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366 results on '"Scarano, Vittorio"'

1. VRKG-CollaborativeExploration - Data-Driven Discussions in the Metaverse

Author

Accardo, Alberto, Monaco, Daniele, Pellegrino, Maria Angela, Scarano, Vittorio, Spagnuolo, Carmine, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pesquita, Catia, editor, Skaf-Molli, Hala, editor, Efthymiou, Vasilis, editor, Kirrane, Sabrina, editor, Ngonga, Axel, editor, Collarana, Diego, editor, Cerqueira, Renato, editor, Alam, Mehwish, editor, Trojahn, Cassia, editor, and Hertling, Sven, editor

Published
2023
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2. Collaborative Cybersecurity Awareness Learning in Higher Education

Author

Scarano, Vittorio, Giuseppina, Palmieri, Andriessen, Jerry, Pardijs, Mirjam, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kubincová, Zuzana, editor, Caruso, Federica, editor, Kim, Tae-eun, editor, Ivanova, Malinka, editor, Lancia, Loreto, editor, and Pellegrino, Maria Angela, editor

Published
2023
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3. Analyzing, Exploring, and Visualizing Complex Networks via Hypergraphs using SimpleHypergraphs.jl

Author

Antelmi, Alessia, Cordasco, Gennaro, Kamiński, Bogumił, Prałat, Paweł, Scarano, Vittorio, Spagnuolo, Carmine, and Szufel, Przemyslaw

Subjects
Computer Science - Social and Information Networks, Computer Science - Discrete Mathematics, G.2.2, G.4
Abstract

Real-world complex networks are usually being modeled as graphs. The concept of graphs assumes that the relations within the network are binary (for instance, between pairs of nodes); however, this is not always true for many real-life scenarios, such as peer-to-peer communication schemes, paper co-authorship, or social network interactions. For such scenarios, it is often the case that the underlying network is better and more naturally modeled by hypergraphs. A hypergraph is a generalization of a graph in which a single (hyper)edge can connect any number of vertices. Hypergraphs allow modelers to have a complete representation of multi-relational (many-to-many) networks; hence, they are extremely suitable for analyzing and discovering more subtle dependencies in such data structures. Working with hypergraphs requires new software libraries that make it possible to perform operations on them, from basic algorithms (such as searching or traversing the network) to computing significant hypergraph measures, to including more challenging algorithms (such as community detection). In this paper, we present a new software library, SimpleHypergraphs.jl, written in the Julia language and designed for high-performance computing on hypergraphs and propose two new algorithms for analyzing their properties: s-betweenness and modified label propagation. We also present various approaches for hypergraph visualization integrated into our tool. In order to demonstrate how to exploit the library in practice, we discuss two case studies based on the 2019 Yelp Challenge dataset and the collaboration network built upon the Game of Thrones TV series. The results are promising and they confirm the ability of hypergraphs to provide more insight than standard graph-based approaches., Comment: 32 pages, 10 figures, 7 tables, submitted to Internet Mathematics Journal

Published
2020
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4. COLTRANE – Towards a Methodology and Platform Supported Educational Basis for Cybersecurity Education

Author

Andriessen, Jerry, Furnell, Steven, Langner, Gregor, Luciano, Carmela, Quirchmayr, Gerald, Scarano, Vittorio, Tokola, Teemu Johannes, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, and Clarke, Nathan, editor

Published
2022
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5. Automatic Skill Generation for Knowledge Graph Question Answering

Author

Pellegrino, Maria Angela, Santoro, Mario, Scarano, Vittorio, Spagnuolo, Carmine, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Verborgh, Ruben, editor, Dimou, Anastasia, editor, Hogan, Aidan, editor, d'Amato, Claudia, editor, Tiddi, Ilaria, editor, Bröring, Arne, editor, Mayer, Simon, editor, Ongenae, Femke, editor, Tommasini, Riccardo, editor, and Alam, Mehwish, editor

Published
2021
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7. QueDI: From Knowledge Graph Querying to Data Visualization

Author

De Donato, Renato, Garofalo, Martina, Malandrino, Delfina, Pellegrino, Maria Angela, Petta, Andrea, Scarano, Vittorio, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Blomqvist, Eva, editor, Groth, Paul, editor, de Boer, Victor, editor, Pellegrini, Tassilo, editor, Alam, Mehwish, editor, Käfer, Tobias, editor, Kieseberg, Peter, editor, Kirrane, Sabrina, editor, Meroño-Peñuela, Albert, editor, and Pandit, Harshvardhan J., editor

