Search (12 results, page 1 of 1)

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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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Abstract

Session characteristics taken from large transaction logs of three Web search environments (academic Web site, public search engine, consumer health information portal) were modeled using cluster analysis to determine if coherent session groups emerged for each environment and whether the types of session groups are similar across the three environments. The analysis revealed three distinct clusters of session behaviors common to each environment: hit and run sessions on focused topics, relatively brief sessions on popular topics, and sustained sessions using obscure terms with greater query modification. The findings also revealed shifts in session characteristics over time for one of the datasets, away from hit and run sessions toward more popular search topics. A better understanding of session characteristics can help system designers to develop more responsive systems to support search features that cater to identifiable groups of searchers based on their search behaviors. For example, the system may identify struggling searchers based on session behaviors that match those identified in the current study to provide context sensitive help.

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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

This paper discusses the impact of metadata implementation in a webpage on its visibility performance in a search engine results list. Influential internal and external factors of metadata implementation were identified. How these factors affect webpage visibility in a search engine results list was examined in an experimental study. Findings suggest that metadata is a good mechanism to improve webpage visibility, the metadata subject field plays a more important role than any other metadata field and keywords extracted from the webpage itself, particularly title or full-text, are most effective. To maximize the effects, these keywords should come from both title and full-text.

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Abstract

This paper discusses the impact of metadata implementation in a webpage on its visibility performance in a search engine results list. Influential internal and external factors of metadata implementation were identified. How these factors affect webpage visibility in a search engine results list was examined in an experimental study. Findings suggest that metadata is a good mechanism to improve webpage visibility, the metadata subject field plays a more important role than any other metadata field and keywords extracted from the webpage itself, particularly title or full-text, are most effective. To maximize the effects, these keywords should come from both title and full-text.

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Abstract

The authors investigated 11 sports-related query keywords extracted from a public search engine query log to better understand sports-related information seeking on the Internet. After the query log contents were cleaned and query data were parsed, popular sports-related keywords were identified, along with frequently co-occurring query terms associated with the identified keywords. Relationships among each sports-related focus keyword and its related keywords were characterized and grouped using multidimensional scaling (MDS) in combination with traditional hierarchical clustering methods. The two approaches were synthesized in a visual context by highlighting the results of the hierarchical clustering analysis in the visual MDS configuration. Important events, people, subjects, merchandise, and so on related to a sport were illustrated, and relationships among the sports were analyzed. A small-scale comparative study of sports searches with and without term assistance was conducted. Searches that used search term assistance by relying on previous query term relationships outperformed the searches without the search term assistance. The findings of this study provide insights into sports information seeking behavior on the Internet. The developed method also may be applied to other query log subject areas.

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Abstract

This article presents a visualization tool for information retrieval. Some retrieval evaluation models are interpreted in the two-dimensional space comprising direction and distance. The two different similarity measures-angle and distance-are displayed in the visual space. A new retrieval means based on the visual retrieval tool, the controlling bar, is developed for a search

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Abstract

Metadata is designed to improve information organization and information retrieval effectiveness and efficiency on the Internet. The way web publishers respond to metadata and the way they use it when publishing their web pages, however, is still a mystery. The authors of this paper aim to solve this mystery by defining different professional publisher groups, examining the behaviors of these user groups, and identifying the characteristics of their metadata use. This study will enhance the current understanding of metadata application behavior and provide evidence useful to researchers, web publishers, and search engine designers.

