Search (121 results, page 2 of 7)

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Abstract

Amid the rapid growth of information today is the increasing challenge for people to navigate its magnitude. Dynamics and heterogeneity of large information spaces such as the Web raise important questions about information retrieval in these environments. Collection of all information in advance and centralization of IR operations are extremely difficult, if not impossible, because systems are dynamic and information is distributed. The chapter discusses some of the key issues facing classic information retrieval models and presents a decentralized, organic view of information systems pertaining to search in large scale networks. It focuses on the impact of network structure on search performance and discusses a phenomenon we refer to as the Clustering Paradox, in which the topology of interconnected systems imposes a scalability limit.

Source

Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

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a

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Abstract

In this note, we show how to marginalize over the damping parameter of the PageRank equation so as to obtain a parameter-free version known as TotalRank. Our discussion is meant as a reference and intended to provide a guided tour towards an interesting result that has applications in information retrieval and classification.

Type

a

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Abstract

For introductory information retrieval courses at the undergraduate and graduate level in computer science, information science and computer engineering departments. Written by a leader in the field of information retrieval, Search Engines: Information Retrieval in Practice, is designed to give undergraduate students the understanding and tools they need to evaluate, compare and modify search engines. Coverage of the underlying IR and mathematical models reinforce key concepts. The book's numerous programming exercises make extensive use of Galago, a Java-based open source search engine. SUPPLEMENTS / Extensive lecture slides (in PDF and PPT format) / Solutions to selected end of chapter problems (Instructors only) / Test collections for exercises / Galago search engine

LCSH

Information retrieval
Information Storage and Retrieval

RSWK

Suchmaschine / Information Retrieval

Subject

Suchmaschine / Information Retrieval
Information retrieval
Information Storage and Retrieval

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Abstract

The study sets out to explore the factors that influence the evaluation of information and the judgments made in the process of finding useful information in web search contexts. Based on a diary study of 2 assigned tasks to search on Google and Google Scholar, factor analysis identified the core constructs of content, relevance, scope, and style, as well as informational and system "ease of use" as influencing the judgment that useful information had been found. Differences were found in the participants' evaluation of information across the search tasks on Google and on Google Scholar when identified by the factors related to both content and ease of use. The findings from this study suggest how searchers might critically evaluate information, and the study identifies a relation between the user's involvement in the information interaction and the influences of the perceived system ease of use and information design.

Source

Journal of the Association for Information Science and Technology. 67(2016) no.4, S.824-840

Type

a

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Abstract

Searches conducted on web search engines reflect the interests of users and society. Google Trends, which provides information about the queries searched by users of the Google web search engine, is a rich data source from which a wealth of information can be mined. We investigated the possibility of using web search volume data from Google Trends to predict academic fame. As queries are language-dependent, we studied universities from two countries with different languages, the United States and Spain. We found a significant correlation between the search volume of a university name and the university's academic reputation or fame. We also examined the effect of some Google Trends features, namely, limiting the search to a specific country or topic category on the search volume data. Finally, we examined the effect of university sizes on the correlations found to gain a deeper understanding of the nature of the relationships.

Source

Journal of the Association for Information Science and Technology. 65(2014) no.4, S.707-720

Type

a

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Abstract

People commonly experience difficulties when searching the Web, arising from an incomplete knowledge regarding their information needs, an inability to formulate accurate queries, and a low tolerance for considering the relevance of the search results. While simple and easy to use interfaces have made Web search universally accessible, they provide little assistance for people to overcome the difficulties they experience when their information needs are more complex than simple fact-verification. In human-centred Web search, the purpose of the search engine expands from a simple information retrieval engine to a decision support system. People are empowered to take an active role in the search process, with the search engine supporting them in developing a deeper understanding of their information needs, assisting them in crafting and refining their queries, and aiding them in evaluating and exploring the search results. In this chapter, recent research in this domain is outlined and discussed.

