We propose the new cloud-based service OpenResearch for managing and analyzing data about scientific events such as conferences and workshops in a persistent and reliable way. This includes data about scientific articles, participants, acceptance rates, submission numbers, impact values as well as organizational details such as program committees, chairs, fees and sponsors. OpenResearch is a centralized repository for scientific events and supports researchers in collecting, organizing, sharing and disseminating information about scientific events in a structured way. An additional feature currently under development is the possibility to archive web pages along with the extracted semantic data in order to lift the burden of maintaining new and old conference web sites from public research institutions. However, the main advantage is that this cloud-based repository enables a comprehensive analysis of conference data. Based on extracted semantic data, it is possible to determine quality estimations, scientific communities, research trends as well the development of acceptance rates, fees, and number of participants in a continuous way complemented by projections into the future. Furthermore, data about research articles can be systematically explored using a content-based analysis as well as citation linkage. All data maintained in this crowd-sourcing platform is made freely available through an open SPARQL endpoint, which allows for analytical queries in a flexible and user-defined way.
CfP Manager @cite_4 is an information extraction tool for CfPs from the computer science domain. It extracts metadata of events from the unstructured text of CfPs focusing on keyword detection. Because of the different representations and terminologies across research communities, this approach requires domain specific implementations. CfP Manager neither supports comprehensive data analysis nor data curation workflows involving multiple stakeholders. Hurtado proposed an approach based on user profiles, which takes a scholar's recent publication list and recommends related CfPs using content analysis @cite_6 . presented a classification method to filter CfPs by social tagging @cite_1 . proposed another approach to classify CfPs by implementing three different methods but focus on comparing the classification methods rather than services to improve scientific communication @cite_5 . Paperleap http: www.paperleap.com is a commercial tool for analyzing CfPs and provide recommendations for users. The two well-known digital libraries in the field of computer science provide event support for researchers: ACM http: www.acm.org conferences hosts webpages for their conferences and IEEE upcoming events with deadlines9
@cite_1 @cite_5 @cite_4 @cite_6
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Call for papers (CFPs) are notifications of academic events that invite researchers to submit their works. Traditionally, CFPs are handed out to researchers by mailing lists and web pages. With the number of conferences increasing, finding, reading and filtering out relevant CFPs become time consuming and need the assistance from information retrieval techniques. In this paper, we employ collaborative filtering to match relevant CFPs to researchers. Non-personalized, neighborhood-based and class-based methods are applied in CFP recommendation. We also propose a hybrid approach that utilizes conference series and submission deadlines of CFPs. The experiments on Wiki CFP data set show that the class-based method outperforms both neighborhood-based and non-personalized methods, whereas the proposed hybrid approach has the best overall performance. This paper proposes a system called CFP Manager specialized on IT field and designed to ease the process of searching conference suitable to one's need. At present, the handling of CFP faces two problems: for emails, the huge quantity of CFP received can be easily skimmed through. For websites, the reviewing of some of the main CFP aggregators available online points out the lack of usable criteria. This system proposes to answer to these problems via its architecture consisting of three components: firstly an Information Extraction module extracting relevant information as date, location, etc... from CFP using rule based text mining algorithm. The second component enriches the now extracted data with external one from ontology models. Finally the last one displays the said data and allows the end user to perform complex queries on the CFP dataset and thus allow him to only access to CFP suitable for him. In order to validate the authors' proposal, they eventually process the well-known precision recall metric on our information extraction component with an average of 0.95 for precision and 0.91 for recall on three different 100 CFP dataset. This paper finally discusses the validity of our approach by confronting our system for different queries with two systems already available online WikiCFP and IEEE Conference Search and basic text searching approach standing for searching in an email box. On a 100 CFP dataset with the wide variety of usable data and the possibility to perform complex queries we surpass basic text searching method and WikiCFP by not returning the false positive usually returned by them and find a result close to the IEEE system. Due to the increasing number of conferences, researchers need to spend more and more time browsing through the respective calls for papers CFPs to identify those conferences which might be of interest to them. In this paper we study several content-based techniques to filter CFPs retrieved from the web. To this end, we explore how to exploit the information available in a typical CFP: a short introductory text, topics in the scope of the conference, and the names of the people in the program committee. While the introductory text and the topics can be directly used to model the document e.g. to derive a tf-idf weighted vector, the names of the members of the program committee can be used in several indirect ways. One strategy we pursue in particular is to take into account the papers that these people have recently written. Along similar lines, to find out the research interests of the users, and thus to decide which CFPs to select, we look at the abstracts of the papers that they have recently written. We compare and contrast a number of approaches based on the vector space model and on generative language models.