Leading Semantic Search Engine Adds More Than 10 million PubMed Abstracts,
Enabling Users to Find Credible Health Information Easier and Faster than with
Current Search Technologies: hakia
NEW YORK, June 12 /PRNewswire/ -- hakia, the Web's leading general
semantic search engine, announced today that it has increased its health and
medical search coverage by adding more than 10 million abstracts from PubMed,
a medical database maintained by the U.S. National Library of Medicine and the
National Institutes of Health. Using its advanced QDEX technology and
semantic capabilities, hakia makes it easier to find current and historic
medical and health documents dating back five years. The PubMed search is now
available at pubmed.hakia.com.
Medical researchers, doctors, students, and consumers can now benefit from
more transparent and accurate PubMed searches via hakia's powerful semantic
technology. Compared to searching for documents on PubMed itself, hakia's
advanced technology saves time to reach relevant information, which is
displayed as more transparent.
"If anything, hakia's PubMed search can bring results that you didn't know
existed," said Dr. John Boockvar, Alvina and Willis Murphy Assistant Professor
of Neurological Surgery at Weill Cornell Medical College, "hakia's addition
of PubMed articles is the right direction toward more efficient search of
medical information, which is critical to timely progress in medical
research."
The addition of the PubMed documents demonstrates hakia's commitment to
providing users with the most accurate, credible and useful health content
online. hakia offers results that are credibility-stamped and as up-to-date as
possible, using sources that have met the quality criteria set forth by the
Medical Library Association.
"PubMed documents, like many other credible databases, represent a unique
challenge for search engines like Google, because popularity is simply not the
right criteria for retrieving medical information," said Dr. Riza C. Berkan,
CEO of hakia.com. "hakia's semantic search technology is devoid of all such
limitations that come with statistical methods. Instead, hakia's algorithms
search for the best contextual matches of the search query by using medical
ontologies."
PubMed searches can be conducted using hakia's exclusive PubMed search at
pubmed.hakia.com, as part of hakia's medical search at
medical.hakia.com, or as part of the general search engine itself --
www.hakia.com.
About hakia
hakia is a pioneer in the semantic search space and is currently in the
late stages of developing technology with the potential to revolutionize the
search industry. Founded in 2004, hakia is privately held and based in New
York City. For more information about hakia, please visit
www.hakia.com.
| Semantic Search Engines |
Semantic Search - Biomedical InformationTopic: Text Mining MedLine Data e-LiSe (e-Literature Searcher) is an easy-to-use web-based application which finds biomedical information truly related to English words provided by the user. The program uses PubMed database of scientific abstracts as the source of data and a novel bio-linguistic statistical method (based on Z-score), to discover true correlations, even when they are low-frequency associations. e-LiSe is also capable of finding names of researchers correlated to the information searched by the user. It can function as a name reference engine, answering questions like “who is working on specified subject?†or “what are the coworkers/collaborators of a certain person?â€. For the latter the software uses the list of co-authors of each publication a researcher has written to display connections between scientists. Data analysed by e-LiSe comprises of about 17 millions abstracts. To improve the programs’ performance a threshold has been set for the number of abstracts that can be processed for a single query (50 thousand abstracts). When the user submits a query with demands a larger abstracts set, e-LiSe will ask to refine the query. (new) More sophisticated ways of searching with e-LiSe are now possible.Full support for queries with logical operators (AND, OR and NOT), prioritized by the means of brackets, has just been introduced read more (new) e-LiSe usability was thoroughly tested. We created automated test to check the value of e-LiSe-generated information about hereditary diseases.read more (new) Application note about e-LiSe has just been published in Bioinformatics. You can find it here Reference: |