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 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. Research publications and patents are an ideal medium to analyze this space, but current datasets of scholarly data cannot be used for such a purpose since they lack a high-quality characterization of the relevant research topics and industrial sectors. In this paper, we introduce the Academia/Industry DynAmics (AIDA) Knowledge Graph, which describes 21M publications and 8M patents according to the research topics drawn from the Computer Science Ontology. 5.1M publications and 5.6M patents are further characterized according to the type of the author’s affiliations and 66 industrial sectors from the proposed Industrial Sectors Ontology (INDUSO). AIDA was generated by an automatic pipeline that integrates data from Microsoft Academic Graph, Dimensions, DBpedia, the Computer Science Ontology, and the Global Research Identifier Database. It is publicly available under CC BY 4.0 and can be downloaded as a dump or queried via a triplestore. We evaluated the different parts of the generation pipeline on a manually crafted gold standard yielding competitive results.

Scientific conferences are essential for developing active research communities, promoting the cross-pollination of ideas and technologies, bridging between academia and industry, and disseminating new findings. Analyzing and monitoring scientific conferences is thus crucial for all users who need to take informed decisions in this space. However, scholarly search engines and bibliometric applications only provide a limited set of analytics for assessing research conferences, preventing us from performing a comprehensive analysis of these events. In this paper, we introduce the AIDA Dashboard, a novel web application, developed in collaboration with Springer Nature, for analyzing and comparing scientific conferences. This tool introduces three major new features: 1) it enables users to easily compare conferences within specific fields (e.g., Digital Libraries) and time-frames (e.g., the last five years), 2) it characterises conferences according to a 14K research topics from the Computer Science Ontology (CSO), and 3) it provides several functionalities for assessing the involvement of commercial organizations, including the ability to characterize industrial contributions according to 66 industrial sectors (e.g., automotive, financial, energy, electronics) from the Industrial Sectors Ontology (INDUSO). We evaluated the AIDA Dashboard by performing both a quantitative evaluation and a user study, obtaining excellent results in terms of quality of the analytics and usability.

In the last few years, chatbots have become mainstream solutions adopted in a variety of domains for automatizing communication at scale. In the same period, knowledge graphs have attracted significant attention from business and academia as robust and scalable representations of information. In the scientific and academic research domain, they are increasingly used to illustrate the relevant actors (e.g., researchers, institutions), documents (e.g., articles, patents), entities (e.g., concepts, innovations), and other related information. Following the same direction, this paper describes how to integrate conversational agents with knowledge graphs focused on the scholarly domain, a.k.a. Scientific Knowledge Graphs. On top of the proposed architecture, we developed AIDA-Bot, a simple chatbot that leverages a large-scale knowledge graph of scholarly data. AIDA-Bot can answer natural language questions about scientific articles, research concepts, researchers, institutions, and research venues. We have developed four prototypes of AIDA-Bot on Alexa products, web browsers, Telegram clients, and humanoid robots. We performed a user study evaluation with 15 domain experts showing a high level of interest and engagement with the proposed agent.