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This paper aims to present an open source math-aware Question Answering System based on Ask Platypus.

The system returns as a single mathematical formula for a natural language question in English or Hindi. These formulae originate from the knowledge-based Wikidata. The authors translate these formulae to computable data by integrating the calculation engine sympy into the system. This way, users can enter numeric values for the variables occurring in the formula. Moreover, the system loads numeric values for constants occurring in the formula from Wikidata.

In a user study, this system outperformed a commercial computational mathematical knowledge engine by 13 per cent. However, the performance of this system heavily depends on the size and quality of the formula data available in Wikidata. As only a few items in Wikidata contained formulae when the project started, the authors facilitated the import process by suggesting formula edits to Wikidata editors. With the simple heuristic that the first formula is significant for the paper, 80 per cent of the suggestions were correct.

This research was presented at the JCDL17 KDD workshop.

Modern mathematicians and scientists of math-related disciplines often use Document Preparation Systems (DPS) to write and Computer Algebra Systems (CAS) to calculate mathematical expressions. Usually, they translate the expressions manually between DPS and CAS. This process is time-consuming and error-prone. The purpose of this paper is to automate this translation. This paper uses Maple and Mathematica as the CAS, and LaTeX as the DPS.

Bruce Miller at the National Institute of Standards and Technology (NIST) developed a collection of special LaTeX macros that create links from mathematical symbols to their definitions in the NIST Digital Library of Mathematical Functions (DLMF). The authors are using these macros to perform rule-based translations between the formulae in the DLMF and CAS. Moreover, the authors develop software to ease the creation of new rules and to discover inconsistencies.

The authors created 396 mappings and translated 58.8 percent of DLMF formulae (2,405 expressions) successfully between Maple and DLMF. For a significant percentage, the special function definitions in Maple and the DLMF were different. An atomic symbol in one system maps to a composite expression in the other system. The translator was also successfully used for automatic verification of mathematical online compendia and CAS. The evaluation techniques discovered two errors in the DLMF and one defect in Maple.

This paper introduces the first translation tool for special functions between LaTeX and CAS. The approach improves error-prone manual translations and can be used to verify mathematical online compendia and CAS.

This purpose of this paper is to discuss some of the problems that exist with Google Scholar, particularly regarding content spam and citation spam.

The paper provides an analysis of how Google Scholar has been duped by real but manipulated documents and reference lists, as well as by fake documents and references. Details of research regarding the duping of Google Scholar is presented and a possible solution is offered.

Researchers showed how easy it was to dupe Google Scholar. In one case, the researchers added invisible words to the first page of one of their conference papers (using the well‐known white letter on white screen/paper technique), and modified the content and bibliography of some of their already published papers, then posted them on the web to see if Google Scholar would bite, i.e. would improve their rank position, and increase the number of citations that the targeted papers received, and the number of papers published by the authors. Google Scholar did bite. While the size of Google Scholar kept growing at an impressive rate, the intellectual growth of the Google Scholar software has been stunted.

The paper makes the point that the best move from Google Scholar would be to realise that the existing metadata created by competent human indexers, cataloguers, librarians and other information professionals for tens of millions of scholarly documents is far superior to the parser's results.

This column aims to explore the potential of personal information management (PIM) and reference management. It focuses on combining the use of PIM and reference management software with mind maps to stimulate the creative and innovative use of information collected

Following a brief review of the literature on the topic per se, awareness is raised of appropriate software and the potential of mind maps. The column is written against the background of research from information behaviour, PIM, mind maps, creativity, innovation and the reflective and evidence‐focused librarian.

There is growing emphasis on more than information literacy skills and the responsible use of information. Creative and innovative use of information is stressed. More intensive use and exploitation of information is necessary to justify the time and effort spent in using PIM and reference management software. Mind maps and collaboration might support creative and innovative use of information, and need to be further exploited. Although software is strong in supporting collaboration, there is very limited built‐in support for the combination of features for information and reference management with features for mind maps.

Although much has been published on developments in PIM and reference management, there is limited coverage of combining PIM and reference management with the use of mind maps and creativity, reflection and noting evidence.

One of the main tasks of a researcher is to properly communicate the results he obtained. The choice of the journal in which to publish the work is therefore very important. However, not all journals have suitable characteristics for a correct dissemination of scientific knowledge. Some publishers turn out to be unreliable and, against a payment, they publish whatever researchers propose. The authors call “predatory journals” these untrustworthy journals. The purpose of this paper is to analyse the incidence of predatory journals in computer science literature and present a tool that was developed for this purpose.

The authors focused their attention on editors, universities and publishers that are involved in this kind of publishing process. The starting point of their research is the list of scholarly open-access publishers and open-access stand-alone journals created by Jeffrey Beall. Specifically, they analysed the presence of predatory journals in the search results obtained from Google Scholar in the engineering and computer science fields. They also studied the change over time of such incidence in the articles published between 2011 and 2015.

The analysis shows that the phenomenon of predatory journals somehow decreased in 2015, probably due to a greater awareness of the risks related to the reputation of the authors.

We focused on computer science field, using a specific sample of queries. We developed a software to automatically make queries to the search engine, and to detect predatory journals, using Beall’s list.