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Joeran Beel
Ussher Assistant Professor, Computer Science

Biography

Joeran Beel is Assistant Professor in Intelligent Systems at Trinity College Dublin and a member of the ADAPT Centre. His research focuses on recommender systems (recommendations as-a-service and recommender-system evaluation) and related technologies such as machine learning and natural language processing. Joeran is further interested in the blockchain/cryptocurrencies, plagiarism detection, and information extraction in the fields of digital libraries, finance, tourism, transport, and healthcare. Joeran has published three books and over 50 peer-reviewed articles and has been awarded various grants for research projects, patent applications, and prototype development as well as some business start-up funding. He is involved in the development of open-source projects such as Mr. DLib, Docear, JabRef, and Freeplane, some of which he initiated. Joeran founded two successful IT start-ups and received multiple awards and prizes for each. Joeran studied and researched in the USA (Berkeley), Australia (Sydney), Germany (Magdeburg & Konstanz), Cyprus (Nicosia) and England (Lancaster). He has an M.Sc. in Project Management, an M.Sc. in Business Information Systems and a PhD in Computer Science. Prior to Trinity College, Joeran worked as IT product manager in the tourism industry (Munich, Germany), and as a postdoctoral researcher at the National Institute of Informatics in Tokyo, Japan.

Publications and Further Research Outputs

Peer-Reviewed Publications

Joeran Beel, Please visit my Google Scholar profile for a complete list of publications, https://scholar.google.de/citations?user=jyXACVcAAAAJ&hl=en, 2018 Journal Article, 2018

Bela Gipp, Corinna Breitinger, Norman Meuschke, and Joeran Beel, CryptSubmit: Introducing Securely Timestamped Manuscript Submission and Peer Review Feedback using the Blockchain, Proceedings of the ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL), 2017 Conference Paper, 2017

Joeran Beel, Corinna Breitinger, and Stefan Langer, Evaluating the CC-IDF citation-weighting scheme: How effectively can 'Inverse Document Frequency' (IDF) be applied to references, Proceedings of the 12th iConference, 2017 Conference Paper, 2017

Joeran Beel, Siddharth Dinesh, Philipp Mayr, Zeljko Carevic, and Jain Raghvendra, Stereotype and Most-Popular Recommendations in the Digital Library Sowiport, Proceedings of the 15th International Symposium of Information Science, 2017 Conference Paper, 2017

Stefan Feyer, Sophie Siebert, Bela Gipp, Akiko Aizawa, and Joeran Beel, Integration of the Scientific Recommender System Mr. DLib into the Reference Manager JabRef, Proceedings of the 39th European Conference on Information Retrieval (ECIR), 2017 Conference Paper, 2017

Joeran Beel, Bela Gipp, and Akiko Aizawa, Mr. DLib: Recommendations-as-a-Service (RaaS) for Academia, Proceedings of the ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL), 2017 Conference Paper, 2017

Joeran Beel, Real-World Recommender Systems for Academia: The Gain and Pain in Developing, Operating, and Researching them, 5th International Workshop on Bibliometric-enhanced Information Retrieval (BIR) at the 39th European Conference on Information Retrieval (ECIR), 2017 Conference Paper, 2017

Joeran Beel, Stefan Langer, and Bela Gipp, TF-IDuF: A Novel Term-Weighting Scheme for User Modeling based on Users' Personal Document Collections, Proceedings of the 12th iConference, 2017 Conference Paper, 2017

Felix Beierle, Akiko Aizawa, and Joeran Beel, Exploring Choice Overload in Related-Article Recommendations in Digital Libraries, 5th International Workshop on Bibliometric-enhanced Information Retrieval (BIR) at the 39th European Conference on Information Retrieval (ECIR), 2017 Conference Paper, 2017

Bela Gipp, Norman Meuschke, Joeran Beel, and Corinna Breitinger, Using the Blockchain of Cryptocurrencies for Timestamping Digital Cultural Heritage, Bulletin of IEEE Technical Committee on Digital Libraries (TCDL), 2017 Journal Article, 2017

