This semester my colleague and I are teaching the Machine-Learning module CS7CS4 at Trinity College Dublin. We decided to publish the lecture slides on Slideshare. The slides contain 11 lectures about machine learning, covering aspects such as an introduction to machine Read more…
Two of our papers about weighting citations and terms in the context of user modeling and recommender systems got accepted at the iConference 2017. Here are the abstracts, and links to the pre-print versions: Evaluating the CC-IDF citation-weighting scheme: How effectively Read more…
A demonstration paper about the integration of Mr. DLib in JabRef is accepted for publication at ECIR 2017. We will update this post soon with more information and a pre-print.
Content-based filtering recommendations suffer from the problem that no human quality assessments are taken into account. This means a poorly written paper ppoor would be considered equally relevant for a given input paper pinput as high-quality paper pquality if pquality and ppoor contain the same Read more…
We are glad to announce that JabRef – one of the most popular open-source reference manager – will soon be using Mr. DLib´s recommender system to provide article recommendations to its users. The team of Mr. DLib and JabRef had several Skype calls, Read more…
Our servers for Mr. DLib’s recommender-system as-a-service (RaaS) are now monitored by UptimeRobot, a free monitoring service. You can access all our RaaS server statuses at this URL https://stats.uptimerobot.com/WLL5PUjN6 and you will see a dashboard like this: A click on one Read more…
We have integrated several new recommendation algorithms into Mr. DLib. Some recommendation algorithms are only ought as baselines for our researchers, others hopefully will further increase the effectiveness of Mr. DLib. Overall, Mr. DLib now uses the following recommendation algorithms in Read more…
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 Read more…
Update on 2018-03-15: We Are Hiring 1 Software Engineer & 1 Software Architect / Product Owner for a Recommender-System Business Start-up Updated on 2017-08-14: Here at Docear and Mr. DLib we have many exciting projects in the field of Read more…
We are proud to announce that the social science portal Sowiport is using Mr. DLib´s recommender-system as-a-service as first pilot partner. Sowiport pools and links quality information from domestic and international providers, making it available in one place. Sowiport currently Read more…
Update 2016-01-12: The salary in Tokyo would be around 1.600 US$ per month, not 1.400. 2015 has been a rather quiet year for Docear, but 2016 will be different. We have lots of ideas for new projects, and even better – Read more…
After releasing the Beta some weeks ago, we made some minor adjustments, and consider the current version 1.2 as stable. There are two major improvements and two bad news: Various improvements in the PDF Metadata retrieval function for Google Scholar. If you had Read more…
Docear 1.2 Beta is now available and has two major improvements: A new add-on to import any kind of highlighted text from PDFs This new add-on is a true milestone in the Docear development. Until now, you could only import Read more…
One reason why we originally started the development of Docear was our interest in how people are creating mind-maps and how the information contained in mind-maps could be used for building recommender systems and other user-modeling applications. As a result, we developed Docear’s Read more…
More than half a year ago, we started a call for donation to pay a freelancer who wanted to develop an add-on for LibreOffice and OpenOffice, comparable to Docear4Word. Originally, we estimated that it would take about 2 months before the work was completed, or at least a decent demo version was ready to released. Well, that estimate wasn’t quite precise – the developer hasn’t finished even an alpha version yet. In addition, we are still missing a significant amount of donations to fully pay the developer ($1,000 are missing).
The question arises, how to proceed? We see the following options:
1. Just wait
The freelancer is still working on the add-on. So, most likely he will finish the add-on some day – maybe in 2 months, maybe in 6 months, maybe in a year. However, I have to point out that my satisfaction with the current progress and outcomes are not overwhelming. Personally, I have some doubts that the final add-on will meet the quality expectations I have, and that probably most Docear users have. However, I suggest you get an idea of the add-on yourself. The freelancer sent me a demo version that you can try out. To do so, download the add-on, store it on your hard drive, and open the downloaded file with LibreOffice or OpenOffice. This should open an installation dialog, and you need to confirm all messages in the dialog. After the installation, you should restart Libre/OpenOffice. If you are using OpenOffice, you will have a Docear entry in the menu and in the tool bar (see screenshot below). If you are using LibreOffice, you will only have an entry in the menu.
