@article{trichopoulos_tangible_2022,

title = {Tangible and Personalized DS Application Approach in Cultural Heritage: The CHATS Project},

author = {Giorgos Trichopoulos and John Aliprantis and Markos Konstantakis and Konstantinos Michalakis and George Caridakis},

url = {https://www.mdpi.com/2073-431X/11/2/19},

doi = {10.3390/computers11020019},

issn = {2073-431X},

year  = {2022},

date = {2022-01-01},

urldate = {2024-11-14},

journal = {Computers},

volume = {11},

number = {2},

pages = {19},

abstract = {Storytelling is widely used to project cultural elements and engage people emotionally. Digital storytelling enhances the process by integrating images, music, narrative, and voice along with traditional storytelling methods. Newer visualization technologies such as Augmented Reality allow more vivid representations and further influence the way museums present their narratives. Cultural institutions aim towards integrating such technologies in order to provide a more engaging experience, which is also tailored to the user by exploiting personalization and context-awareness. This paper presents CHATS, a system for personalized digital storytelling in cultural heritage sites. Storytelling is based on a tangible interface, which adds a gamification aspect and improves interactivity for people with visual impairment. Technologies of AR and Smart Glasses are used to enhance visitors’ experience. To test CHATS, a case study was implemented and evaluated.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ntagiantas_augmented_2022,

title = {An Augmented Reality Children’s Book Edutainment through Participatory Content Creation and Promotion Based on the Pastoral Life of Psiloritis},

author = {Antonis Ntagiantas and Markos Konstantakis and John Aliprantis and Dimitris Manousos and Lefteris Koumakis and George Caridakis},

url = {https://www.mdpi.com/2076-3417/12/3/1339},

doi = {10.3390/app12031339},

issn = {2076-3417},

year  = {2022},

date = {2022-01-01},

urldate = {2024-11-14},

journal = {Applied Sciences},

volume = {12},

number = {3},

pages = {1339},

abstract = {Augmented reality (AR) provides excellent learning potential, especially in a school environment. Multiple students can share the virtual scene and interact with it using the mobile interface as a hand-held display in AR children’s books. Students’ participation is an essential element of learning, and one of AR’s greatest strengths is its ability to promote collaborative experiences. An augmented reality children’s book edutainment through participatory content creation and promotion based on the pastoral life of Psiloritis has been recommended through this study, highlighting the features of AR to reveal educational values unique to AR and studying approaches for incorporating these characteristics into the typical education curriculum.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{alexandridis_knowledge-based_2021,

title = {A Knowledge-Based Deep Learning Architecture for Aspect-Based Sentiment Analysis},

author = {Georgios Alexandridis and John Aliprantis and Konstantinos Michalakis and Konstantinos Korovesis and Panagiotis Tsantilas and George Caridakis},

url = {https://www.worldscientific.com/doi/abs/10.1142/S0129065721500465},

doi = {10.1142/S0129065721500465},

issn = {0129-0657, 1793-6462},

year  = {2021},

date = {2021-10-01},

urldate = {2024-11-14},

journal = {International Journal of Neural Systems},

volume = {31},

number = {10},

pages = {2150046},

abstract = {The task of sentiment analysis tries to predict the affective state of a document by examining its content and metadata through the application of machine learning techniques. Recent advances in the field consider sentiment to be a multi-dimensional quantity that pertains to different interpretations (or aspects), rather than a single one. Based on earlier research, the current work examines the said task in the framework of a larger architecture that crawls documents from various online sources. Subsequently, the collected data are pre-processed, in order to extract useful features that assist the machine learning algorithms in the sentiment analysis task. More specifically, the words that comprise each text are mapped to a neural embedding space and are provided to a hybrid, bi-directional long short-term memory network, coupled with convolutional layers and an attention mechanism that outputs the final textual features. Additionally, a number of document metadata are extracted, including the number of a document’s repetitions in the collected corpus (i.e. number of reposts/retweets), the frequency and type of emoji ideograms and the presence of keywords, either extracted automatically or assigned manually, in the form of hashtags. The novelty of the proposed approach lies in the semantic annotation of the retrieved keywords, since an ontology-based knowledge management system is queried, with the purpose of retrieving the classes the aforementioned keywords belong to. Finally, all features are provided to a fully connected, multi-layered, feed-forward artificial neural network that performs the analysis task. The overall architecture is compared, on a manually collected corpus of documents, with two other state-of-the-art approaches, achieving optimal results in identifying negative sentiment, which is of particular interest to certain parties (like for example, companies) that are interested in measuring their online reputation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Alexandridis2021,

