Typology
operator:
and / or
Language
operator:
and / or
Date
operator:
and / or
Rights
operator:
and / or
2026
Conference article
Open Access
Building geospatial knowledge graphs for cultural heritage with the GeoLinks API
Pratelli Nicolo, De Martino Claudio, Lenzi Emanuele, Bartalesi Valentina
The Cultural Heritage domain is rich in contextual information, often found in unstructured texts. These sources frequently reference places but typically lack the semantic structure required for geospatial integration and analysis. We introduce the GeoLinks API, a lightweight web service that extracts and links geospatial entities from narrative texts to support the automatic construction of structured geospatial knowledge graphs. The API performs two core functions: (i) extracting geometries from GeoNames IRIs, and (ii) recognising and linking place entities to Wikidata. Outputs are expressed in GeoSPARQL, enabling spatial reasoning, semantic interoperability, and integration with Linked Data infrastructures. We demonstrate the performance of the GeoLinks API on a dataset of contextual narratives related to traditional craft production, illustrating how it transforms unstructured cultural texts into machine-readable, semantically enriched geospatial knowledge. This approach advances Cultural Heritage preservation and research by making spatial context explicit and supporting new forms of digital analysis.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16297, pp. 445-452. Heraklion, Crete, Greece, 15–17 October 2025
DOI: 10.1007/978-981-95-5009-8_31
Project(s): Craeft
Metrics:
Pratelli Nicolo, De Martino Claudio, Lenzi Emanuele, Bartalesi Valentina
The Cultural Heritage domain is rich in contextual information, often found in unstructured texts. These sources frequently reference places but typically lack the semantic structure required for geospatial integration and analysis. We introduce the GeoLinks API, a lightweight web service that extracts and links geospatial entities from narrative texts to support the automatic construction of structured geospatial knowledge graphs. The API performs two core functions: (i) extracting geometries from GeoNames IRIs, and (ii) recognising and linking place entities to Wikidata. Outputs are expressed in GeoSPARQL, enabling spatial reasoning, semantic interoperability, and integration with Linked Data infrastructures. We demonstrate the performance of the GeoLinks API on a dataset of contextual narratives related to traditional craft production, illustrating how it transforms unstructured cultural texts into machine-readable, semantically enriched geospatial knowledge. This approach advances Cultural Heritage preservation and research by making spatial context explicit and supporting new forms of digital analysis.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16297, pp. 445-452. Heraklion, Crete, Greece, 15–17 October 2025
DOI: 10.1007/978-981-95-5009-8_31
Project(s): Craeft
Metrics:
See at:
CNR IRIS 
| link.springer.com | CNR IRIS 
| CNR IRIS
2026
Journal article
Open Access
Interactive story maps for historical musical instruments: a 3D and semantic web tool for cultural heritage preservation
Bartalesi Valentina, Lenzi Emanuele, De Martino Claudio, Coro Gianpaolo
Ancient stringed instruments from the 17th and 18th centuries are essential for global cultural heritage but pose preservation and study challenges due to their fragility and rarity. Digital reproductions and interactive story maps offer new ways to analyse these instruments, providing historical, mechanical, and acoustic insights while aiding modern luthiers in replication. This paper presents a novel methodology and software that integrates 3D models of historical instruments into interactive story maps using Semantic Web technologies. The system enables users to explore 3D models with embedded annotations from Sketchfab and supports the creation of formal narratives enriched with geospatial and multimedia content. Each narrative event is linked to a backend knowledge base (KB) structured with established ontologies. The tool is open-source and web-based, and allows interoperability between its story maps and external KBs such as Wikidata and Europeana. It supports multiple levels of descriptive details, from precise 3D model annotations to broad geographic and temporal references. An evaluation case study featured 25 participants exploring a story map about a 1737 Antonio Stradivari violin, connected to a KB of 1716 triples. The results highlight the tool’s usability and effectiveness in representing the violin’s spatiotemporal and cultural context, showcasing the value of combining 3D models and geospatial data to enhance cultural heritage preservation.Source: INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS, vol. 22 (issue 1)
