@conference {821,
	title = {RoG: A Pipeline for Automated Sensitive Data Identification and Anonymisation},
	booktitle = {Proceedings of the 2023 IEEE CSR Workshop on Data Science for Cyber Security (DS4CS)},
	year = {2023},
	abstract = {<p>Nowadays, the amount of data available online is constantly increasing. This data may contain sensitive or private information that can expose the person behind the data or be misused by malicious actors for identity theft, stalking, and other nefarious purposes. There is thus, a growing need to protect individuals{\textquoteright} privacy and prevent data breaches in several application domains. Protecting data privacy though, is a complex and multifaceted issue that involves a range of legal, ethical, and technical considerations. In this paper, we discuss the challenges associated with data protection, the role of automated tools, and the effectiveness of identifying and anonymising sensitive data. We then, propose a fully-automated process for sensitive data identification and anonymisation, based on Natural Language Processing (NLP) techniques, that can be applied both in big diverse datasets and to a wide range of domains.</p>
},
	keywords = {anonymisation, automated process, k-anonymity, NER, NLP, pipeline, sensitive/private data},
	author = {Sotiris Nikoletos and Stefanos Vlachos and Efstathios Zaragkas and Costas Vassilakis and Christos Tryfonopoulos and Paraskevi Raftopoulou}
}
@conference {778,
	title = {{\textquotedblleft}Talking{\textquotedblright} Triples to Museum Chatbots},
	booktitle = {Proceedings of the 22nd International Conference on Human-Computer Interaction, volume "Culture and Computing"},
	year = {2020},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {<p>The paper presents recent work on the design and development of AI chatbots for museums using Knowledge Graphs (KGs). The utilization of KGs as a key technology for implementing chatbots raises not only issues related to the representation and structuring of exhibits{\textquoteright} knowledge in suitable formalism and models, but also issues related to the translation of natural language dialogues to and from the selected technology for the formal representation and structuring of information and knowledge. Moreover, such a translation must be as transparent as possible to visitors, towards a realistic human-like question-answering process. The paper reviews and evaluates a number of recent approaches for the use of KGs in developing AI chatbots, as well as key tools that provide solutions for natural language translation and the querying of Knowledge Bases and Linked Open Data sources. This evaluation aims to provide answers to issues that are identified within the proposed MuBot approach for designing and implementing AI chatbots for museums. The paper also presents Cretan MuBot, the first experimental KG/Ontology-based AI chatbot of the MuBot Platform, which is under development in the Heracleum Archaeological Museum.</p>
},
	keywords = {chatbots, knowledge graphs, Museums, NLP, RDF triples},
	doi = {10.1007/978-3-030-50267-6_22},
	author = {Savvas Varitimiadis and Konstantinos Kotis and Dimitris Spiliotopoulos and Costas Vassilakis and Dionisis Margaris}
}