title = {A Knowledge-Based Deep Learning Architecture for Aspect-Based Sentiment Analysis},

author = {Georgios Alexandridis and John Aliprantis and Konstantinos Michalakis and Konstantinos Korovesis and Panagiotis Tsantilas and George Caridakis},

doi = {10.1142/s0129065721500465},

issn = {1793-6462},

year  = {2021},

date = {2021-10-00},

journal = {Int. J. Neur. Syst.},

volume = {31},

number = {10},

publisher = {World Scientific Pub Co Pte Ltd},

abstract = {The task of sentiment analysis tries to predict the affective state of a document by examining its content and metadata through the application of machine learning techniques. Recent advances in the field consider sentiment to be a multi-dimensional quantity that pertains to different interpretations (or aspects), rather than a single one. Based on earlier research, the current work examines the said task in the framework of a larger architecture that crawls documents from various online sources. Subsequently, the collected data are pre-processed, in order to extract useful features that assist the machine learning algorithms in the sentiment analysis task. More specifically, the words that comprise each text are mapped to a neural embedding space and are provided to a hybrid, bi-directional long short-term memory network, coupled with convolutional layers and an attention mechanism that outputs the final textual features. Additionally, a number of document metadata are extracted, including the number of a document’s repetitions in the collected corpus (i.e. number of reposts/retweets), the frequency and type of emoji ideograms and the presence of keywords, either extracted automatically or assigned manually, in the form of hashtags. The novelty of the proposed approach lies in the semantic annotation of the retrieved keywords, since an ontology-based knowledge management system is queried, with the purpose of retrieving the classes the aforementioned keywords belong to. Finally, all features are provided to a fully connected, multi-layered, feed-forward artificial neural network that performs the analysis task. The overall architecture is compared, on a manually collected corpus of documents, with two other state-of-the-art approaches, achieving optimal results in identifying negative sentiment, which is of particular interest to certain parties (like for example, companies) that are interested in measuring their online reputation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{alexandridis_survey_2021,

title = {A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media},

author = {Georgios Alexandridis and Iraklis Varlamis and Konstantinos Korovesis and George Caridakis and Panagiotis Tsantilas},

url = {https://www.mdpi.com/2078-2489/12/8/331},

doi = {10.3390/info12080331},

issn = {2078-2489},

year  = {2021},

date = {2021-08-01},

urldate = {2024-11-14},

journal = {Information},

volume = {12},

number = {8},

pages = {331},

abstract = {As the amount of content that is created on social media is constantly increasing, more and more opinions and sentiments are expressed by people in various subjects. In this respect, sentiment analysis and opinion mining techniques can be valuable for the automatic analysis of huge textual corpora (comments, reviews, tweets etc.). Despite the advances in text mining algorithms, deep learning techniques, and text representation models, the results in such tasks are very good for only a few high-density languages (e.g., English) that possess large training corpora and rich linguistic resources; nevertheless, there is still room for improvement for the other lower-density languages as well. In this direction, the current work employs various language models for representing social media texts and text classifiers in the Greek language, for detecting the polarity of opinions expressed on social media. The experimental results on a related dataset collected by the authors of the current work are promising, since various classifiers based on the language models (naive bayesian, random forests, support vector machines, logistic regression, deep feed-forward neural networks) outperform those of word or sentence-based embeddings (word2vec, GloVe), achieving a classification accuracy of more than 80%. Additionally, a new language model for Greek social media has also been trained on the aforementioned dataset, proving that language models based on domain specific corpora can improve the performance of generic language models by a margin of 2%. Finally, the resulting models are made freely available to the research community.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Alexandridis2021b,