Published
2020
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8. FLY: A Domain-Specific Language for Scientific Computing on FaaS

Author

Cordasco, Gennaro, D’Auria, Matteo, Negro, Alberto, Scarano, Vittorio, Spagnuolo, Carmine, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Schwardmann, Ulrich, editor, Boehme, Christian, editor, B. Heras, Dora, editor, Cardellini, Valeria, editor, Jeannot, Emmanuel, editor, Salis, Antonio, editor, Schifanella, Claudio, editor, Manumachu, Ravi Reddy, editor, Schwamborn, Dieter, editor, Ricci, Laura, editor, Sangyoon, Oh, editor, Gruber, Thomas, editor, Antonelli, Laura, editor, and Scott, Stephen L., editor

Published
2020
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12. Orchestrated Co-creation of High-Quality Open Data Within Large Groups

Author

Ferretti, Giuseppe, Malandrino, Delfina, Pellegrino, Maria Angela, Petta, Andrea, Renzi, Gianluigi, Scarano, Vittorio, Serra, Luigi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Lindgren, Ida, editor, Janssen, Marijn, editor, Lee, Habin, editor, Polini, Andrea, editor, Rodríguez Bolívar, Manuel Pedro, editor, Scholl, Hans Jochen, editor, and Tambouris, Efthimios, editor

Published
2019
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13. SimpleHypergraphs.jl—Novel Software Framework for Modelling and Analysis of Hypergraphs

Author

Antelmi, Alessia, Cordasco, Gennaro, Kamiński, Bogumił, Prałat, Paweł, Scarano, Vittorio, Spagnuolo, Carmine, Szufel, Przemyslaw, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Avrachenkov, Konstantin, editor, Prałat, Paweł, editor, and Ye, Nan, editor

Published
2019
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14. The MASON Simulation Toolkit: Past, Present, and Future

Author

Luke, Sean, Simon, Robert, Crooks, Andrew, Wang, Haoliang, Wei, Ermo, Freelan, David, Spagnuolo, Carmine, Scarano, Vittorio, Cordasco, Gennaro, Cioffi-Revilla, Claudio, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Davidsson, Paul, editor, and Verhagen, Harko, editor

Published
2019
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15. Heterogeneous Scalable Multi-languages Optimization via Simulation

Author

Cordasco, Gennaro, D’Auria, Matteo, Spagnuolo, Carmine, Scarano, Vittorio, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Ghosh, Ashish, Series Editor, Li, Liang, editor, Hasegawa, Kyoko, editor, and Tanaka, Satoshi, editor

Published
2018
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16. OpenABL: A Domain-Specific Language for Parallel and Distributed Agent-Based Simulations

Author

Cosenza, Biagio, Popov, Nikita, Juurlink, Ben, Richmond, Paul, Chimeh, Mozhgan Kabiri, Spagnuolo, Carmine, Cordasco, Gennaro, Scarano, Vittorio, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Aldinucci, Marco, editor, Padovani, Luca, editor, and Torquati, Massimo, editor

Published
2018
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23. Governance, Transparency and the Collaborative Design of Open Data Collaboration Platforms: Understanding Barriers, Options, and Needs

Author

Hogan, Michael, Ojo, Adegboyega, Harney, Owen, Ruijer, Erna, Meijer, Albert, Andriessen, Jerry, Pardijs, Mirjam, Boscolo, Paolo, Palmisano, Elena, Satta, Matteo, Groff, Jonathan, Baker, Michael, Détienne, Françoise, Porwol, Lukasz, Scarano, Vittorio, Malandrino, Delfina, Reddick, Christopher G., Series editor, Ojo, Adegboyega, editor, and Millard, Jeremy, editor

Published
2017
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26. Immersivity and Playability Evaluation of a Game Experience in Cultural Heritage

Author

Andreoli, Roberto, Corolla, Angela, Faggiano, Armando, Malandrino, Delfina, Pirozzi, Donato, Ranaldi, Mirta, Santangelo, Gianluca, Scarano, Vittorio, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ioannides, Marinos, editor, Fink, Eleanor, editor, Moropoulou, Antonia, editor, Hagedorn-Saupe, Monika, editor, Fresa, Antonella, editor, Liestøl, Gunnar, editor, Rajcic, Vlatka, editor, and Grussenmeyer, Pierre, editor

Published
2016
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28. A Survey on Hypergraph Representation Learning.

