebra". What do you get? Nearly 700,000 hits, of which AltaVista will allow you to view only what it determines is the top 200. Major search engines such as AltaVista, Excite, HotBot, Lycos, and the like continue to provide a valuable service, but with the recent growth of the Internet, topic-specific sites that provide some organization to the topic are increasingly important. It the goal of the Mathematics Archives to make it easier for the ordinary user to find useful mathematical information on the Web. The Mathematics Archives (http://archives.math.utk.edu) is a multipurpose site for mathematics on the Internet. The focus is on materials which can be used in mathematics education (primarily at the undergraduate level). Resources available range from shareware and public domain software to electronic proceedings of various conferences, to an extensive collection of annotated links to other mathematical sites. All materials on the Archives are categorized and cross referenced for the convenience of the user. Several search mechanisms are provided. The Harvest search engine is implemented to provide a full text search of most of the pages on the Archives. The software we house and our list of annotated links to mathematical sites are both categorized by subject matter. Each of these collections has a specialized search engine to assist the user in locating desired material. Services at the Mathematics Archives are divided up into five broad topics: * Links organized by Mathematical Topics * Software * Teaching Materials * Other Math Archives Features * Other Links
  15. Warnick, W.L.; Leberman, A.; Scott, R.L.; Spence, K.J.; Johnsom, L.A.; Allen, V.S.: Searching the deep Web : directed query engine applications at the Department of Energy (2001) 0.08 
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    Abstract
    Directed Query Engines, an emerging class of search engine specifically designed to access distributed resources on the deep web, offer the opportunity to create inexpensive digital libraries. Already, one such engine, Distributed Explorer, has been used to select and assemble high quality information resources and incorporate them into publicly available systems for the physical sciences. By nesting Directed Query Engines so that one query launches several other engines in a cascading fashion, enormous virtual collections may soon be assembled to form a comprehensive information infrastructure for the physical sciences. Once a Directed Query Engine has been configured for a set of information resources, distributed alerts tools can provide patrons with personalized, profile-based notices of recent additions to any of the selected resources. Due to the potentially enormous size and scope of Directed Query Engine applications, consideration must be given to issues surrounding the representation of large quantities of information from multiple, heterogeneous sources.
  16. Schaer, P.; Mayr, P.; Sünkler, S.; Lewandowski, D.: How relevant is the long tail? : a relevance assessment study on million short (2016) 0.07 
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    Abstract
    Users of web search engines are known to mostly focus on the top ranked results of the search engine result page. While many studies support this well known information seeking pattern only few studies concentrate on the question what users are missing by neglecting lower ranked results. To learn more about the relevance distributions in the so-called long tail we conducted a relevance assessment study with the Million Short long-tail web search engine. While we see a clear difference in the content between the head and the tail of the search engine result list we see no statistical significant differences in the binary relevance judgments and weak significant differences when using graded relevance. The tail contains different but still valuable results. We argue that the long tail can be a rich source for the diversification of web search engine result lists but it needs more evaluation to clearly describe the differences.
  17. Spink, A.; Wilson, T.; Ellis, D.; Ford, N.: Modeling users' successive searches in digital environments : a National Science Foundation/British Library funded study (1998) 0.07 
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    Abstract
    As digital libraries become a major source of information for many people, we need to know more about how people seek and retrieve information in digital environments. Quite commonly, users with a problem-at-hand and associated question-in-mind repeatedly search a literature for answers, and seek information in stages over extended periods from a variety of digital information resources. The process of repeatedly searching over time in relation to a specific, but possibly an evolving information problem (including changes or shifts in a variety of variables), is called the successive search phenomenon. The study outlined in this paper is currently investigating this new and little explored line of inquiry for information retrieval, Web searching, and digital libraries. The purpose of the research project is to investigate the nature, manifestations, and behavior of successive searching by users in digital environments, and to derive criteria for use in the design of information retrieval interfaces and systems supporting successive searching behavior. This study includes two related projects. The first project is based in the School of Library and Information Sciences at the University of North Texas and is funded by a National Science Foundation POWRE Grant <http://www.nsf.gov/cgi-bin/show?award=9753277>. The second project is based at the Department of Information Studies at the University of Sheffield (UK) and is funded by a grant from the British Library <http://www.shef. ac.uk/~is/research/imrg/uncerty.html> Research and Innovation Center. The broad objectives of each project are to examine the nature and extent of successive search episodes in digital environments by real users over time. The specific aim of the current project is twofold: * To characterize progressive changes and shifts that occur in: user situational context; user information problem; uncertainty reduction; user cognitive styles; cognitive and affective states of the user, and consequently in their queries; and * To characterize related changes over time in the type and use of information resources and search strategies particularly related to given capabilities of IR systems, and IR search engines, and examine changes in users' relevance judgments and criteria, and characterize their differences. The study is an observational, longitudinal data collection in the U.S. and U.K. Three questionnaires are used to collect data: reference, client post search and searcher post search questionnaires. Each successive search episode with a search intermediary for textual materials on the DIALOG Information Service is audiotaped and search transaction logs are recorded. Quantitative analysis includes statistical analysis using Likert scale data from the questionnaires and log-linear analysis of sequential data. Qualitative methods include: content analysis, structuring taxonomies; and diagrams to describe shifts and transitions within and between each search episode. Outcomes of the study are the development of appropriate model(s) for IR interactions in successive search episodes and the derivation of a set of design criteria for interfaces and systems supporting successive searching.