title = {A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media},

author = {Georgios Alexandridis and Iraklis Varlamis and Konstantinos Korovesis and George Caridakis and Panagiotis Tsantilas},

doi = {10.3390/info12080331},

issn = {2078-2489},

year  = {2021},

date = {2021-08-00},

journal = {Information},

volume = {12},

number = {8},

publisher = {MDPI AG},

abstract = {As the amount of content that is created on social media is constantly increasing, more and more opinions and sentiments are expressed by people in various subjects. In this respect, sentiment analysis and opinion mining techniques can be valuable for the automatic analysis of huge textual corpora (comments, reviews, tweets etc.). Despite the advances in text mining algorithms, deep learning techniques, and text representation models, the results in such tasks are very good for only a few high-density languages (e.g., English) that possess large training corpora and rich linguistic resources; nevertheless, there is still room for improvement for the other lower-density languages as well. In this direction, the current work employs various language models for representing social media texts and text classifiers in the Greek language, for detecting the polarity of opinions expressed on social media. The experimental results on a related dataset collected by the authors of the current work are promising, since various classifiers based on the language models (naive bayesian, random forests, support vector machines, logistic regression, deep feed-forward neural networks) outperform those of word or sentence-based embeddings (word2vec, GloVe), achieving a classification accuracy of more than 80%. Additionally, a new language model for Greek social media has also been trained on the aforementioned dataset, proving that language models based on domain specific corpora can improve the performance of generic language models by a margin of 2%. Finally, the resulting models are made freely available to the research community.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Michalakis2021,

title = {A Context-Aware Middleware for Context Modeling and Reasoning: A Case-Study in Smart Cultural Spaces},

author = {Konstantinos Michalakis and Yannis Christodoulou and George Caridakis and Yorghos Voutos and Phivos Mylonas},

doi = {10.3390/app11135770},

issn = {2076-3417},

year  = {2021},

date = {2021-07-00},

journal = {Applied Sciences},

volume = {11},

number = {13},

publisher = {MDPI AG},

abstract = {The proliferation of smart things and the subsequent emergence of the Internet of Things has motivated the deployment of intelligent spaces that provide automated services to users. Context-awareness refers to the ability of the system to be aware of the virtual and physical environment, allowing more efficient personalization. Context modeling and reasoning are two important aspects of context-aware computing, since they enable the representation of contextual data and inference of high-level, meaningful information. Context-awareness middleware systems integrate context modeling and reasoning, providing abstraction and supporting heterogeneous context streams. In this work, such a context-awareness middleware system is presented, which integrates a proposed context model based on the adaptation and combination of the most prominent context categorization schemata. A hybrid reasoning procedure, which combines multiple techniques, is also proposed and integrated. The proposed system was evaluated in a real-case-scenario cultural space, which supports preventive conservation. The evaluation showed that the proposed system efficiently addressed both conceptual aspects, through means of representation and reasoning, and implementation aspects, through means of performance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{michalakis_context-aware_2021,

title = {A Context-Aware Middleware for Context Modeling and Reasoning: A Case-Study in Smart Cultural Spaces},

author = {Konstantinos Michalakis and Yannis Christodoulou and George Caridakis and Yorghos Voutos and Phivos Mylonas},