Author

ANTELMI, ALESSIA, CORDASCO, GENNARO, POLATO, MIRKO, SCARANO, VITTORIO, SPAGNUOLO, CARMINE, and DINGQI YANG

Subjects
HYPERGRAPHS, VIRTUAL networks, DEEP learning, ARTIFICIAL neural networks, CONVOLUTIONAL neural networks, MACHINE learning, NATURAL language processing, ONLINE social networks
Published
2024
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29. The MASON Simulation Toolkit: Past, Present, and Future

Author

Luke, Sean, primary, Simon, Robert, additional, Crooks, Andrew, additional, Wang, Haoliang, additional, Wei, Ermo, additional, Freelan, David, additional, Spagnuolo, Carmine, additional, Scarano, Vittorio, additional, Cordasco, Gennaro, additional, and Cioffi-Revilla, Claudio, additional

Published
2019
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31. ROUTE-TO-PA H2020 Project: Raising Open and User-Friendly Transparency-Enabling Technologies for Public Administrations

Author

Scarano, Vittorio, Malandrino, Delfina, Baker, Michael, Détienne, Françoise, Andriessen, Jerry, Pardijs, Mirjam, Ojo, Adegboyega, Porwol, Lukasz, Szufel, Przemyslaw, Kamiński, Bogumil, Meijer, Albert, Ruijer, Erna, Forrester, John, Clementino, Giuseppe, Trochidis, Ilias, Banos, Vangelis, Andriessen, Martin, van de Klashorst, Jan Pieter, Riordan, Pauline, Farrell, Ronan, Boscolo, Paolo, Palmisano, Elena, van der Waal, Sander, Gray, Jonathan, Satta, Matteo, Legale, Eric, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Katsikas, Sokratis K., editor, and Sideridis, Alexander B., editor

Published
2015
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33. KGHeartbeat

Author

Pellegrino, Maria Angela, Scarano, Vittorio, and Tuozzo, Gabriele

Subjects
Quality analysis, Knowledge Graph, Quality
Abstract

This repository behaves as asupport material for the paper entitled "KGHeartBeat: a Knowledge Graph Quality Management Tool" submitted (and under evaluation) as resource paper at ISWC 2020. This is a python applicationthat automatically analyzes the quality of Knowledge Graphs available on LODCloud and DataHUB. Thequality categoriesanalyzed by the application are: Accessibilithy Intrinsic Trust Dataset dynamicity Contextual Representational Each of these categories is made up of quality dimensions. Overall, the tool analyzes 23 differentquality dimensions, without the need of iteraction with the user. Input configuration From the configuration.jsonfile, you can choose the Knowledge Graph to analyze. You can analyze it by using a list of keywords or ids. In the example below, all the Knowledge Graphs that have keywordsmuseumwill be analyzed. {"name": ["museum"], "id": []} Or, by a list of ids with this: {"name": [], "id": ["dbpedia","taxref-ld"]} If instead, you want to analyze all the Knowledge Graphs automatically discoverable fromLODCloudandDataHub: {"name": [], "id": []} Start of the analysis After the input configuration, to execute the analysis simply execute from the main directory of the project: python manager.py The content of this repository is also provided on GitHub at https://github.com/isislab-unisa/KGHeartbeat. The entire project is released under the MIT license.

Published
2023
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34. A Tailorable Infrastructure to Enhance Mobile Seamless Learning

Author

Malandrino, Delfina, Manno, Ilaria, Palmieri, Giuseppina, Scarano, Vittorio, Tateo, Luca, Casola, Daniele, Ferrante, Ivan, and Foresta, Francesco

Abstract

The widespread use of mobile devices is leading towards their adoption in the learning process, even if some pedagogical challenges are still not fully addressed when integrating mobile-assisted activities into regular curricula activities. In this paper, we first define some guidelines to design a general, tailorable, and platform-independent mobile learning system. Second, we present the aCME system, a mobile infrastructure, developed by adhering to the defined guidelines, to provide a general-purpose system, easy to start up with and friendly to user, and finally, tailorable, i.e., that can be easily adapted to the needs of different learning environments. The aCME system has been implemented as a Web-based architecture, to provide content and functionalities for different contexts, accessible from any location and by using any communication device. Finally, we describe a tool, deployed into aCME, that allows responding to quiz-based questionnaires during learning activities. A preliminary evaluation was performed to analyze usability and user satisfaction when interacting with the system we developed. The paper is concluded with some comments and future research directions.

