18,741 research outputs found
Enhanced Search for Educational Resources - A Perspective and a Prototype from ccLearn
Users of search tools who seek educational materials on the Internet are typically presented with either a web-scale search (e.g., Google or Yahoo) or a specialized, site-specific tool. The specialized search tools often rely upon custom data fields, such as user-entered ratings, to provide additional value. As currently designed, these systems are generally too labor intensive to manage and scale up beyond a single site or set of resources.However, custom (or structured) data of some form is necessary if search outcomes foreducational materials are to be improved. For example, design criteria and evaluative metrics are crucial attributes for educational resources, and these currently require human labeling and verification. Thus, one challenge is to design a search tool that capitalizes on available structured data (also called metadata) but is not crippled if the data are missing. This information should be amenable to repurposing by anyone, which means that it must be archived in a manner that can be discovered and leveraged easily.In this paper, we describe the extent to which DiscoverEd, a prototype developed by ccLearn, meets the design challenge of a scalable, enhanced search platform for educational resources. We then explore some of the key challenges regarding enhanced search for topic-specific Internet resources generally. We conclude by illustrating some possible future developments and third-party enhancements to the DiscoverEd prototype
Technology Integration around the Geographic Information: A State of the Art
One of the elements that have popularized and facilitated the use of geographical information on a variety of computational applications has been the use of Web maps; this has opened new research challenges on different subjects, from locating places and people, the study of social behavior or the analyzing of the hidden structures of the terms used in a natural language query used for locating a place. However, the use of geographic information under technological features is not new, instead it has been part of a development and technological integration process. This paper presents a state of the art review about the application of geographic information under different approaches: its use on location based services, the collaborative user participation on it, its contextual-awareness, its use in the Semantic Web and the challenges of its use in natural languge queries. Finally, a prototype that integrates most of these areas is presented
What Users See – Structures in Search Engine Results Pages
This paper investigates the composition of search engine results pages. We define what elements the most
popular web search engines use on their results pages (e.g., organic results, advertisements, shortcuts) and to
which degree they are used for popular vs. rare queries. Therefore, we send 500 queries of both types to the
major search engines Google, Yahoo, Live.com and Ask. We count how often the different elements are used by
the individual engines. In total, our study is based on 42,758 elements. Findings include that search engines use
quite different approaches to results pages composition and therefore, the user gets to see quite different results
sets depending on the search engine and search query used. Organic results still play the major role in the results
pages, but different shortcuts are of some importance, too. Regarding the frequency of certain host within the
results sets, we find that all search engines show Wikipedia results quite often, while other hosts shown depend
on the search engine used. Both Google and Yahoo prefer results from their own offerings (such as YouTube or
Yahoo Answers). Since we used the .com interfaces of the search engines, results may not be valid for other
country-specific interfaces
Query Chains: Learning to Rank from Implicit Feedback
This paper presents a novel approach for using clickthrough data to learn
ranked retrieval functions for web search results. We observe that users
searching the web often perform a sequence, or chain, of queries with a similar
information need. Using query chains, we generate new types of preference
judgments from search engine logs, thus taking advantage of user intelligence
in reformulating queries. To validate our method we perform a controlled user
study comparing generated preference judgments to explicit relevance judgments.
We also implemented a real-world search engine to test our approach, using a
modified ranking SVM to learn an improved ranking function from preference
data. Our results demonstrate significant improvements in the ranking given by
the search engine. The learned rankings outperform both a static ranking
function, as well as one trained without considering query chains.Comment: 10 page
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