url = {https://www.mdpi.com/2076-3417/11/13/5770},

doi = {10.3390/app11135770},

issn = {2076-3417},

year  = {2021},

date = {2021-06-01},

urldate = {2024-11-14},

journal = {Applied Sciences},

volume = {11},

number = {13},

pages = {5770},

abstract = {The proliferation of smart things and the subsequent emergence of the Internet of Things has motivated the deployment of intelligent spaces that provide automated services to users. Context-awareness refers to the ability of the system to be aware of the virtual and physical environment, allowing more efficient personalization. Context modeling and reasoning are two important aspects of context-aware computing, since they enable the representation of contextual data and inference of high-level, meaningful information. Context-awareness middleware systems integrate context modeling and reasoning, providing abstraction and supporting heterogeneous context streams. In this work, such a context-awareness middleware system is presented, which integrates a proposed context model based on the adaptation and combination of the most prominent context categorization schemata. A hybrid reasoning procedure, which combines multiple techniques, is also proposed and integrated. The proposed system was evaluated in a real-case-scenario cultural space, which supports preventive conservation. The evaluation showed that the proposed system efficiently addressed both conceptual aspects, through means of representation and reasoning, and implementation aspects, through means of performance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{konstantakis_personalized_2020,

title = {A Personalized Heritage-Oriented Recommender System Based on Extended Cultural Tourist Typologies},

author = {Markos Konstantakis and Georgios Alexandridis and George Caridakis},

url = {https://www.mdpi.com/2504-2289/4/2/12},

doi = {10.3390/bdcc4020012},

issn = {2504-2289},

year  = {2020},

date = {2020-06-01},

urldate = {2024-11-14},

journal = {Big Data and Cognitive Computing},

volume = {4},

number = {2},

pages = {12},

abstract = {Recent developments in digital technologies regarding the cultural heritage domain have driven technological trends in comfortable and convenient traveling, by offering interactive and personalized user experiences. The emergence of big data analytics, recommendation systems and personalization techniques have created a smart research field, augmenting cultural heritage visitor’s experience. In this work, a novel, hybrid recommender system for cultural places is proposed, that combines user preference with cultural tourist typologies. Starting with the McKercher typology as a user classification research base, which extracts five categories of heritage tourists out of two variables (cultural centrality and depth of user experience) and using a questionnaire, an enriched cultural tourist typology is developed, where three additional variables governing cultural visitor types are also proposed (frequency of visits, visiting knowledge and duration of the visit). The extracted categories per user are fused in a robust collaborative filtering, matrix factorization-based recommendation algorithm as extra user features. The obtained results on reference data collected from eight cities exhibit an improvement in system performance, thereby indicating the robustness of the presented approach.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Konstantakis2020,

title = {A Personalized Heritage-Oriented Recommender System Based on Extended Cultural Tourist Typologies},

author = {Markos Konstantakis and Georgios Alexandridis and George Caridakis},

doi = {10.3390/bdcc4020012},

issn = {2504-2289},

year  = {2020},

date = {2020-06-00},

journal = {BDCC},

volume = {4},

number = {2},

publisher = {MDPI AG},

abstract = {Recent developments in digital technologies regarding the cultural heritage domain have driven technological trends in comfortable and convenient traveling, by offering interactive and personalized user experiences. The emergence of big data analytics, recommendation systems and personalization techniques have created a smart research field, augmenting cultural heritage visitor’s experience. In this work, a novel, hybrid recommender system for cultural places is proposed, that combines user preference with cultural tourist typologies. Starting with the McKercher typology as a user classification research base, which extracts five categories of heritage tourists out of two variables (cultural centrality and depth of user experience) and using a questionnaire, an enriched cultural tourist typology is developed, where three additional variables governing cultural visitor types are also proposed (frequency of visits, visiting knowledge and duration of the visit). The extracted categories per user are fused in a robust collaborative filtering, matrix factorization-based recommendation algorithm as extra user features. The obtained results on reference data collected from eight cities exhibit an improvement in system performance, thereby indicating the robustness of the presented approach.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{alexandridis_geolocation_2020,

title = {A Geolocation Analytics-Driven Ontology for Short-Term Leases: Inferring Current Sharing Economy Trends},

author = {Georgios Alexandridis and Yorghos Voutos and Phivos Mylonas and George Caridakis},