In the past few weeks, several users reported that Docear could not retrieve metadata more from Google Scholar any more. It took as a while, but now we found and fixed the problem – hopefully. We need your help to Read more…
Our paper “The Architecture and Datasets of Docear’s Research Paper Recommender System” was accepted at the 3rd International Workshop on Mining Scientific Publications (WOSP 2014), which is held in conjunction with the ACM/IEEE Joint Conference on Digital Libraries (JCDL 2014). Read more…
Compared to several other reference managers, Docear lacks a feature to directly import references from the Web. For instance, if you visit the detail page of a research article on a publisher’s website, you might wish to directly import the bibliographic data of that article to Docear. Many publishers offer export options for reference managers such as Endnote, RefWorks, or Zotero. So, how do you do it with Docear?
Fortunately, Docear uses the BibTeX format to store references. BibTeX is a de-facto standard for references that is supported by almost any publisher and any reference manager. So, read on to learn how to import bibliographic data from web-pages in two steps!
As you may know, Docear features a recommender system for academic literature. To find out which papers you might be interested in, the recommender system parses your mind maps and compares them to our digital library with currently about 1.8 million academic articles. While this is helpful and might point you to papers relevant for your general research goals, you will sometimes have to find information on a specific topic and hence search directly.
Based on our knowledge about recommender systems and some user requests, we decided to implement a direct search feature on our digital library. I am very grateful to Keystone, who supported me in visiting Dr. Georgia Kapitsaki at the University of Cyprus (UCY) in Nicosia for a full month to work on this idea. Dr. Kapitsaki’s has already supported us in our work on Docear’s recommender system in July 2013. Her knowledge about the inner mechanics and her ideas on the the search engine were essential for the implementation and the research part of the project.
You can access the search feature from Docear’s ribbon bar (“Search and Filter > Documents > Online search”) or by double-clicking the “Online search” entry in Docear’s workspace panel. Since both the recommender system and the personalized search engine make use of your mind maps. you need to enable the recommendation service in Docear.
After opening the search page, you will see
In the past years, Docear evolved to a powerful software for managing literature and references. However, we have to admit that Docear is still not as user friendly as we would like it to be. This is mainly caused by the workspace concept which is not very intuitive. We are aware of this problem and we would like to fix it. Therefore, we spent the last weeks with a lot of brainstorming and discussions, and we came up with a new concept. We believe it to be more intuitive, and more similar to the concepts you know from other reference managers. In the following, we would like to introduce our ideas for the new workspace concept and some other changes and we ask you for your feedback. Please let us know in the comments if you like our ideas, and how we could make the concept even better.
This is how the new workspace panel would look like after you freshly installed Docear and sorted a few PDFs including annotations (click the image to enlarge it).
There are four main categories in the workspace panel (left).
Finally, after releasing the alpha and beta, today we release Docear 1.1 stable. If you have tried already one of the previous versions, there is not much news. Otherwise, read on.
Thanks to all the generous donors, our student Christoph could work on an improved PDF metadata retrieval for Docear. The new Docear 1.1 is able to extract the title of a PDF and fetch metadata from Google Scholar for that title. To do so, select a PDF in your mind-map and chose “Create or Update reference”, …
… and 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 two 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…)
As some of you might know, I am a PhD student and the focus of my research lies on research-paper recommender systems. Now, I am about to finish an extensive literature review of more than 200 research articles on research Read more…
We recently submitted a paper to UMAP (The Conference on User Modelling, Adaptation, and Personalization). The paper was about how mind-maps could be utilized by information retrieval applications such as recommender systems. The paper got accepted, which means we will be in Aalborg, Read more…
We kindly ask you to participate in a brief study about Docear’s recommender system. Your participation will help us to improve the recommender system, and to secure long-term funding for the development of Docear in general! If you are willing to invest Read more…
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.
Thanks 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.