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Abstract

In this article we present a method for retrieving documents from a digital library through a visual interface based on automatically generated concepts. We used a vocabulary generation algorithm to generate a set of concepts for the digital library and a technique called the max-min distance technique to cluster them. Additionally, the concepts were visualized in a spring embedding graph layout to depict the semantic relationship among them. The resulting graph layout serves as an aid to users for retrieving documents. An online archive containing the contents of D-Lib Magazine from July 1995 to May 2002 was used to test the utility of an implemented retrieval and visualization system. We believe that the method developed and tested can be applied to many different domains to help users get a better understanding of online document collections and to minimize users' cognitive load during execution of search tasks. Over the past few years, the volume of information available through the World Wide Web has been expanding exponentially. Never has so much information been so readily available and shared among so many people. Unfortunately, the unstructured nature and huge volume of information accessible over networks have made it hard for users to sift through and find relevant information. To deal with this problem, information retrieval (IR) techniques have gained more intensive attention from both industrial and academic researchers. Numerous IR techniques have been developed to help deal with the information overload problem. These techniques concentrate on mathematical models and algorithms for retrieval. Popular IR models such as the Boolean model, the vector-space model, the probabilistic model and their variants are well established.
From the user's perspective, however, it is still difficult to use current information retrieval systems. Users frequently have problems expressing their information needs and translating those needs into queries. This is partly due to the fact that information needs cannot be expressed appropriately in systems terms. It is not unusual for users to input search terms that are different from the index terms information systems use. Various methods have been proposed to help users choose search terms and articulate queries. One widely used approach is to incorporate into the information system a thesaurus-like component that represents both the important concepts in a particular subject area and the semantic relationships among those concepts. Unfortunately, the development and use of thesauri is not without its own problems. The thesaurus employed in a specific information system has often been developed for a general subject area and needs significant enhancement to be tailored to the information system where it is to be used. This thesaurus development process, if done manually, is both time consuming and labor intensive. Usage of a thesaurus in searching is complex and may raise barriers for the user. For illustration purposes, let us consider two scenarios of thesaurus usage. In the first scenario the user inputs a search term and the thesaurus then displays a matching set of related terms. Without an overview of the thesaurus - and without the ability to see the matching terms in the context of other terms - it may be difficult to assess the quality of the related terms in order to select the correct term. In the second scenario the user browses the whole thesaurus, which is organized as in an alphabetically ordered list. The problem with this approach is that the list may be long, and neither does it show users the global semantic relationship among all the listed terms.
Nevertheless, because thesaurus use has shown to improve retrieval, for our method we integrate functions in the search interface that permit users to explore built-in search vocabularies to improve retrieval from digital libraries. Our method automatically generates the terms and their semantic relationships representing relevant topics covered in a digital library. We call these generated terms the "concepts", and the generated terms and their semantic relationships we call the "concept space". Additionally, we used a visualization technique to display the concept space and allow users to interact with this space. The automatically generated term set is considered to be more representative of subject area in a corpus than an "externally" imposed thesaurus, and our method has the potential of saving a significant amount of time and labor for those who have been manually creating thesauri as well. Information visualization is an emerging discipline and developed very quickly in the last decade. With growing volumes of documents and associated complexities, information visualization has become increasingly important. Researchers have found information visualization to be an effective way to use and understand information while minimizing a user's cognitive load. Our work was based on an algorithmic approach of concept discovery and association. Concepts are discovered using an algorithm based on an automated thesaurus generation procedure. Subsequently, similarities among terms are computed using the cosine measure, and the associations among terms are established using a method known as max-min distance clustering. The concept space is then visualized in a spring embedding graph, which roughly shows the semantic relationships among concepts in a 2-D visual representation. The semantic space of the visualization is used as a medium for users to retrieve the desired documents. In the remainder of this article, we present our algorithmic approach of concept generation and clustering, followed by description of the visualization technique and interactive interface. The paper ends with key conclusions and discussions on future work.

Content

The JAVA applet is available at <http://ella.slis.indiana.edu/~junzhang/dlib/IV.html>. A prototype of this interface has been developed and is available at <http://ella.slis.indiana.edu/~junzhang/dlib/IV.html>. The D-Lib search interface is available at <http://www.dlib.org/Architext/AT-dlib2query.html>.

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Abstract

Bibliometric analysis based on literature in the Web of Science (WOS) has become an increasingly popular method for visualizing the structure of scientific fields. Keywords Plus and Author Keywords are commonly selected as units of analysis, despite the limited research evidence demonstrating the effectiveness of Keywords Plus. This study was conceived to evaluate the efficacy of Keywords Plus as a parameter for capturing the content and scientific concepts presented in articles. Using scientific papers about patient adherence that were retrieved from WOS, a comparative assessment of Keywords Plus and Author Keywords was performed at the scientific field level and the document level, respectively. Our search yielded more Keywords Plus terms than Author Keywords, and the Keywords Plus terms were more broadly descriptive. Keywords Plus is as effective as Author Keywords in terms of bibliometric analysis investigating the knowledge structure of scientific fields, but it is less comprehensive in representing an article's content.

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Date

8. 4.2015 16:22:13