Update 2018-07-31: We updated the Dropbox Link Google Scholar recently changed its layout, and as a consequence, Docear couldn’t fetch metadata anymore from Google Scholar for PDF files. Fortunately, one of our users (“Silberzwiebel”) adjusted Docear’s Google Scholar Parser, and now everything works as usual. However, we have not yet integrated the function into the main version of Docear. This means, even if you have just downloaded Docear, you need to manually update the Google Scholar Parser, if you want to fetch metadata for your PDF files. To update the Google Scholar parser, do the following Close Docear if it’s currently running Download the updated library docear-metadata-lib-0.0.1.jar. Replace the existing file “docear-metadata-lib-0.0.1.jar” with the new one. You will find the file in Read more…
Mr. DLib Recommendations-as-a-Service v1.3: “Word Embeddings” and Many Minor Improvements and Bug Fixes
We released version 1.3 of Mr. DLib´s Recommender-System as-a-Service. The new major feature is “word embeddings” based recommendations. We are excited to see how the new recommendations will perform with our partners. In addition, we fixed many small bugs, and added some minor improvements. A complete overview can be found in JIRA.
The new version of our recommender system completes 104 issues and significantly improves the recommendations. The most notable improvements are: We improved the keyphrase extraction process in the recommender system, i.e. keyphrases are not stored differently in Lucene. We expect better recommendation effectiveness and are currently running an A/B test. More robust path encoding for search queries (special characters in a URL caused errors) Lucene’s eDismax function is A/B tested (together with Lucene’s standard query parser) Improved queries for CORE recommender system (their system needs queries to be of a certain length; Mr. DLib now just multiplies the queries until they are at least 50 characters) Abstracts and keywords in the XML response of Mr. DLib are enclosed in <![CDATA[ HTML Snippet is improved Read more…
There are two major news coming along with the new version of Mr. DLib’s Recommendation API. JabRef finally uses Mr. DLib for it’s recommender system We have announced this already a while ago, but now, finally, Mr. DLib’s recommendations are available in one of the most popular open-source reference managers, i.e. JabRef. Currently, Mr. DLib enables JabRef users to retrieve a list of related-article recommendations, given a currently selected entry in the reference list (see screenshot). In the long run, we aim for creating personalized recommendations, too. Mr. DLib is not the only provider of recommendations-as-a-service in Academia. Another provider is the CORE project, with whom we partnered now. CORE is offering an API similar to the one we offer. We Read more…
On 28th February, we released version 1.1.1 of Mr. DLib’s recommender system with some minor improvements and bug fixes: Improved 404 error handling for unknown document IDs Fix: The order of authors in the XML was not sorted properly Several internal changes (adjusted logging table; click time is not updated any more for second clicks etc;an automatic tool to add stereotype recommendations)
So far, Mr. DLib’s recommender system was running only on a single server. Consequently, when me messed up something in the development environment, sometimes the production system was affected, i.e. down. From today on, we have two additional dedicated servers running, meaning we have a total of three recommender-system servers, one for the development, one for beta, and one for production.
Docear 1.1 Beta Released: New PDF Metadata Extraction, Better Zotero and Mendeley BibTeX support, and Bug Fixes
If you have tested the Preview of Docear 1.1 you may already know about some of Docear's new features. With your feedback and the mind maps, log files and BibTeX files you shared with us, these features have matured. We are proud to introduce the first (and hopefully only) Beta release of Docear 1.1.
The new key features of Docear 1.1
Improved metadata retrievalThanks to your donations, our student Christoph greatly enhanced Docear's PDF metadata retrieval. For us, it works really great, and with Docear 1.1 Beta the last bugs have been fixed. Btw. if you like what Christoph did, and if you are using LibreOffice, or OpenOffice, please also read our call for donation to develop an add-on for these two text processing tools.
Improved support for Zotero / Mendeley BibTeX files(more…)
Thanks to all the generous donors, our student Christoph could work on an improved PDF metadata retrieval for Docear, and today it's time to present the first preview. The new Docear 1.1 (preview) is able to extract the title of a PDF and fetch appropriate metadata from Google Scholar. Whenever you select a PDF in your mind-map and chose "Create or Update reference", the following new dialog appears. The dialog shows the file name of your PDF file, and the extracted title. In the background, the extracted title is sent to Google Scholar and metadata for the first three search results are shown in the dialog. If the title was extracted incorrectly, you can manually correct it. You may also chose to use the PDF's file name for the search. For instance, when you named your PDF already according to the title, select the radio button with the file name, and the file name is sent as search query to Google Scholar (you may also manually correct the file name before it's sent to Google Scholar). Of course, all other options you already know are still available, such as creating a blank entry, or importing the XMP data of PDFs. Btw. Docear remembers your choice, i.e. when you select to create a blank entry, the option will be pre-selected when open that dialog the next time. It might happen, that your IP will be blocked by Google Scholar when you use the service too frequently. If this happens, a captcha should appear, and after solving it, you should be able to proceed. We did not yet test this thoroughly. Please let us know your experiences.
The precision of our metadata tool depends on two factors, A) the precision of the title extraction and B) the coverage of Google Scholar. According to a recent experiment, title extraction of our tool is around 70%. However, the final result very much depends on the format of your research articles. In my research field (i.e. recommender systems), I would say that our tool extracts the title correctly for about 90% of the articles in my personal library. In addition, almost all articles that are relevant for my research are indexed by Google Scholar (i would estimate, more than 90%). This means, for around 80% of my PDFs the correct metadata is retrieved fully automatically. Given that I provide the title manually, for even more than 90% the metadata may be retrieved. Please let us know your experience (and your research field). (more…)
Update: February 18, 2014: No bugs were reported, as such we declare Docear 1.03 with its recommender system as stable. It can be downloaded on the normal download page.
With Docear 1.0.3 beta we have improved PDF handling, the recommender system, provided some help for new users and enhanced the way how you can access your mind maps online. PDF Handling We fixed several minor bugs with regard to PDF handling. In previous versions of Docear, nested PDF bookmarks were imported twice when you drag & dropped a PDF file to the mind map. Renaming PDF files from within Docear changed the file links in your mind maps but did not change them in your BibTeX file. Both issues are fixed now. To rename a PDF file from within Docear you just have to right-click it in Docear's workspace panel on the left hand side and it is important that the mind maps you have linked the file in, are opened. We know, this is still not ideal, and will improve this in future versions of Docear. Rate Your Recommendations
You already know about our recommender system for academic literature. If you want to help us improving it, you can now rate how good a specific set of recommendations reflects your personal field of interest. Btw. it would be nice if you do not rate a set of recommendations negatively only because it contains some recommendations you received previously. Currently, we have no mechanism to detect duplicate recommendations.