Stefan Langer and Joeran Beel, Apache Lucene as Content-Based-Filtering Recommender System: 3 Lessons Learned, 5th International Workshop on Bibliometric-enhanced Information Retrieval (BIR) at the 39th European Conference on Information Retrieval (ECIR), 2017 Conference Paper, 2017

Joeran Beel, Zeljko Carevic, Johann Schaible, and Gabor Neusch, RARD: The Related-Article Recommendation Dataset, D-Lib Magazine, 2017 Journal Article, 2017

Beel, Joeran and Breitinger, Corinna and Langer, Stefan and Lommatzsch, Andreas and Gipp, Bela, Towards Reproducibility in Recommender-Systems Research, User Modeling and User-Adapted Interaction (UMUAI), 26, (1), 2016, p69-101 Journal Article, 2016 DOI

Joeran Beel and Bela Gipp and Stefan Langer and Corinna Breitinger, Research Paper Recommender Systems: A Literature Survey, International Journal on Digital Libraries, (4), 2016, p305â€"338 Journal Article, 2016 DOI

Stability Evaluation of Event Detection Techniques for Twitter in, editor(s)Bostrm, Henrik and Knobbe, Arno and Soares, Carlos and Papapetrou, Panagiotis , Advances in Intelligent Data Analysis XV, Springer, 2016, pp368--380 , [Weiler, Andreas and Beel, Joeran and Gipp, Bela and Grossniklaus, Michael] Book Chapter, 2016 DOI

Joeran Beel and Stefan Langer, A Comparison of Offline Evaluations, Online Evaluations, and User Studies in the Context of Research-Paper Recommender Systems, Proceedings of the 19th International Conference on Theory and Practice of Digital Libraries (TPDL), edited by Kapidakis, Sarantos and Mazurek, Cezary and Werla, Marcin , 9316, 2015, pp153-168 Conference Paper, 2015 DOI

Beel, Joeran and Langer, Stefan and Kapitsaki, Georgia M. and Breitinger, Corinna and Gipp, Bela, Exploring the Potential of User Modeling based on Mind Maps, Proceedings of the 23rd Conference on User Modelling, Adaptation and Personalization (UMAP), edited by Francesco Ricci and Kalina Bontcheva and Owen Conlan and Séamus Lawless , 9146, Springer, 2015, pp3-17 Conference Paper, 2015 DOI

Joeran Beel, Utilizing Mind-Maps for Information Retrieval and User Modelling, Proceedings of the 22nd Conference on User Modelling, Adaption, and Personalization (UMAP), edited by Vania Dimitrova and Tsvi Kuflik and David Chin and Francesco Ricci and Peter Dolog and Geert-Jan Houben , 8538, Springer, 2014, pp301-313 Conference Paper, 2014 DOI

Joeran Beel and Stefan Langer and Bela Gipp and Andreas NÃ"rnberger, The Architecture and Datasets of Docear's Research Paper Recommender System, D-Lib Magazine, 20, (11/12), 2014 Journal Article, 2014 DOI

Stefan Langer and Joeran Beel, The Comparability of Recommender System Evaluations and Characteristics of Docear's Users, Proceedings of the Workshop on Recommender Systems Evaluation: Dimensions and Design (REDD) at the 2014 ACM Conference Series on Recommender Systems (RecSys), CEUR-WS, 2014, pp1--6 Conference Paper, 2014

Joeran Beel and Stefan Langer and Marcel Genzmehr and Andreas NÃ"rnberger, Persistence in Recommender Systems: Giving the Same Recommendations to the Same Users Multiple Times, Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries (TPDL 2013), Valletta, Malta, edited by Trond Aalberg and Milena Dobreva and Christos Papatheodorou and Giannis Tsakonas and Charles Farrugia , 8092, Springer, 2013, pp390--394 Conference Paper, 2013