DOI: 10.1007/s41060-025-01014-4
Metrics:
Bartalesi Valentina, Lenzi Emanuele, De Martino Claudio, Coro Gianpaolo
Ancient stringed instruments from the 17th and 18th centuries are essential for global cultural heritage but pose preservation and study challenges due to their fragility and rarity. Digital reproductions and interactive story maps offer new ways to analyse these instruments, providing historical, mechanical, and acoustic insights while aiding modern luthiers in replication. This paper presents a novel methodology and software that integrates 3D models of historical instruments into interactive story maps using Semantic Web technologies. The system enables users to explore 3D models with embedded annotations from Sketchfab and supports the creation of formal narratives enriched with geospatial and multimedia content. Each narrative event is linked to a backend knowledge base (KB) structured with established ontologies. The tool is open-source and web-based, and allows interoperability between its story maps and external KBs such as Wikidata and Europeana. It supports multiple levels of descriptive details, from precise 3D model annotations to broad geographic and temporal references. An evaluation case study featured 25 participants exploring a story map about a 1737 Antonio Stradivari violin, connected to a KB of 1716 triples. The results highlight the tool’s usability and effectiveness in representing the violin’s spatiotemporal and cultural context, showcasing the value of combining 3D models and geospatial data to enhance cultural heritage preservation.Source: INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS, vol. 22 (issue 1)
DOI: 10.1007/s41060-025-01014-4
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS | CNR IRIS
2026
Conference article
Open Access
Connecting cultural heritage through knowledge graphs: a case study on medieval manuscripts
Pratelli Nicolo, Lenzi Emanuele, Bartalesi Valentina
Knowledge Graphs (KGs) are increasingly leveraged in Cultural Heritage (CH) domains to enable structured, interoperable representations of artefacts, people, events, and places. The ISO CIDOC CRM, as a core reference ontology, facilitates semantic alignment and interoperability across diverse CH datasets. However, despite the CIDOC CRM, linking independently developed KGs remains a complex challenge. This paper investigates the process of integrating two KGs collecting knowledge about ancient manuscripts: the Mapping Manuscript Migrations (MMM) and the IMAGO knowledge graphs, both of which adopt CIDOC CRM as their underlying schema. Through this case study, we explore methodological and technical hurdles in aligning entities, reconciling modelling decisions, and preserving provenance and semantics during integration. We discuss strategies for identity resolution, semantic approximation, and metadata enrichment, and propose reusable patterns for future KG linking efforts in the CH domain.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16297, pp. 494-508. Heraklion, Crete, Greece, 15–17 October 2025
DOI: 10.1007/978-981-95-5009-8_36
Metrics:
Pratelli Nicolo, Lenzi Emanuele, Bartalesi Valentina
Knowledge Graphs (KGs) are increasingly leveraged in Cultural Heritage (CH) domains to enable structured, interoperable representations of artefacts, people, events, and places. The ISO CIDOC CRM, as a core reference ontology, facilitates semantic alignment and interoperability across diverse CH datasets. However, despite the CIDOC CRM, linking independently developed KGs remains a complex challenge. This paper investigates the process of integrating two KGs collecting knowledge about ancient manuscripts: the Mapping Manuscript Migrations (MMM) and the IMAGO knowledge graphs, both of which adopt CIDOC CRM as their underlying schema. Through this case study, we explore methodological and technical hurdles in aligning entities, reconciling modelling decisions, and preserving provenance and semantics during integration. We discuss strategies for identity resolution, semantic approximation, and metadata enrichment, and propose reusable patterns for future KG linking efforts in the CH domain.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16297, pp. 494-508. Heraklion, Crete, Greece, 15–17 October 2025
DOI: 10.1007/978-981-95-5009-8_36
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
Linked geospatial data for rural territorial sustainability: a knowledge graph of european mountain value chains
Pratelli N., Lenzi E., Bartalesi Lenzi V.