Source

Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

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a

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Abstract

This paper reports on a transaction log analysis of the type and topic of search queries entered into the search engine Google (Australia). Two aspects, in particular, set this apart from previous studies: the sampling and analysis take account of the distribution of search queries, and lifestyle information of the searcher was matched with each search query. A surprising finding was that there was no observed statistically significant difference in search type or topics for different segments of the online population. It was found that queries about popular culture and Ecommerce accounted for almost half of all search engine queries and that half of the queries were entered with a particular Website in mind. The findings of this study also suggest that the Internet search engine is not only an interface to information or a shortcut to Websites, it is equally a site of leisure. This study has implications for the design and evaluation of search engines as well as our understanding of search engine use.

Source

Journal of the American Society for Information Science and Technology. 62(2011) no.4, S.761-775

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Date

22. 7.2004 9:42:33

Type

a

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Abstract

The main goal of this book is to transfer new research results from the fields of advanced computer sciences and information science to the design of new search engines. The readers will have a better idea of the new trends in applied research. The achievement of relevant, organized, sorted, and workable answers- to name but a few - from a search is becoming a daily need for enterprises and organizations, and, to a greater extent, for anyone. It does not consist of getting access to structural information as in standard databases; nor does it consist of searching information strictly by way of a combination of key words. It goes far beyond that. Whatever its modality, the information sought should be identified by the topics it contains, that is to say by its textual, audio, video or graphical contents. This is not a new issue. However, recent technological advances have completely changed the techniques being used. New Web technologies, the emergence of Intranet systems and the abundance of information on the Internet have created the need for efficient search and information access tools.
Recent technological progress in computer science, Web technologies, and constantly evolving information available on the Internet has drastically changed the landscape of search and access to information. Web search has significantly evolved in recent years. In the beginning, web search engines such as Google and Yahoo! were only providing search service over text documents. Aggregated search was one of the first steps to go beyond text search, and was the beginning of a new era for information seeking and retrieval. These days, new web search engines support aggregated search over a number of vertices, and blend different types of documents (e.g., images, videos) in their search results. New search engines employ advanced techniques involving machine learning, computational linguistics and psychology, user interaction and modeling, information visualization, Web engineering, artificial intelligence, distributed systems, social networks, statistical analysis, semantic analysis, and technologies over query sessions. Documents no longer exist on their own; they are connected to other documents, they are associated with users and their position in a social network, and they can be mapped onto a variety of ontologies. Similarly, retrieval tasks have become more interactive and are solidly embedded in a user's geospatial, social, and historical context. It is conjectured that new breakthroughs in information retrieval will not come from smarter algorithms that better exploit existing information sources, but from new retrieval algorithms that can intelligently use and combine new sources of contextual metadata.
With the rapid growth of web-based applications, such as search engines, Facebook, and Twitter, the development of effective and personalized information retrieval techniques and of user interfaces is essential. The amount of shared information and of social networks has also considerably grown, requiring metadata for new sources of information, like Wikipedia and ODP. These metadata have to provide classification information for a wide range of topics, as well as for social networking sites like Twitter, and Facebook, each of which provides additional preferences, tagging information and social contexts. Due to the explosion of social networks and other metadata sources, it is an opportune time to identify ways to exploit such metadata in IR tasks such as user modeling, query understanding, and personalization, to name a few. Although the use of traditional metadata such as html text, web page titles, and anchor text is fairly well-understood, the use of category information, user behavior data, and geographical information is just beginning to be studied. This book is intended for scientists and decision-makers who wish to gain working knowledge about search engines in order to evaluate available solutions and to dialogue with software and data providers.