Beel, Joeran and Langer, Stefan and Genzmehr, Marcel, Sponsored vs. Organic (Research Paper) Recommendations and the Impact of Labeling, Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries (TPDL 2013), Valletta, Malta, edited by Trond Aalberg and Milena Dobreva and Christos Papatheodorou and Giannis Tsakonas and Charles Farrugia , 2013, pp395--399 Conference Paper, 2013

Joeran Beel and Stefan Langer and Andreas NÃ"rnberger and Marcel Genzmehr, The Impact of Demographics (Age and Gender) and Other User Characteristics on Evaluating Recommender Systems, Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries (TPDL 2013), Valletta, Malta, edited by Trond Aalberg and Milena Dobreva and Christos Papatheodorou and Giannis Tsakonas and Charles Farrugia , Springer, 2013, pp400--404 Conference Paper, 2013

Mario Lipinski and Kevin Yao and Corinna Breitinger and Joeran Beel and Bela Gipp, Evaluation of Header Metadata Extraction Approaches and Tools for Scientific PDF Documents, Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries (JCDL'13), V, 2013, pp385-386 Conference Paper, 2013

Joeran Beel and Stefan Langer and Marcel Genzmehr, Docear4Word: Reference Management for Microsoft Word based on BibTeX and the Citation Style Language (CSL), Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL'13), ACM, 2013, pp445-446 Conference Paper, 2013 DOI

Joeran Beel and Stefan Langer and Marcel Genzmehr and Christoph MÃ"ller, Docears PDF Inspector: Title Extraction from PDF files, Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL'13), ACM, 2013, pp443-444 Conference Paper, 2013 DOI

Joeran Beel and Stefan Langer and Marcel Genzmehr and Bela Gipp and Corinna Breitinger and Andreas NÃ"rnberger, Research Paper Recommender System Evaluation: A Quantitative Literature Survey, Proceedings of the Workshop on Reproducibility and Replication in Recommender Systems Evaluation (RepSys) at the ACM Recommender System Conference (RecSys), ACM, 2013, pp15-22 Conference Paper, 2013 DOI

Joeran Beel and Stefan Langer and Marcel Genzmehr and Andreas NÃŒrnberger, Introducing Docear's Research Paper Recommender System, Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL'13), ACM, 2013, pp459-460 Conference Paper, 2013 DOI

Joeran Beel and Stefan Langer and Marcel Genzmehr and Bela Gipp and Andreas NÃ"rnberger, A Comparative Analysis of Offline and Online Evaluations and Discussion of Research Paper Recommender System Evaluation, Proceedings of the Workshop on Reproducibility and Replication in Recommender Systems Evaluation (RepSys) at the ACM Recommender System Conference (RecSys), 2013, pp7-14 Conference Paper, 2013 DOI

Beel, Joeran and Gipp, Bela, Academic search engine spam and Google Scholar's resilience against it, Journal of Electronic Publishing, 13, (3), 2010 Journal Article, 2010 DOI

Bela Gipp and Joeran Beel and Ivo Roessling, ePassport: The World's New Electronic Passport, Scotts Valley (USA), Createspace, 2007 Book, 2007

Non-Peer-Reviewed Publications

Joeran Beel, Virtual Citation Proximity (VCP): Calculating Co-Citation-Proximity-Based Document Relatedness for Uncited Documents with Machine Learning, 2017 Working Paper, 2017 DOI

Joeran Beel, Towards Effective Research-Paper Recommender Systems and User Modeling based on Mind Maps, PhD Thesis. Otto-von-Guericke Universit{\"a, 2015 Journal Article, 2015

Research Expertise

Description

Recommender Systems, User Modelling, Information Retrieval, Machine Learning, Artificial Intelligence, Information Extraction, Natural Language Processing, Text Mining, Citation Analysis, Bibliometrics, Altmetrics, Scientometrics, Plagiarism Detection, Blockchain, Digital Libraries, Digital Humanities, Finance (FinTech), Legal, Tourism, Medical