Published
2015
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41. Move cultural heritage knowledge graphs in everyone's pocket.

Author

Pellegrino, Maria Angela, Scarano, Vittorio, and Spagnuolo, Carmine

Subjects
KNOWLEDGE graphs, QUESTION answering systems, CULTURAL property, SEMANTIC Web, SOFTWARE frameworks, WEB-based user interfaces
Abstract

Last years witnessed a shift from the potential utility in digitisation to a crucial need to enjoy activities virtually. In fact, before 2019, data curators recognised the utility of performing data digitisation, while during the lockdown caused by the COVID-19, investing in virtual and remote activities to make culture survive became crucial as no one could enjoy Cultural Heritage in person. The Cultural Heritage community heavily invested in digitisation campaigns, mainly modelling data as Knowledge Graphs by becoming one of the most successful Semantic Web technologies application domains. Despite the vast investment in Cultural Heritage Knowledge Graphs, the syntactic complexity of RDF query languages, e.g., SPARQL, negatively affects and threatens data exploitation, risking leaving this enormous potential untapped. Thus, we aim to support the Cultural Heritage community (and everyone interested in Cultural Heritage) in querying Knowledge Graphs without requiring technical competencies in Semantic Web technologies. We propose an engaging exploitation tool accessible to all without losing sight of developers' technological challenges. Engagement is achieved by letting the Cultural Heritage community leave the passive position of the visitor and actively create their Virtual Assistant extensions to exploit proprietary or public Knowledge Graphs in question-answering. By accessible to all, we mean that the proposed software framework is freely available on GitHub and Zenodo with an open-source license. We do not lose sight of developers' technical challenges, which are carefully considered in the design and evaluation phases. This article first analyses the effort invested in publishing Cultural Heritage Knowledge Graphs to quantify data developers can rely on in designing and implementing data exploitation tools in this domain. Moreover, we point out challenges developers may face in exploiting them in automatic approaches. Second, it presents a domain-agnostic Knowledge Graph exploitation approach based on virtual assistants as they naturally enable question-answering features where users formulate questions in natural language directly by their smartphones. Then, we discuss the design and implementation of this approach within an automatic community-shared software framework (a.k.a. generator) of virtual assistant extensions and its evaluation in terms of performance and perceived utility according to end-users. Finally, according to a taxonomy of the Cultural Heritage field, we present a use case for each category to show the applicability of the proposed approach in the Cultural Heritage domain. In overviewing our analysis and the proposed approach, we point out challenges that a developer may face in designing virtual assistant extensions to query Knowledge Graphs, and we show the effect of these challenges in practice. [ABSTRACT FROM AUTHOR]

Published
2023
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46. A pilot study to inform the design of a supportive environment for challenge-based collaboration

Author

Pisoni, Galena, Gijlers, Hannie, Kubincová, Zuzana, Lancia, Loreto, Popescu, Elvira, Nakayama, Minoru, Scarano, Vittorio, Gil, Ana B., and Instructional Technology

Subjects
Teamwork, Medical education, Entrepreneurship, Reflection (computer programming), media_common.quotation_subject, Business ecosystem, Authentication (law), Challenge-based education, Work (electrical), Perception, Mobile payment, ComputingMilieux_COMPUTERSANDEDUCATION, Collaborative work, Team dynamics, Psychology, media_common
Abstract

In the course “An introduction to FinTech: from mobile payments to blockchains”, students are introduced to the broad area of FinTech, from both technical and business side. The course consists of two technical lectures that explain the students the basic technology for authentication and blockchain and two entrepreneurship lectures that discuss the fintech business ecosystem. In the rest of the course the students work on a project provided by companies in which they put into practice the obtained knowledge and provide a solution to a real life challenge. The course is face-to-face, with two weekly sessions of 2 h each. The students work in teams and each week they need to show progress/work towards the challenge resolution. The students are not constrained in any way and are asked to self organize for the team work. In this study we present the results we obtained from questionnaires that we delivered before the course on course expectations (that we used also to form the teams), during the course on teams’ dynamics, and after the course to measure students’ perception on collaboration strategies. We end the paper with a reflection on the design space for an application that can support this type of collaborative work online.

