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I launched an online course on Instagram, here is my lesson learned

THIS IS A DRAFT – WORK IN PROGRESS During the COVID-19 outbreak many people relied more and more on web technologies like such as video calls and social networks, to fulfil their social needs. I decided to design and release an online course on Instagram so that users while consuming content could be engaged in […]

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Ontology Extraction and Usage in the Scholarly Knowledge Domain

Ontologies of research areas have been proven to be useful in many application for analysing and making sense of scholarly data. In this chapter, we present the Computer Science Ontology (CSO), which is the largest ontology of research areas in the field of Computer Science, and discuss a number of applications that build on CSO, to support high-level tasks, such as topic classification, metadata extraction, and recommendation of books.

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1st​ Workshop on Scientific Knowledge Graphs (SKG2020)

In the last decade, we experienced an urgent need for a flexible, context-sensitive, fine-grained, and machine-actionable representation of scholarly knowledge and corresponding infrastructures for knowledge curation, publishing and processing. Such technical infrastructures are becoming increasingly popular in representing scholarly knowledge as structured, interlinked, and semantically rich Scholarly Knowledge Graphs (SKG).
The 1st​ Workshop on Scientific Knowledge Graphs (SKG2020) aims at bringing together researchers and practitioners from different fields (including, but not limited to, Digital Libraries, Information Extraction, Machine Learning, Semantic Web, Knowledge Engineering, Natural Language Processing, Scholarly Communication, and Bibliometrics) in order to explore innovative solutions and ideas for the production and consumption of Scientific Knowledge Graphs (SKGs).

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Computing Automorphic Numbers

In our lab, we like to tease each other with fancy riddles. In our kitchen, we have a large wooden box, filled with some chocolates and locked by a 4-digits lock. Those who crave for some sugar will just need to solve the riddle and unlock the box.
The last few riddles involved a particular family of numbers which are called automorphic, and the complexity of such riddles was increasing with the size of those numbers in terms of the number of digits. For instance, in the last riddle, we were asked to compute a number with 44444 digits, requiring an enormous computational power.
In this post, I will show how I developed the algorithm that allowed me to solve the riddle.

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1st Smart City and Robotic Challenge (SCiRoC 2019)

Last week — 18th to 21st September 2019 — the first International Competition on Smart Cities and Robotics took place in Milton Keynes (UK). Different teams from Spain, UK, Germany, France, Portugal and others took part in this competition. As the name suggests, SCiRoC aims at bringing robots in the context of smart cities. Indeed, their primary objective was to interact both with smart cities infrastructures, such as the MK Data Hub, and citizens.

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How to use the CSO Classifier in other domains

Being able to characterise research papers according to their topics enables a multitude of high-level applications such as i) categorise proceedings in digital libraries, ii) semantically enhance the metadata of scientific publications, iii) generate recommendations, iv) produce smart analytics, v) detect research trends, and others.

In our recent work, we designed and developed an unsupervised approach to automatically classify research papers according to an ontology of research areas in the field of Computer Science. This approach uses well-known technologies from the field of Natural Language Processing which makes it easily generalisable. In this article, we will show how we can customise the CSO Classifier and apply it to other fields of Science.

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The Computer Science Ontology: A Comprehensive Automatically-Generated Taxonomy of Research Areas

Ontologies of research areas are important tools for characterising, exploring, and analysing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 14K topics and 162K semantic relationships. It was created by applying the Klink-2 algorithm on a very large dataset of 16M scientific articles. CSO presents two main advantages over the alternatives: i) it includes a very large number of topics that do not appear in other classifications, and ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO, we have also released the CSO Classifier, a tool for automatically classifying research papers, and the CSO Portal, a web application that enables users to download, explore, and provide granular feedback on CSO. Users can use the portal to navigate and visualise sections of the ontology, rate topics and relationships, and suggest missing ones. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various research communities engaged with scholarly data.

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Integrating Knowledge Graphs for Comparing the Scientific Output of Academia and Industry

Analysing the relationship between academia and industry allows us to understand how the knowledge produced by the universities is being adopted and enriched by the industrial sector, and ultimately affects society through the release of relevant products and services. In this paper, we present a preliminary approach to assess and compare the research outputs of academia and industry. This solution integrates data from several knowledge graphs describing scientific articles (Microsoft Academics Graph), research topics (Computer Science Ontology), organizations (Global Research Identifier Database), and types of industry (DBpedia). We focus on the Semantic Web as exemplary field and report several insights regarding the different behaviours of academia and industry, and the types of industries most active in this field.

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