We released a new version of RARD, i.e. RARD II and describe the new release in a preprint published on arXiv. The dataset is available at http://data.mr-dlib.org and the new manuscript is available on arXiv and here in our Blog. The main contribution of this paper is to introduce and describe Read more…
Our paper “Machine Learning vs. Rules and Out-of-the-Box vs. Retrained: An Evaluation of Open-Source Bibliographic Reference and Citation Parsers” got recently accepted and will be presented at Joint Conference on Digital Libraries 2018. Abstract: Bibliographic reference parsing refers to extracting machine-readable metadata, such as the names of the authors, the Read more…
Our paper “Who Did What? Identifying Author Contributions in Biomedical Publications using Naïve Bayes” got recently accepted and will be presented at Joint Conference on Digital Libraries 2018. Abstract: Creating scientific publications is a complex process. It is composed of a number of different activities, such as designing the experiments, Read more…
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 Read more…
We are proud to announce the release of ‘RARD’, the related-article recommendation dataset from the digital library Sowiport and the recommendation-as-a-service provider Mr. DLib. The dataset contains information about 57.4 million recommendations that were displayed to the users of Sowiport. Information includes details on which recommendation approaches were used (e.g. content-based Read more…
In the past few weeks, we published (or received acceptance notices for) a number of papers related to Mr. DLib, research-paper recommender systems, and recommendations-as-a-service. Many of them were written during our time at the NII or in collaboration with the NII. Here is the list of publications: Beel, Joeran, Bela Gipp, Read more…
Six months ago, we launched Mr. DLib’s recommendations-as-a-service for Academia. Time, to look back and provide some numbers: Since September 2016, Mr. DLib´s recommender system has delivered 60,836,800 recommendations to our partner Sowiport, and Sowiport’s users have clicked 91,545 of the recommendations. This equals on overall click-through rate (CTR) of Read more…
Our paper titled “Stereotype and Most-Popular Recommendations in the Digital Library Sowiport” is accepted for publication at the 15th International Symposium on Information Science (ISI) in Berlin. Abstract: Stereotype and most-popular recommendations are widely neglected in the research-paper recommender-system and digital-library community. In other domains such as movie recommendations and hotel 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 can ‘Inverse Document Frequency’ (IDF) be applied to references? In 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 words. We elevate for this problem by using Mendeley’s readership data 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 recommender systems, user modelling, personalisation, and adaptive systems (primarily with 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 – we have funding to pay at least 1 Master or 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 research-paper recommender system, and if you are interested in how 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). This means, we will be in London from September 9 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
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 paper recommender systems. My colleagues and I summarized the findings 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, Denmark from July 7 until July 11 to present the 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 15 minutes of your time, then please continue reading. Participate 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.
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…)
As you probably know, Docear is free and open source. As you might know as well, we do accept donations. Today, we would like to share some statistics with you about the amount of donations we received. Actually, in the past two years, we received 434 US$ (~340€) from from 33 donators. That’s not a lot, given that Docear has several thousands of active users. However, it’s also no surprise and to be honest, we ourselves hardly ever donate for other software tools, so we cannot blame anyone for not donating to Docear (even if he should heavily use it).
The average donation we received was 13.16$ (median was 10$), the highest donation was 50$, the smallest 1$, standard deviation 11.04$. The following chart shows the individual and cumulated donations. Sometimes, we don’t receive any recommendations for several month, sometimes we get multiple ones within a week or so.
This weekend, I had some spare time and I wondered which was the most popular reference manager (and how Docear is doing in comparison). So, I took a list of reference managers from Wikipedia, and checked some statistics on Alexa, Google Trends, and Google Keyword Planner. Since I had the data anyway, I thought I share it with you :-). Please note that this is a quick and dirty analysis. I cannot guarantee that there is not one or two reference managers missing (i just took the list from Wikipedia), and, of course, there are many alternatives to Alexa and Google for measuring the popularity of a reference manager.