The AIDA Dashboard: Analysing Conferences with Semantic Technologies

“The AIDA Dashboard: Analysing Conferences with Semantic Technologies” is a demo paper submitted to the Posters and Demos tracks of the 19th International Semantic Web Conference.   Simone Angioni1, Francesco Osborne2, Angelo A. Salatino2, Diego Reforgiato Recupero1, Enrico Motta2 1 University of Cagliari, Via Università 40, 09124 Cagliari 2 Knowledge Media Institute, The Open University, […]

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AIDA Dashboard

The AIDA Dashboard is a web application that allows users to visualize several kind of analytics about a specific conference (see Figure 1). The backend is developed in Python, while the frontend is in HTML5 and Javascript. The AIDA Dashboard builds on the Academia/Industry DynAmics knowledge graph (AIDA), a large knowledge base describing 14M articles […]

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Databases and Information Systems in the AI Era: Contributions from ADBIS, TPDL and EDA 2020 Workshops and Doctoral Consortium

“Databases and Information Systems in the AI Era: Contributions from ADBIS, TPDL and EDA 2020 Workshops and Doctoral Consortium” is the introductory chapter for the ADBIS , TPDL and EDA 2020 Common Workshops and Doctoral Consortium proceedings as satellite events of the 2020 International Conference on Theory and Practice of Digital Libraries (TPDL2020). Ladjel Bellatreche, […]

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ResearchFlow: Understanding the Knowledge Flow between Academia and Industry

“ResearchFlow: Understanding the Knowledge Flow between Academia and Industry” is a conference paper submitted to Knowledge Engineering and Knowledge Management – 22nd International Conference, EKAW 2020. Angelo Salatino, Francesco Osborne, Enrico Motta Abstract Understanding, monitoring, and predicting the flow of knowledge between academia and industry is of critical importance for a variety of stakeholders, including governments, funding […]

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Integrating Knowledge Graphs for Analysing Academia and Industry Dynamics

Academia and industry are constantly engaged in a joint effort for producing scientific knowledge that will shape the society of the future. Analysing the knowledge flow between them and understanding how they influence each other is a critical task for researchers, governments, funding bodies, investors, and companies. However, current corpora are unfit to support large-scale analysis of the knowledge flow between academia and industry since they lack of a good characterization of research topics and industrial sectors. In this short paper, we introduce the Academia/Industry DynAmics (AIDA) Knowledge Graph, which characterizes 14M papers and 8M patents according to the research topics drawn from the Computer Science Ontology. 4M papers and 5M patents are also classified according to the type of the author’s affiliations (academy, industry, or collaborative) and 66 industrial sectors (e.g., automotive, financial, energy, electronics) obtained from DBpedia. AIDA was generated by an automatic pipeline that integrates several knowledge graphs and bibliographic corpora, including Microsoft Academic Graph, Dimensions, English DBpedia, the Computer Science Ontology, and the Global Research Identifier Database.

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Academia/Industry DynAmics (AIDA) Knowledge Graph

Academia and industry share a complex, multifaceted, and symbiotic relationship. Analysing the knowledge flow between them, understanding which directions have the biggest potential, and discovering the best strategies to harmonise their efforts is a critical task for several stakeholders. While research publications and patents are an ideal media to analyse this space, current datasets of […]

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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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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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