KC-Viz: a novel approach to visualizing and nNavigating ontologies

There is empirical evidence that the user interaction metaphors used in ontology engineering toolkits are largely inadequate and that novel interactive frameworks for human ontology interaction are needed. Here we present a novel tool for visualizing and navigating ontologies, called KC Viz, which exploits an innovative ontology summarization method to support a ’middleout ontology browsing’ approach, where it becomes possible to navigate ontologies starting from the most information-rich nodes (i.e., key concepts). This approach is similar to map-based visualization and navigation in Geographical Information Systems, where, e.g., major cities are displayed more prominently than others, depending on the current level of granularity

SAVE-SD 2017: Third Workshop on Semantics, Analytics and Visualisation: Enhancing Scholarly Data

The third edition of the Workshop on Semantics, Analytics and Visualisation: Enhancing Scholarly Data (SAVE-SD 2017) is taking place in Perth, Australia on the 3rd of April 2017, co-located with the 26th International World Wide Web Conference. The main goal of the workshop is to provide a venue for researchers, publishers and other companies to engage in discussions about semantics, analytics and visualisations on scholarly data

A quantitative and qualitative open citation analysis of retracted articles in the humanities

In this article, we show and discuss the results of a quantitative and qualitative analysis of open citations to retracted publications in the humanities domain. Our study was conducted by selecting retracted papers in the humanities domain and marking their main characteristics (e.g., retraction reason). Then, we gathered the citing entities and annotated their basic metadata (e.g., title, venue, subject, etc.) and the characteristics of their in-text citations (e.g., intent, sentiment, etc.). Using these data, we performed a quantitative and qualitative study of retractions in the humanities, presenting descriptive statistics and a topic modeling analysis of the citing entities' abstracts and the in-text citation contexts. As part of our main findings, we noticed that there was no drop in the overall number of citations after the year of retraction, with few entities which have either mentioned the retraction or expressed a negative sentiment toward the cited publication. In addition, on several occasions, we noticed a higher concern/awareness when it was about citing a retracted publication, by the citing entities belonging to the health sciences domain, if compared to the humanities and the social science domains. Philosophy, arts, and history are the humanities areas that showed the higher concern toward the retraction

Review of: "Finding citations for PubMed: A large-scale comparison between five open access data sources"

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Review of: "Comparison of self-citation patterns in WoS and Scopus databases based on national scientific production in Slovenia (1996-2020)"

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Review of "The Four Pillars of Research Software Engineering"

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Review of: "CRAFTS: Configurable REST APIs For Triple Stores"

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Clustering citation distributions for semantic categorization and citation prediction

In this paper we present i) an approach for clustering authors according to their citation distributions and ii) an ontology, the Bibliometric Data Ontology, for supporting the formal representation of such clusters. This method allows the formulation of queries which take in consideration the citation behaviour of an author and predicts with a good level of accuracy future citation behaviours. We evaluate our approach with respect to alternative solutions and discuss the predicting abilities of the identified clusters