  18. Birmingham, W.; Pardo, B.; Meek, C.; Shifrin, J.: ¬The MusArt music-retrieval system (2002) 0.07 
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    Abstract
    Music websites are ubiquitous, and music downloads, such as MP3, are a major source of Web traffic. As the amount of musical content increases and the Web becomes an important mechanism for distributing music, we expect to see a rising demand for music search services. Many currently available music search engines rely on file names, song title, composer or performer as the indexing and retrieval mechanism. These systems do not make use of the musical content. We believe that a more natural, effective, and usable music-information retrieval (MIR) system should have audio input, where the user can query with musical content. We are developing a system called MusArt for audio-input MIR. With MusArt, as with other audio-input MIR systems, a user sings or plays a theme, hook, or riff from the desired piece of music. The system transcribes the query and searches for related themes in a database, returning the most similar themes, given some measure of similarity. We call this "retrieval by query." In this paper, we describe the architecture of MusArt. An important element of MusArt is metadata creation: we believe that it is essential to automatically abstract important musical elements, particularly themes. Theme extraction is performed by a subsystem called MME, which we describe later in this paper. Another important element of MusArt is its support for a variety of search engines, as we believe that MIR is too complex for a single approach to work for all queries. Currently, MusArt supports a dynamic time-warping search engine that has high recall, and a complementary stochastic search engine that searches over themes, emphasizing speed and relevancy. The stochastic search engine is discussed in this paper.
  19. Bensman, S.J.: Eugene Garfield, Francis Narin, and PageRank : the theoretical bases of the Google search engine (2013) 0.07 
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    Abstract
    This paper presents a test of the validity of using Google Scholar to evaluate the publications of researchers by comparing the premises on which its search engine, PageRank, is based, to those of Garfield's theory of citation indexing. It finds that the premises are identical and that PageRank and Garfield's theory of citation indexing validate each other.
    Date
    17.12.2013 11:02:22
  20. Zhang, L.; Liu, Q.L.; Zhang, J.; Wang, H.F.; Pan, Y.; Yu, Y.: Semplore: an IR approach to scalable hybrid query of Semantic Web data (2007) 0.07 
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    Abstract
    As an extension to the current Web, Semantic Web will not only contain structured data with machine understandable semantics but also textual information. While structured queries can be used to find information more precisely on the Semantic Web, keyword searches are still needed to help exploit textual information. It thus becomes very important that we can combine precise structured queries with imprecise keyword searches to have a hybrid query capability. In addition, due to the huge volume of information on the Semantic Web, the hybrid query must be processed in a very scalable way. In this paper, we define such a hybrid query capability that combines unary tree-shaped structured queries with keyword searches. We show how existing information retrieval (IR) index structures and functions can be reused to index semantic web data and its textual information, and how the hybrid query is evaluated on the index structure using IR engines in an efficient and scalable manner. We implemented this IR approach in an engine called Semplore. Comprehensive experiments on its performance show that it is a promising approach. It leads us to believe that it may be possible to evolve current web search engines to query and search the Semantic Web. Finally, we briefy describe how Semplore is used for searching Wikipedia and an IBM customer's product information.

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