@article {828,
	title = {Chatbots for Cultural Venues: A Topic-Based Approach},
	journal = {Algorithms},
	volume = {16},
	year = {2023},
	month = {jul},
	pages = {339},
	abstract = {<p>Digital assistants{\textemdash}such as chatbots{\textemdash}facilitate the interaction between persons and machines and are increasingly used in web pages of enterprises and organizations. This paper presents a methodology for the creation of chatbots that offer access to museum information. The paper introduces an information model that is offered through the chatbot, which subsequently maps the museum{\textquoteright}s modeled information to structures of DialogFlow, Google{\textquoteright}s chatbot engine. Means for automating the chatbot generation process are also presented. The evaluation of the methodology is illustrated through the application of a real case, wherein we developed a chatbot for the Archaeological Museum of Tripolis, Greece.</p>
},
	keywords = {chatbots, cultural venues, Museums, RDF triples, topics},
	doi = {10.3390/a16070339},
	url = {https://doi.org/10.3390/a16070339},
	author = {Vasilis Bouras and Dimitris Spiliotopoulos and Dionisis Margaris and Costas Vassilakis and Konstantinos Kotis and Angeliki Antoniou and George Lepouras and Manolis Wallace and Vassilis Poulopoulos}
}
@conference {717,
	title = {Citizen Engagement for Transparent and Accountable Policy Modelling},
	booktitle = {Proceedings of the 11th International Conference on Management of Digital EcoSystems (MEDES{\textquoteright}19)},
	year = {2019},
	publisher = {ACM},
	organization = {ACM},
	abstract = {In the modern information society, learning is no longer just about obtaining factual knowledge, but more about general skills on how and where to apply available knowledge and obtain new knowledge in order to solve new problems. Such skills include abilities to connect and organize ideas, fill gaps in knowledge structures, evaluate evidence, argue with new information, test and modify, predict, clarify, generate questions, learn new concepts, make unexpected connections, reflect, analyze, synthesize and loop back. This work presents the Immersion framework, a digital ecosystem for adaptive smart learning environments for interactive mixed reality driven methods to foster learners{\textquoteright} self-regulating skill development.},
	keywords = {Game-based Interaction, Learner Collaboration, Mixed Reality, Mobile Learning, Self-regulated Learning, Technology Enhanced Learning},
	author = {Dimitris Spiliotopoulos and Dionisis Margaris and Costas Vassilakis}
}
@conference {marg15a,
	title = {A Collaborative Filtering Algorithm with Clustering for Personalized Web Service Selection in Business Processes},
	booktitle = {Proceedings of RCIS 2015},
	year = {2015},
	month = {5},
	abstract = {Recommender systems aim to propose items that are expected to be of interest to the users. As one of the most successful approaches to building recommender systems, collaborative filtering exploits the known preferences of a group of users to formulate recommendations or predictions of the unknown preferences for other users. In many cases, collaborative filtering algorithms handle complex items, which are described using hierarchical tree structures containing rich semantic information. In order to make accurate recommendations on such items, the related algorithms must examine all aspects of the available semantic information. Thus, when collaborative filtering techniques are employed to adapt the execution of business processes, they must take into account the services{\textquoteright} Quality of Service parameters, so as to generate recommendations tailored to the individual user needs. In this paper, we present a collaborative filtering-based algorithm which takes into account the web services{\textquoteright} QoS parameters in order to tailor the execution of business processes to the preferences of users. An offline clustering technique is also introduced for supporting the efficient and scalable execution of proposed algorithm under the presence of large repositories of sparse data.},
	keywords = {business processes, clustering, collaborative filtering, hierarchical tree, performance, Quality of service, Web services},
	doi = {10.1109/RCIS.2015.7128877},
	author = {Dionisis Margaris and P. Georgiadis and Costas Vassilakis}
}