url = {https://www.mdpi.com/1999-4893/13/3/59},

doi = {10.3390/a13030059},

issn = {1999-4893},

year  = {2020},

date = {2020-03-01},

urldate = {2024-11-14},

journal = {Algorithms},

volume = {13},

number = {3},

pages = {59},

abstract = {Short-term property rentals are perhaps one of the most common traits of present day shared economy. Moreover, they are acknowledged as a major driving force behind changes in urban landscapes, ranging from established metropolises to developing townships, as well as a facilitator of geographical mobility. A geolocation ontology is a high level inference tool, typically represented as a labeled graph, for discovering latent patterns from a plethora of unstructured and multimodal data. In this work, a two-step methodological framework is proposed, where the results of various geolocation analyses, important in their own respect, such as ghost hotel discovery, form intermediate building blocks towards an enriched knowledge graph. The outlined methodology is validated upon data crawled from the Airbnb website and more specifically, on keywords extracted from comments made by users of the said platform. A rather solid case-study, based on the aforementioned type of data regarding Athens, Greece, is addressed in detail, studying the different degrees of expansion & prevalence of the phenomenon among the city’s various neighborhoods.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Alexandridis2020b,

title = {A Geolocation Analytics-Driven Ontology for Short-Term Leases: Inferring Current Sharing Economy Trends},

author = {Georgios Alexandridis and Yorghos Voutos and Phivos Mylonas and George Caridakis},

doi = {10.3390/a13030059},

issn = {1999-4893},

year  = {2020},

date = {2020-03-00},

journal = {Algorithms},

volume = {13},

number = {3},

publisher = {MDPI AG},

abstract = {Short-term property rentals are perhaps one of the most common traits of present day shared economy. Moreover, they are acknowledged as a major driving force behind changes in urban landscapes, ranging from established metropolises to developing townships, as well as a facilitator of geographical mobility. A geolocation ontology is a high level inference tool, typically represented as a labeled graph, for discovering latent patterns from a plethora of unstructured and multimodal data. In this work, a two-step methodological framework is proposed, where the results of various geolocation analyses, important in their own respect, such as ghost hotel discovery, form intermediate building blocks towards an enriched knowledge graph. The outlined methodology is validated upon data crawled from the Airbnb website and more specifically, on keywords extracted from comments made by users of the said platform. A rather solid case-study, based on the aforementioned type of data regarding Athens, Greece, is addressed in detail, studying the different degrees of expansion & prevalence of the phenomenon among the city’s various neighborhoods.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Konstantakis2020b,

title = {Adding Culture to UX},

author = {Markos Konstantakis and George Caridakis},

doi = {10.1145/3354002},

issn = {1556-4711},

year  = {2020},

date = {2020-02-25},

journal = {J. Comput. Cult. Herit.},

volume = {13},

number = {1},

pages = {1--17},

publisher = {Association for Computing Machinery (ACM)},

abstract = {For more than a decade, User eXperience (UX) has grown into a core concept of Human--Computer Interaction (HCI) and has been widely disseminated and accepted in the HCI community. At the same time, Cultural Heritage (CH) has been a favored domain for UX research, while the visitor is able to exploit CH material before, during, and after the visit, having different goals and requirements in each phase. Thus, CH organisations need to carefully explore the potential of investing in UX, as visitor satisfaction and intention to return and spread positive word-of-mouth is closely linked to the destination's overall success. A multitude of methods for UX analysis exist, but a clear overview of the current state of the available UX research methods in CH is missing. Recent studies [Othman 2012; Roussou 2018] have successfully developed frameworks to measure UX in cultural spaces, but most of them still lack understanding about how visitors interact with new technologies and simultaneously with the exhibits. In this article, a survey that describes the conceptual frameworks, models, research methodologies, and paradigms of incorporating Cultural User eXperience (CUX) in applications is reported, as well as the overall interaction between UX and CH and the related aspects that are influenced by the CH application domain and the parameters leading to optimization of the CUX. The outcome of this survey lays ground for understanding and defining the concept of UX research in CH, while current challenges and issues for future work are discussed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{konstantakis_adding_2020,

title = {Adding Culture to UX: UX Research Methodologies and Applications in Cultural Heritage},

