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As a research area for Knowledge-based AI,
our goal is to be at the forefront of research and education in this field,
while also fostering strong relationships with industry partners.
To achieve this, we envisage the following strategic plan.

Build a strong research team: We will recruit and retain top talent in the field of Knowledge-based AI by offering a competitive working environment, high moral standards, and a relaxed atmosphere, as well as providing opportunities for professional development and collaboration. We will also partner with top universities and research institutes to work on exciting projects, and to enable knowledge exchange and accelerate research progress. We will make a concerted effort to recruit, retain and promote under-represented groups in Knowledge-based AI, and to create a culture that is inclusive and respectful of all people. We will follow a set of guidelines to avoid the pitfalls of AI hype within KRDB. The guidelines aim to promote responsible and realistic deployment of AI technologies, emphasising the importance of transparency, accountability, and ethical considerations.

Disseminate research findings: We will prioritise publishing our research findings in high-impact academic journals and presenting our work at major conferences and symposia in the field of Knowledge-based AI.  We will also host public lectures and workshops to engage with local businesses, community groups, and other stakeholders. The dissemination strategy reflects a commitment to sharing the research findings with the wider community and promoting the development and application of AI technologies for the benefit of society.

Foster collaboration: We will actively seek out partnerships and collaborations with industry partners, third sector organisations, and government agencies to share resources and expertise, and to accelerate the pace of innovation. We will invest, through the AI laboratory of which KRDB is a member, in resources and personnel dedicated to relationship-building and partnership development. With this goal in mind, we will adhere to the goals set up by the Smart Specialisation Strategy (RIS3) of the Autonomous Province of Bozen-Bolzano, as specified in the document “Innovazione e Ricerca Alto Adige 2030”, in the specialisation area “Automation and Digital – Smart Processing”. Our activity is also at the core of the Digital Innovation Hub (DIH) of South Tyrol, in which the following strategic actions are being planned to establish the digital backbone to enable smart green regions by 2030: Internet of Things (IoT) and Data Collection, Open Data Hub for Data Sharing and Community, Artificial Intelligence and Data Processing.

Encourage responsible AI: We will promote guidelines for the responsible use of AI, such as ethical considerations, fairness, privacy, and safety. We believe that the research in Knowledge-based AI is exactly what is needed to help ensure that AI is developed and used in ways that align with social and ethical values.

Enhance AI education: We will promote a sustainable comprehensive AI and Data Science education program that includes undergraduate, graduate, and postgraduate level courses. The paths of study will be designed, in cooperation with the institutes of the faculty, to cover the fundamental concepts and techniques of AI and Data Science.

Provide training: We will provide training programs on artificial intelligence for companies, public bodies, and third sector organisations, by offering a range of courses and workshops that cover the latest developments in Knowledge-based AI technology and its applications. Research priorities: Given the context in the Knowledge-based AI and the KRDB vision, we have identified four different research priorities. (1) Basic research on Knowledge Representation, including well established approaches such as Description Logics, new approaches like Knowledge Graphs, and their role as ontology languages. (2) Basic and applied research on the impact of Knowledge-based AI to data-intensive domains, namely on Ontology-based Data Access, Conceptual Modelling, Knowledge-based Data Management, including the impact of Knowledge-based AI on Data Science. A novel research effort on which KRDB wants to invest more is Neuro-symbolic approaches, to support Explainable AI. (3) Basic and applied research on Knowledge in Time and Processes, including Temporal and Action Languages, Process Mining and Analysis, Predictive Process Monitoring, and Artificial Intelligence for Process Science. (4) Basic and applied research on Knowledge and Cognition, with already established expertise on Creativity and Foundational Ontologies, while there is a need to strengthen the areas of Knowledge-driven Natural Language Processing and Multi-Modal Interfaces.

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