Projects

  • Title
    • DISCANT: Domain-Independent Semantic Annotation of the Text
  • Summary
    • Surrounded by huge and exponentially growing volume of information of various nature, every day we face challenges with keeping track of the latest news in a domain of interest or finding good-quality answers to specific questions. The problem of information overload has been addressed by modern information systems and search engines, however, their capabilities are strongly limited by using unstructured textual formats for exchanging and storing information. Textual documents often contain a lot of concrete facts and quantifiable information, but the communication of these facts and information through human languages renders them inaccessible to machines. This "semantic bottleneck" problem substantially slows down communication and knowledge propagation in the world. In order to equip machines with the ability of effective processing of the text, DISCANT ("Domain-Independent SemantiC ANnotation of the Text") aims at creating a comprehensive framework for semantic annotation of textual documents of arbitrary domains, such as scientific papers, legal documents, customer reviews or clinical trial reports. We will develop approaches, methods and tools for two classes of solutions: an environment for discovering entity and relation types in a given domain and a system for automated semantic annotation of the text. The project proposes a novel approach based on a combination of unsupervised natural language processing and machine learning techniques. DISCANT will advance machine understanding of the text, contributing to the release of important knowledge buried in textual documents, the creation of machine-readable knowledge repositories and more effective solutions for semantic search and personal recommendations. As a consequence, DISCANT will equip the consumers of textual documents with better tools for overcoming data deluge and information overload, enabling them to make better-quality, data-driven decisions.
  • Funding Agency
    • EU / SFI
  • Date From
    • 01/07/2017
  • Date To
    • 30/06/2019
  • Title
    • Mr. DLib
  • Summary
    • Mr. DLib http://mr-dlib.org is a non-profit open-source project to provide recommendations-as-a-service for research articles, call for papers, and academic news. Mr. DLib was originally developed as a Machine-readable Digital Library at the University of California, Berkeley and is nowadays run by researchers, among others, from the Trinity College Dublin (Ireland), and the University of Konstanz (Germany). Mr. DLib offers three services: 1. Recommendations-as-a-service (RaaS) for operators of academic products Operators of academic products such as digital libraries or reference management tools can easily integrate a recommender system in their own products with Mr. DLib. To do so, operators need no knowledge about recommender systems. In addition, the effort of integrating Mr. DLib's recommendations-as-a-service ranges from a few hours to a few days, compared to several months of work for implementing one's own recommender system. Operators have the choice to recommend only their own content (e.g. research articles) to their users, or content from Mr. DLib's content providers. 2. Academic outreach for providers of academic content Mr. DLib helps content providers such as universities, publishers, conference organizers, and open access repositories to reach out to students and researchers and win them as new visitors, readers, users, or customers. For instance, publishers may gain new readers for their publications; universities may attract new students for their courses; and conference organizers may attract new submissions. Mr. DLib is doing so by recommending the providers' content - e.g. call for papers, course descriptions, or research articles - to the users of Mr. DLib's RaaS partners. 3. A real-world research environment for students and researchers Mr. DLib shares its data, i.e. we allow external researchers and students to conduct their research with Mr. DLib (as long as the privacy of our partners and users is ensured). In the short-term, we publish datasets that contain the documents indexed by Mr. DLib and information about the delivered recommendations. Our long-term goal is to establish a "living lab" that allows external researchers to evaluate their recommendation algorithm in real-time with Mr. DLib and its partners. Mr. DLib is an ideal environment for research about recommender systems and digital libraries as well as research in the field of machine learning, citation analysis, natural language processing and several related disciplines. So, if you are interested in conducting research that has a real impact on how other researchers work.
  • Title
    • Docear
  • Summary