author = {Markos Konstantakis and George Caridakis},

url = {https://dl.acm.org/doi/10.1145/3354002},

doi = {10.1145/3354002},

issn = {1556-4673, 1556-4711},

year  = {2020},

date = {2020-02-01},

urldate = {2024-11-14},

journal = {Journal on Computing and Cultural Heritage},

volume = {13},

number = {1},

pages = {1–17},

abstract = {For more than a decade, User eXperience (UX) has grown into a core concept of Human–Computer Interaction (HCI) and has been widely disseminated and accepted in the HCI community. At the same time, Cultural Heritage (CH) has been a favored domain for UX research, while the visitor is able to exploit CH material before, during, and after the visit, having different goals and requirements in each phase. Thus, CH organisations need to carefully explore the potential of investing in UX, as visitor satisfaction and intention to return and spread positive word-of-mouth is closely linked to the destination's overall success. A multitude of methods for UX analysis exist, but a clear overview of the current state of the available UX research methods in CH is missing. Recent studies [Othman 2012; Roussou 2018] have successfully developed frameworks to measure UX in cultural spaces, but most of them still lack understanding about how visitors interact with new technologies and simultaneously with the exhibits. In this article, a survey that describes the conceptual frameworks, models, research methodologies, and paradigms of incorporating Cultural User eXperience (CUX) in applications is reported, as well as the overall interaction between UX and CH and the related aspects that are influenced by the CH application domain and the parameters leading to optimization of the CUX. The outcome of this survey lays ground for understanding and defining the concept of UX research in CH, while current challenges and issues for future work are discussed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Michalakis2020,

title = {Semantic Representation and Internet of Things in Cultural Heritage Preventive Conservation},

author = {Konstantinos Michalakis and Efthymia Moraitou and John Aliprantis and George Caridakis},

doi = {10.12681/cicms.2765},

issn = {2654-1866},

year  = {2020},

date = {2020-01-23},

journal = {CICMS},

volume = {1},

number = {1},

publisher = {National Documentation Centre (EKT)},

abstract = {Preservation of Cultural Heritage (CH) collections in the best possible condition for the longest time possible is a crucial part of CH Institutions activity, since it ensures artefacts’ effective function in perpetuity. In this context, preservation processes that do not include any physical interaction with an object or collection can be regarded as preventive conservation. Preventive conservation measures and activities include among others the monitoring and management of environmental factors, in order to reduce potential risks of collections condition. The advent of the Internet of Things (IoT) can help towards this goal by automating the collection of data through sensors deployed in the cultural space and providing available services based on the IoT ecosystem. IoT technologies can facilitate the preventive conservation of tangible CH by exploiting streaming data produced by networks of sensors that keep track of changes in environmental parameters of a particular museum, in order to monitor the condition of its collections. Moreover, Semantic Web (SW) technologies could increase the efficiency of sensed data management by introducing reasoning mechanisms that will result in useful inferences regarding the combination of long-term or short-term records of sensed data and material decay. This work summarizes current state-of-the-art frameworks and monitoring systems that collect data from sensors in CH environments and the use of semantic web technologies for the efficient management of conservation and sensor data. Based on this study, it proposes an IoT infrastructure with semantic tools, which aims to enhance preventive conservation science.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{michalakis_semantic_2020,

title = {Semantic Representation and Internet of Things in Cultural Heritage Preventive Conservation},

author = {Konstantinos Michalakis and Efthymia Moraitou and John Aliprantis and George Caridakis},

url = {https://eproceedings.epublishing.ekt.gr/index.php/cicms/article/view/2765},

doi = {10.12681/cicms.2765},

issn = {2654-1866},

year  = {2020},

date = {2020-01-01},

urldate = {2024-11-14},

journal = {International Conference on Cultural Informatics, Communication & Media Studies},