Content

Enthält die Beiträge: Das, A., A. Jain: Indexing the World Wide Web: the journey so far. Ke, W.: Decentralized search and the clustering paradox in large scale information networks. Roux, M.: Metadata for search engines: what can be learned from e-Sciences? Fluhr, C.: Crosslingual access to photo databases. Djioua, B., J.-P. Desclés u. M. Alrahabi: Searching and mining with semantic categories. Ghorbel, H., A. Bahri u. R. Bouaziz: Fuzzy ontologies building platform for Semantic Web: FOB platform. Lassalle, E., E. Lassalle: Semantic models in information retrieval. Berry, M.W., R. Esau u. B. Kiefer: The use of text mining techniques in electronic discovery for legal matters. Sleem-Amer, M., I. Bigorgne u. S. Brizard u.a.: Intelligent semantic search engines for opinion and sentiment mining. Hoeber, O.: Human-centred Web search.
Vert, S.: Extensions of Web browsers useful to knowledge workers. Chen, L.-C.: Next generation search engine for the result clustering technology. Biskri, I., L. Rompré: Using association rules for query reformulation. Habernal, I., M. Konopík u. O. Rohlík: Question answering. Grau, B.: Finding answers to questions, in text collections or Web, in open domain or specialty domains. Berri, J., R. Benlamri: Context-aware mobile search engine. Bouidghaghen, O., L. Tamine: Spatio-temporal based personalization for mobile search. Chaudiron, S., M. Ihadjadene: Studying Web search engines from a user perspective: key concepts and main approaches. Karaman, F.: Artificial intelligence enabled search engines (AIESE) and the implications. Lewandowski, D.: A framework for evaluating the retrieval effectiveness of search engines.

LCSH

Information retrieval
Information retrieval / Research
Information storage and retrieval systems / Research
Information behavior

Subject

Information retrieval
Information retrieval / Research
Information storage and retrieval systems / Research
Information behavior

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Abstract

This article offers a comparative analysis of the personal profiling capabilities of the two most important free citation-based academic search engines, namely, Microsoft Academic Search (MAS) and Google Scholar Citations (GSC). Author profiles can be useful for evaluation purposes once the advantages and the shortcomings of these services are described and taken into consideration. In total, 771 personal profiles appearing in both the MAS and the GSC databases were analyzed. Results show that the GSC profiles include more documents and citations than those in MAS but with a strong bias toward the information and computing sciences, whereas the MAS profiles are disciplinarily better balanced. MAS shows technical problems such as a higher number of duplicated profiles and a lower updating rate than GSC. It is concluded that both services could be used for evaluation proposes only if they are applied along with other citation indices as a way to supplement that information.

Source

Journal of the Association for Information Science and Technology. 65(2014) no.6, S.1149-1156

Type

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Source

Information - Wissenschaft und Praxis. 70(2019) H.1, S.1-2

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Abstract

A commonly used technique for improving search engine performance is result caching. In result caching, precomputed results (e.g., URLs and snippets of best matching pages) of certain queries are stored in a fast-access storage. The future occurrences of a query whose results are already stored in the cache can be directly served by the result cache, eliminating the need to process the query using costly computing resources. Although other performance metrics are possible, the main performance metric for evaluating the success of a result cache is hit rate. In this work, we present a machine learning approach to improve the hit rate of a result cache by facilitating a large number of features extracted from search engine query logs. We then apply the proposed machine learning approach to static, dynamic, and static-dynamic caching. Compared to the previous methods in the literature, the proposed approach improves the hit rate of the result cache up to 0.66%, which corresponds to 9.60% of the potential room for improvement.

Source

Information processing and management. 53(2017) no.4, S.834-850

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Abstract

Information Retrieval (IR) research typically evaluates search systems in terms of the standard precision, recall and F-measures to weight the relative importance of precision and recall (e.g. van Rijsbergen, 1979). All of these assess the extent to which the system returns good matches for a query. In contrast, webometric measures are designed specifically for web search engines and are designed to monitor changes in results over time and various aspects of the internal logic of the way in which search engine select the results to be returned. This chapter introduces a range of webometric measurements and illustrates them with case studies of Google, Bing and Yahoo! This is a very fertile area for simple and complex new investigations into search engine results.

Source

Innovations in information retrieval: perspectives for theory and practice. Eds.: A. Foster, u. P. Rafferty

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Abstract

In der vorgestellten Studie aus dem Bereich des interaktiven Information Retrieval wurde erstmals die Erwartungshaltung von Suchmaschinennutzern als mögliche Determinante der Benutzerzufriedenheit untersucht. Das experimentelle Untersuchungsdesign basiert auf einem betriebswirtschaftlichen Modell, das die Entstehung von Kundenzufriedenheit durch die Bestätigung bzw. Nicht-Bestätigung von Erwartungen erklärt. Ein zentrales Ergebnis dieser Studie ist, das bei der Messung von Benutzerzufriedenheit besonders auf den Messzeitpunkt zu achten ist. Des Weiteren konnte ein von der Systemgüte abhängiger Adaptionseffekt hinsichtlich der Relevanzbewertung der Benutzer nachgewiesen werden.

