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The NASA-ADS Abstract Service provides a sophisticated search capability for the literature in Astronomy, Planetary Sciences, Physics/Geophysics, and Space Instrumentation. The ADS is funded by NASA and access to the ADS services is free to anybody worldwide without restrictions. It allows the user to search the literature by author, title, and abstract text. The ADS database contains over 3.6 million references, with 965,000 in the Astronomy/Planetary Sciences database, and 1.6 million in the Physics/Geophysics database. 2/3 of the records have full abstracts, the rest are table of contents entries (titles and author lists only). The coverage for the Astronomy literature is better than 95% from 1975. Before that we cover all major journals and many smaller ones. Most of the journal literature is covered back to volume 1. We now get abstracts on a regular basis from most journals. Over the last year we have entered basically all conference proceedings tables of contents that are available at the Harvard Smithsonian Center for Astrophysics library. This has greatly increased the coverage of conference proceedings in the ADS. The ADS also covers the ArXiv Preprints. We download these preprints every night and index all the preprints. They can be searched either together with the other abstracts or separately. There are currently about 260,000 preprints in that database. In January 2004 we have introduced two new services, full text searching and a personal notification service called "myADS". As all other ADS services, these are free to use for anybody.
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Background With the growing availability of full-text articles online, scientists and other consumers of the life sciences literature now have the ability to go beyond searching bibliographic records (title, abstract, metadata) to directly access full-text content. Motivated by this emerging trend, I posed the following question: is searching full text more effective than searching abstracts? This question is answered by comparing text retrieval algorithms on MEDLINE abstracts, full-text articles, and spans (paragraphs) within full-text articles using data from the TREC 2007 genomics track evaluation. Two retrieval models are examined: bm25 and the ranking algorithm implemented in the open-source Lucene search engine. Results Experiments show that treating an entire article as an indexing unit does not consistently yield higher effectiveness compared to abstract-only search. However, retrieval based on spans, or paragraphs-sized segments of full-text articles, consistently outperforms abstract-only search. Results suggest that highest overall effectiveness may be achieved by combining evidence from spans and full articles. Conclusion Users searching full text are more likely to find relevant articles than searching only abstracts. This finding affirms the value of full text collections for text retrieval and provides a starting point for future work in exploring algorithms that take advantage of rapidly-growing digital archives. Experimental results also highlight the need to develop distributed text retrieval algorithms, since full-text articles are significantly longer than abstracts and may require the computational resources of multiple machines in a cluster. The MapReduce programming model provides a convenient framework for organizing such computations. PMID:19192280
The purpose of this study was to investigate differences between abstracts of posters presented at the 79(th) (2002) and 80(th) (2003) Annual Session & Exhibition of the American Dental Education Association (ADEA) and the published full-length articles resulting from the same studies. The abstracts for poster presentation sessions were downloaded, and basic characteristics of the abstracts and their authors were determined. A PubMed search was then performed to identify the publication of full-length articles based on those abstracts in a peer-reviewed journal. The differences between the abstract and the article were examined and categorized as major and minor differences. Differences identified included authorship, title, materials and methods, results, conclusions, and funding. Data were analyzed with both descriptive and analytic statistics. Overall, 89 percent of the abstracts had at least one variation from its corresponding article, and 65 percent and 76 percent of the abstracts had at least one major and minor variation, respectively, from its corresponding article. The most prevalent major variation was in study results, and the most prevalent minor variation was change in the number of authors. The discussion speculates on some possible reasons for these differences.
Explores trade-offs among existing approaches. Reveals strengths and weaknesses of proposed approaches, as well as which aspects of the problem are not yet covered. Introduces software engineering approach to evaluating semantic web services. Service-Oriented Computing is one of the most promising software engineering trends because of the potential to reduce the programming effort for future distributed industrial systems. However, only a small part of this potential rests on the standardization of tools offered by the web services stack. The larger part of this potential rests upon the development of sufficient semantics to automate service orchestration. Currently there are many different approaches to semantic web service descriptions and many frameworks built around them. A common understanding, evaluation scheme, and test bed to compare and classify these frameworks in terms of their capabilities and shortcomings, is necessary to make progress in developing the full potential of Service-Oriented Computing. The Semantic Web Services Challenge is an open source initiative that provides a public evaluation and certification of multiple frameworks on common industrially-relevant problem sets. This edited volume reports on the first results in developing common understanding of the various technologies intended to facilitate the automation of mediation, choreography and discovery for Web Services using semantic annotations. Semantic Web Services Challenge: Results from the First Year is designed for a professional audience composed of practitioners and researchers in industry. Professionals can use this book to evaluate SWS technology for their potential practical use. The book is also suitable for advanced-level students in computer science.