    • Docear http://docear.org is a unique solution to academic literature management, i.e. it helps you organizing, creating, and discovering academic literature. Among others, Docear offers: 1. A single-section user-interface that allows the most comprehensive organization of your literature. With Docear, you can sort documents into categories; you can sort annotations (comments, bookmarks, and highlighted text from PDFs) into categories; you can sort annotations within PDFs; and you can view multiple annotations of multiple documents, in multiple categories - at once. 2. A 'literature suite concept' that combines several tools in a single application (pdf management, reference management, mind mapping, ). This allows you to draft your own papers, assignments, thesis, etc. directly in Docear and copy annotations and references from your collection directly into your draft. 3. A recommender system that helps you to discover new literature: Docear recommends papers which are free, in full-text, instantly to download, and tailored to your information needs.
  • Title
    • CitePlag
  • Summary
    • CitePlag http://citeplag.org/ is a prototype of a hybrid Plagiarism Prevention and Detection System cooperatively developed by the Information Science Group at the University of Konstanz and Prof. Joeran Beel at the Trinity College Dublin. CitePlag implements the Citation-based Plagiarism Detection (CbPD) approach, which was initially introduced in the doctoral thesis of Bela Gipp. Details on the algorithms implemented in CitePlag can be found here. The current prototype was developed in cooperation with students from the HTW, Berlin Compared to existing approaches for plagiarism detection, the CitePlag prototype does not consider textual similarity alone but uses citation patterns within academic documents as a unique, language-independent fingerprint to identify semantic similarity. This feature for the first time enables automated detection of strongly disguised plagiarism forms, including paraphrases, translated plagiarism, and even idea plagiarism. The suitability of the CbPD approach in detecting disguised plagiarism was first demonstrated on the plagiarized thesis of former German defense minister Karl- Theodor zu Guttenberg [PDF]. While conventional detection approaches could not identify a single instance of translated plagiarism in the thesis, the CbPD approach detected 13 of the 16 translated plagiarisms. The effectiveness of the method was further demonstrated when applied to the works of multiple authors and various plagiarism styles in the VroniPlag collection. Evaluations of real-world plagiarism showed that plagiarists commonly disguise academic misconduct by paraphrasing copied text, but often do not substitute or rearrange the citations copied from the source document. Most recently, the practicability of the CbPD approach was demonstrated by analyzing 185,000 publications in the comprehensive bioscience full-text database PubMed Central. The CbPD algorithms allowed the identification of several plagiarism cases that were non-machine-detectable using today's prevalent methods. [PDF]. As a result, several publications were retracted, including a fraudulent medical study. While the CbPD approach offers unique benefits, it should be seen as a supplement not a replacement to existing software-based plagiarism detection methods, since text-based and citation-based plagiarism detection approaches complement each other. The CitePlag prototype represents a visualization of concepts and algorithms developed by the Information Science Group around the idea of Citation-based Plagiarism Detection (CbPD).
  • Title
    • OriginStamp
  • Summary
    • OriginStamp https://originstamp.org is a non-commercial trusted timestamping service that can be used free of charge and anonymously. Trusted timestamping enables you to prove that you were the originator of an information (e.g. text or any other media file) at a certain time. Trusted timestamping isn't new. Even before computers existed information could be hashed and the hash could be published in a newspaper. However, OriginStamp.org allows you to anonymously timestamp information in a decentralized and tamperproof way within seconds. It only takes a few clicks and it's completely free.

Keywords

ARTIFICIAL INTELLIGENCE; Bibliometrics; Bitcoin; Blockchain; computer-assisted language learning; Cross-Language Information Retrieval; Digital Humanities; Digital Libraries; FinTech; INFORMATION EXTRACTION; INFORMATION-RETRIEVAL; MACHINE LEARNING; Natural Language Processing; Personalisation; Personalisation and User-Centric Adaptivity; Personalised Information Retrieval; Plagiarism Detection; Recommender Systems; Scientometrics; Text Mining; TOURISM; User Modeling

Recognition

Awards and Honours

DAAD Postdoctoral Fellowship (FIT Weltweit) 2015

5th prize in B-P-W business plan contest 2013

1st prize in ego.BUSINESS business plan contest 2012

Best graduate of the Computer Science Department 2007/08 2008

2nd prize in business plan contest 'B-P-W' 2003

2nd winner at "Jugend-forscht", Germany's most reputable research contest for youth (national wide round) 2002

Award for an outstanding microelectronic equipment development by the Association of German Electrical Engineers 2001