volume = {1},

number = {1},

abstract = {Preservation of Cultural Heritage (CH) collections in the best possible condition for the longest time possible is a crucial part of CH Institutions activity, since it ensures artefacts’ effective function in perpetuity. In this context, preservation processes that do not include any physical interaction with an object or collection can be regarded as preventive conservation. Preventive conservation measures and activities include among others the monitoring and management of environmental factors, in order to reduce potential risks of collections condition. The advent of the Internet of Things (IoT) can help towards this goal by automating the collection of data through sensors deployed in the cultural space and providing available services based on the IoT ecosystem. IoT technologies can facilitate the preventive conservation of tangible CH by exploiting streaming data produced by networks of sensors that keep track of changes in environmental parameters of a particular museum, in order to monitor the condition of its collections. Moreover, Semantic Web (SW) technologies could increase the efficiency of sensed data management by introducing reasoning mechanisms that will result in useful inferences regarding the combination of long-term or short-term records of sensed data and material decay. This work summarizes current state-of-the-art frameworks and monitoring systems that collect data from sensors in CH environments and the use of semantic web technologies for the efficient management of conservation and sensor data. Based on this study, it proposes an IoT infrastructure with semantic tools, which aims to enhance preventive conservation science.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{aliprantis_survey_2019,

title = {A Survey of Augmented Reality Applications in Cultural Heritage:},

author = {John Aliprantis and George Caridakis},

url = {https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJCMHS.2019070107},

doi = {10.4018/IJCMHS.2019070107},

issn = {2473-5345, 2473-5337},

year  = {2019},

date = {2019-07-01},

urldate = {2024-11-14},

journal = {International Journal of Computational Methods in Heritage Science},

volume = {3},

number = {2},

pages = {118–147},

abstract = {Augmented reality is now a well-known concept, a technology that integrates digital content to the user's physical world. As more and more applications begin to reshape user's daily lives in multiple domains, cultural heritage sites have begun to focus on capitalizing augmented reality's potential and improve their visitor experience. This work provides an overview of the state-of-the-art techniques, methodologies, and technologies integrated with augmented reality applications for cultural heritage while also categorizing the current works according to the latest techniques as they integrate to enrich the user's experience. The survey presents an overview of the characteristics of an augmented reality system, and discusses the current challenges, issues, limitations, and future directions. Finally, this work examines the methods and techniques that are used in the cultural heritage and augmented reality integration, outlining benefits, and open issues.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Aliprantis2019,

title = {A Survey of Augmented Reality Applications in Cultural Heritage},

author = {John Aliprantis and George Caridakis},

doi = {10.4018/ijcmhs.2019070107},

issn = {2473-5337},

year  = {2019},

date = {2019-07-00},

volume = {3},

number = {2},

pages = {118--147},

publisher = {IGI Global},

abstract = {
Augmented reality is now a well-known concept, a technology that integrates digital content to the user's physical world. As more and more applications begin to reshape user's daily lives in multiple domains, cultural heritage sites have begun to focus on capitalizing augmented reality's potential and improve their visitor experience. This work provides an overview of the state-of-the-art techniques, methodologies, and technologies integrated with augmented reality applications for cultural heritage while also categorizing the current works according to the latest techniques as they integrate to enrich the user's experience. The survey presents an overview of the characteristics of an augmented reality system, and discusses the current challenges, issues, limitations, and future directions. Finally, this work examines the methods and techniques that are used in the cultural heritage and augmented reality integration, outlining benefits, and open issues.
},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{alexandridis_personalized_2019,

title = {Personalized and content adaptive cultural heritage path recommendation: an application to the Gournia and Çatalhöyük archaeological sites},

author = {Georgios Alexandridis and Angeliki Chrysanthi and George E. Tsekouras and George Caridakis},

url = {http://link.springer.com/10.1007/s11257-019-09227-6},

doi = {10.1007/s11257-019-09227-6},

issn = {0924-1868, 1573-1391},

year  = {2019},

date = {2019-03-01},

urldate = {2024-11-14},

journal = {User Modeling and User-Adapted Interaction},

volume = {29},

number = {1},

pages = {201–238},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Alexandridis2019,