Source

Information - Wissenschaft und Praxis. 61(2010) H.6/7, S.385-396

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Abstract

E-sciences are data-intensive sciences that make a large use of the Web to share, collect, and process data. In this context, primary scientific data is becoming a new challenging issue as data must be extensively described (1) to account for empiric conditions and results that allow interpretation and/or analyses and (2) to be understandable by computers used for data storage and information retrieval. With this respect, metadata is a focal point whatever it is considered from the point of view of the user to visualize and exploit data as well as this of the search tools to find and retrieve information. Numerous disciplines are concerned with the issues of describing complex observations and addressing pertinent knowledge. In this paper, similarities and differences in data description and exploration strategies among disciplines in e-sciences are examined.

Source

Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

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Abstract

With ever increasing information being available to the end users, search engines have become the most powerful tools for obtaining useful information scattered on the Web. However, it is very common that even most renowned search engines return result sets with not so useful pages to the user. Research on semantic search aims to improve traditional information search and retrieval methods where the basic relevance criteria rely primarily on the presence of query keywords within the returned pages. This work is an attempt to explore different relevancy ranking approaches based on semantics which are considered appropriate for the retrieval of relevant information. In this paper, various pilot projects and their corresponding outcomes have been investigated based on methodologies adopted and their most distinctive characteristics towards ranking. An overview of selected approaches and their comparison by means of the classification criteria has been presented. With the help of this comparison, some common concepts and outstanding features have been identified.

Source

Information processing and management. 50(2014) no.2, S.416-425

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Abstract

Query suggestion is generally an integrated part of web search engines. In this study, we first redefine and reduce the query suggestion problem as "comparison of queries". We then propose a general modular framework for query suggestion algorithm development. We also develop new query suggestion algorithms which are used in our proposed framework, exploiting query, session and user features. As a case study, we use query logs of a real educational search engine that targets K-12 students in Turkey. We also exploit educational features (course, grade) in our query suggestion algorithms. We test our framework and algorithms over a set of queries by an experiment and demonstrate a 66-90% statistically significant increase in relevance of query suggestions compared to a baseline method.

Source

Information processing and management. 52(2016) no.5, S.733-752

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a

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Abstract

This chapter presents a theoretical framework for evaluating next generation search engines. The author focuses on search engines whose results presentation is enriched with additional information and does not merely present the usual list of "10 blue links," that is, of ten links to results, accompanied by a short description. While Web search is used as an example here, the framework can easily be applied to search engines in any other area. The framework not only addresses the results presentation, but also takes into account an extension of the general design of retrieval effectiveness tests. The chapter examines the ways in which this design might influence the results of such studies and how a reliable test is best designed.

Source

Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

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Abstract

The search giant is automatically building Knowledge Vault, a massive database that could give us unprecedented access to the world's facts GOOGLE is building the largest store of knowledge in human history - and it's doing so without any human help. Instead, Knowledge Vault autonomously gathers and merges information from across the web into a single base of facts about the world, and the people and objects in it.

Type

a

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Abstract

Information seeking is a fundamental human activity. In the modern world, it is frequently conducted through interactions with search systems. The retrieval and comprehension of information returned by these systems is a key part of decision making and action in a broad range of settings. Advances in data availability coupled with new interaction paradigms, and mobile and cloud computing capabilities, have created a broad range of new opportunities for information access and use. In this comprehensive book for professionals, researchers, and students involved in search system design and evaluation, search expert Ryen White discusses how search systems can capitalize on new capabilities and how next-generation systems must support higher order search activities such as task completion, learning, and decision making. He outlines the implications of these changes for the evolution of search evaluation, as well as challenges that extend beyond search systems in areas such as privacy and societal benefit.

RSWK

Information Retrieval

Subject

Information Retrieval