Published
2021

47. Move Cultural Heritage Knowledge Graphsin Everyone's Pocket

Author

Pellegrino, Maria Angela, Scarano, Vittorio, and Spagnuolo, Carmine

Subjects
Question-answering, Community-shared software framework, Knowledge Graphs, Virtual assistant, SPARQL
Abstract

This repository behaves as a support material for the paper entitled "Move Cultural Heritage Knowledge Graphsin Everyone’s Pocket" submitted (and under evaluation) as a **Application report** for the *Cultural Heritage and Semantic Web* special issue at the Semantic Web journal. It provides a Permanent URI for the Virtual Assistant Generator (virtual_assistant_generator.zip) that, at its actual version v 1.0, enables the automatic generation of Alexa skills to reply to natural language questions by querying SPARQL endpoints. The operating mechanism of the generator is detailed in Section 5 of the article. To demonstrate the operating mechanisms of the generator, we create the following Alexa skills as use cases, documented in Section 7 of the paper: - Wikidata_usecase.zip contains the two versions of the Wikidata Alexa skill to show the possibility to customize the linking mode performed by the generator. While in the dictionary-based WikiSkill we exploit a dictionary to perform the entity and relation linking, in the API-driven WikiSkill, we perform the linking phase by relying on the Wikidata APIs. For each skill, we report 1) the Alexa skill components (back_end.zip that implements the skill logic in Node.js and the interaction_model.js that defines the skill intents); 2) the conf.json that corresponds to the configuration file to generate the Alexa skill by our generator (when applicable); 3) the replies corresponding to the evaluation of the Wikidata skills on the QALD-7 training set; - DBpedia_usecase.zip contains the DBpedia Alexa skills. They are used to demonstrate the different generator configuration options: users can manually create the generator configuration file or auto-generate them, and use it as-is or manually refine it before generating the Alexa skill. For each DBpedia skill, we report 1) the Alexa skills components (back_end.zip that implements the skill logic in Node.js and the interaction_model.js that defines the skill intents); 2) the conf.json that corresponds to the configuration file to generate the Alexa skill by our generator; 3) its evaluation on the QALD-9 training set; - usesco_usecase.zip contains 1) the UNESCO Thesaurus Alexa skills component (back_end.zip that implements the skill logic in Node.js and the interaction_model.js that defines the skill intents); 2) the conf.json that corresponds to the configuration file to generate the Alexa skill by our generator; - wordnet_personal_assistant.zip contains 1) the WordNet Alexa skills component (back_end.zip that implements the skill logic in Node.js and the interaction_model.js that defines the skill intents); 2) the conf.json that corresponds to the configuration file to generate the Alexa skill by our generator; - warsampo_usecase.zip contains 1) the WarSampo Alexa skills component (back_end.zip that implements the skill logic in Node.js and the interaction_model.js that defines the skill intents); 2) the conf.json that corresponds to the configuration file to generate the Alexa skill by our generator; - dbtune_usecase.zip contains 1) the DBTune classical Alexa skills component (back_end.zip that implements the skill logic in Node.js and the interaction_model.js that defines the skill intents); 2) the conf.json that corresponds to the configuration file to generate the Alexa skill by our generator; - hungarian_museum_usecase.zip contains 1) the Hungarian museum Alexa skills component (back_end.zip that implements the skill logic in Node.js and the interaction_model.js that defines the skill intents); 2) the conf.json that corresponds to the configuration file to generate the Alexa skill by our generator; - mmm_usecase.zip contains 1) the Mapping Manuscript Migrations (MMM) Alexa skills component (back_end.zip that implements the skill logic in Node.js and the interaction_model.js that defines the skill intents); 2) the conf.json that corresponds to the configuration file to generate the Alexa skill by our generator; - natural_features_usecase.zip contains 1) the Natural Features (part of the Scotland's official statistics) Alexa skills component (back_end.zip that implements the skill logic in Node.js and the interaction_model.js that defines the skill intents); 2) the conf.json that corresponds to the configuration file to generate the Alexa skill by our generator; The content of this repository is also provided on GitHub at https://github.com/mariaangelapellegrino/virtual_assistant_generator. The entire project is released under the MIT license., This repository behaves as a support material for the paper entitled "Move Cultural Heritage Knowledge Graphsin Everyone's Pocket" submitted (and under evaluation) as a Application report for the Cultural Heritage and Semantic Web special issue at the Semantic Web journal. The content of this repository is also provided on GitHub at https://github.com/mariaangelapellegrino/virtual_assistant_generator.

Published
2020
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