We offer a quick way to compare the prices of any in-print and many out-of-print books at over a dozen online bookstores. You can view the results with or without the shipping costs of a single book, and also find the fastest source for a book from ordering to delivery.
Discover more than one million documents from scholarly journals, magazines, conference proceedings, and other special publications from prestigious scientific societies and technical publishers.
The Google Book Search Dynamic Links feature allows you to create more customizable, reliable links to Google Book Search from your site. For example, this tool lets you generate "smart" links that appear only when a book is in our index, or display links that indicate to your users whether a book can be previewed on Google Book Search. The Dynamic Links feature also lets you include a thumbnail image in your link to Google Book Search. This document is intended to let you quickly add this functionality to your site.
CrossRef Search
In order to open published scholarly content for the first time to free, full-text interpublisher searchability, a group of 29 leading journal publishers are participating in a CrossRef Search Pilot.
Through a special, reciprocal arrangement between Google and CrossRef, this Pilot launches a typical Google search but filters the result set to the scholarly research content from participating publishers, with the intent of reducing the noise produced by general web searches.
Google has indexed the full text of scholarly journal articles on the publishers' websites through a CrossRef gateway. Users may submit searches from CrossRef Search Pilot boxes on participating publishers' sites. Results are returned from Google using the Google search and ranking algorithms, and using the article's DOI whenever possible to link from the search results to the published article.
Specify your canonical
Thursday, February 12, 2009 at 12:30 PM
Carpe diem on any duplicate content worries: we now support a format that allows you to publicly specify your preferred version of a URL. If your site has identical or vastly similar content that's accessible through multiple URLs, this format provides you with more control over the URL returned in search results. It also helps to make sure that properties such as link popularity are consolidated to your preferred version
STATISTICAL STRATEGIES ENVIRONMENTAL EPIDEMIOLOGY
report is in. three parts, general problems in environmental epidemiology,. prototypical. problems, and statistical strategies. Emphasis ...
projecteuclid.org/DPubS/Repository/1.0/Disseminate?handle=euclid.bsmsp/1200514698&view=body&content-type=pdf_1 - Similar pages -
by JR GOLDSMITH - Related articles - All 2 versions
How is the indexing performed?
A: Indexing is the process of creating a Conceptual Fingerprint from a text. In Collexis, this automated indexing mechanism performs the following steps on the text: removing the stop words, normalizing the text, selecting concepts by comparison with the thesaurus, clustering the concepts and attaching a relative weight to the concepts by means of a set of algorithms and measuring the specificity, similarity and frequency of the concepts.
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Q: How does Collexis generate its search results?
A: Collexis employs vector matching: comparing a search query with the Fingerprints from the records in a Collexion. The outcome is a very accurate and relevant list of content items and/or experts in the form of a list of records. There also exists the possibility of over-specifying a query (i.e., using a considerable piece of text), thus adding context to the query. This context will help the system to improve the accuracy of the query and return references to those content items that are contextually related. The system administrator can enlarge or reduce the set of returned documents by entering a threshold that indicates the minimum “distance” between the records returned and the query. Matching of a search query with Collexion records can be performed on multiple Collexions at the same time.
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Q: What makes Collexis different?
A: Initially, Collexis differentiates itself from full-text search engines by making use of thesauri for information retrieval. The high-quality search is based on semantics that have been defined in a thesaurus or ontology: synonymous terms and terms in different languages are linked to a single concept. Hierarchical relations between concepts, links between definitions and terms, and other semantic relationships are utilized in the search applications. This process helps to highlight those terms most relevant to the searcher’s query.
Collexis High Definition Search enables fast, accurate and extraordinary knowledge retrieval and discovery quickly and accurately by utilizing fingerprinting technology. The Collexis Fingerprint empowers users to identify and search for documents, experts, trends, and new discoveries more quickly, precisely – and thoroughly – than conventional search engines. For users, the savings in research dollars are extraordinary. High Definition Search positions Collexis as a world leader in the vital area of knowledge management and discovery software.
Text Search
Graphs With Text
Types of Things With Text
People With Shared Interests
Interests Around
Top 100 Authors by Text
Social Connections a la LinkedIn
Connection Between
Cloud Around Person
The FacetedDBLP search interface allows to search computer science publications in the DBLP collection starting from some keyword and shows the result set along with a set of facets, e.g., distinguishing publication years, authors, or conferences. It is the first large scale application that uses GrowBag graphs to create a computer science specific topic facet, with which a user can characterize the result set in terms of main research topics and filter it according to certain subtopics.
FacetedDBLP builds upon the DBLP++ data set which is an enhancement of DBLP (as of 2008-11-21) plus additional keywords and abstracts as available on public web pages. We have also corrected some of the links to electronic editions, which were broken in DBLP. A brief description of the GrowBag facet within FacetedDBLP can be found in our JCDL paper, a detailed description of the algorithm is available on the GrowBag project page.
OpenDOAR is an authoritative directory of academic open access repositories. Each OpenDOAR repository has been visited by project staff to check the information that is recorded here. This in-depth approach does not rely on automated analysis and gives a quality-controlled list of repositories.
Y. Tan, M. Kan, and D. Lee. JCDL '06: Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries, page 314--315. New York, NY, USA, ACM, (2006)