title = {Personalized and content adaptive cultural heritage path recommendation: an application to the Gournia and Çatalhöyük archaeological sites},

author = {Georgios Alexandridis and Angeliki Chrysanthi and George E. Tsekouras and George Caridakis},

doi = {10.1007/s11257-019-09227-6},

issn = {1573-1391},

year  = {2019},

date = {2019-03-00},

journal = {User Model User-Adap Inter},

volume = {29},

number = {1},

pages = {201--238},

publisher = {Springer Science and Business Media LLC},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Moraitou2019,

title = {Semantic Bridging of Cultural Heritage Disciplines and Tasks},

author = {Efthymia Moraitou and John Aliprantis and Yannis Christodoulou and Alexandros Teneketzis and George Caridakis},

doi = {10.3390/heritage2010040},

issn = {2571-9408},

year  = {2019},

date = {2019-03-00},

journal = {Heritage},

volume = {2},

number = {1},

pages = {611--630},

publisher = {MDPI AG},

abstract = {The Cultural Heritage (CH) domain encompasses a wide range of different disciplines, serving the study, interpretation, curation, and preservation of objects, collections, archives, sites, and the dissemination of related knowledge. In this context, stakeholders generate, retrieve, and share a vast amount of diverse information. Therefore, information interoperability has been considered a crucial task, especially in terms of semantics. In this way, the CIDOC CRM (International Committee for Documentation Conceptual Reference Model) has been widely used as an underlying model that offers interoperability between CH domain metadata standards and ontologies. To the best of our knowledge, an overall review of mapping, merging, and extending this core ontology, as well as an aggregate table which classifies and correlates those ontologies and standards, has not yet been presented. Our study conducts an aggregate review of relevant published efforts and outlines the various associations between them, encapsulating the CIDOC CRM and its specialized models, as well. This work aims to further clarify the field and scope of the different works, identify their methods, and highlight the semantic overlap, or differences, between them.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{moraitou_semantic_2019,

title = {Semantic Bridging of Cultural Heritage Disciplines and Tasks},

author = {Efthymia Moraitou and John Aliprantis and Yannis Christodoulou and Alexandros Teneketzis and George Caridakis},

url = {https://www.mdpi.com/2571-9408/2/1/40},

doi = {10.3390/heritage2010040},

issn = {2571-9408},

year  = {2019},

date = {2019-02-01},

urldate = {2024-11-14},

journal = {Heritage},

volume = {2},

number = {1},

pages = {611–630},

abstract = {The Cultural Heritage (CH) domain encompasses a wide range of different disciplines, serving the study, interpretation, curation, and preservation of objects, collections, archives, sites, and the dissemination of related knowledge. In this context, stakeholders generate, retrieve, and share a vast amount of diverse information. Therefore, information interoperability has been considered a crucial task, especially in terms of semantics. In this way, the CIDOC CRM (International Committee for Documentation Conceptual Reference Model) has been widely used as an underlying model that offers interoperability between CH domain metadata standards and ontologies. To the best of our knowledge, an overall review of mapping, merging, and extending this core ontology, as well as an aggregate table which classifies and correlates those ontologies and standards, has not yet been presented. Our study conducts an aggregate review of relevant published efforts and outlines the various associations between them, encapsulating the CIDOC CRM and its specialized models, as well. This work aims to further clarify the field and scope of the different works, identify their methods, and highlight the semantic overlap, or differences, between them.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{michalakis_visualizing_2018,

title = {Visualizing the Internet of Things: Naturalizing Human-Computer Interaction by Incorporating AR Features},

author = {Konstantinos Michalakis and John Aliprantis and George Caridakis},

url = {https://ieeexplore.ieee.org/document/8333005/},

doi = {10.1109/MCE.2018.2797638},

issn = {2162-2248, 2162-2256},

year  = {2018},

date = {2018-05-01},

urldate = {2024-11-14},

journal = {IEEE Consumer Electronics Magazine},

volume = {7},

number = {3},

pages = {64–72},

keywords = {},

pubstate = {published},

tppubtype = {article}

}