As global urbanisation trends continue, with projections indicating that 70% of the world population will reside in cities by 2050, there is growing concern over the sustainability of rural and mountain regions. These areas face increasing depopulation, threatening their socio-economic value chains (VCs). Addressing these challenges requires access to reliable and interoperable geospatial data. Knowing the exact location of mountain VCs can provide important insights to support territorial resilience. This paper investigates how Knowledge Representation and Semantic Web technologies can enhance the analysis of geographic data to support sustainable development in rural territories. As a case study, we use data from the H2020 MOVING project, encompassing 454 VCs across 16 European mountain regions. Our findings show that semantic technologies offer a valuable framework for integrating heterogeneous datasets, thereby improving decision-making and fostering resilience in rural areas.DOI: 10.1109/ieee-ch65308.2025.11279601
Project(s): ITSERR Italian Strengthening of the ESFRI RI RESILIENCE
Metrics:
Pratelli N., Lenzi E., Bartalesi Lenzi V.
As global urbanisation trends continue, with projections indicating that 70% of the world population will reside in cities by 2050, there is growing concern over the sustainability of rural and mountain regions. These areas face increasing depopulation, threatening their socio-economic value chains (VCs). Addressing these challenges requires access to reliable and interoperable geospatial data. Knowing the exact location of mountain VCs can provide important insights to support territorial resilience. This paper investigates how Knowledge Representation and Semantic Web technologies can enhance the analysis of geographic data to support sustainable development in rural territories. As a case study, we use data from the H2020 MOVING project, encompassing 454 VCs across 16 European mountain regions. Our findings show that semantic technologies offer a valuable framework for integrating heterogeneous datasets, thereby improving decision-making and fostering resilience in rural areas.DOI: 10.1109/ieee-ch65308.2025.11279601
Project(s): ITSERR Italian Strengthening of the ESFRI RI RESILIENCE
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
Using large language models to create narrative events
Bartalesi Lenzi V., Lenzi E., De Martino C.
Narratives play a crucial role in human communication, serving as a means to convey experiences, perspectives, and meanings across various domains. They are particularly significant in scientific communities, where narratives are often utilized to explain complex phenomena and share knowledge. This article explores the possibility of integrating large language models (LLMs) into a workflow that, exploiting the Semantic Web technologies, transforms raw textual data gathered by scientific communities into narratives. In particular, we focus on using LLMs to automatically create narrative events, maintaining the reliability of the generated texts. The study provides a conceptual definition of narrative events and evaluates the performance of different smaller LLMs compared to the requirements we identified. A key aspect of the experiment is the emphasis on maintaining the integrity of the original narratives in the LLM outputs, as experts often review texts produced by scientific communities to ensure their accuracy and reliability. We first perform an evaluation on a corpus of five narratives and then on a larger dataset comprising 124 narratives. LLaMA 2 is identified as the most suitable model for generating narrative events that closely align with the input texts, demonstrating its ability to generate high-quality narrative events. Prompt engineering techniques are then employed to enhance the performance of the selected model, leading to further improvements in the quality of the generated texts.Source: PEERJ. COMPUTER SCIENCE., vol. 10
DOI: 10.7717/peerj-cs.2242
Project(s): Craeft
Metrics:
Bartalesi Lenzi V., Lenzi E., De Martino C.
Narratives play a crucial role in human communication, serving as a means to convey experiences, perspectives, and meanings across various domains. They are particularly significant in scientific communities, where narratives are often utilized to explain complex phenomena and share knowledge. This article explores the possibility of integrating large language models (LLMs) into a workflow that, exploiting the Semantic Web technologies, transforms raw textual data gathered by scientific communities into narratives. In particular, we focus on using LLMs to automatically create narrative events, maintaining the reliability of the generated texts. The study provides a conceptual definition of narrative events and evaluates the performance of different smaller LLMs compared to the requirements we identified. A key aspect of the experiment is the emphasis on maintaining the integrity of the original narratives in the LLM outputs, as experts often review texts produced by scientific communities to ensure their accuracy and reliability. We first perform an evaluation on a corpus of five narratives and then on a larger dataset comprising 124 narratives. LLaMA 2 is identified as the most suitable model for generating narrative events that closely align with the input texts, demonstrating its ability to generate high-quality narrative events. Prompt engineering techniques are then employed to enhance the performance of the selected model, leading to further improvements in the quality of the generated texts.Source: PEERJ. COMPUTER SCIENCE., vol. 10
DOI: 10.7717/peerj-cs.2242
Project(s): Craeft
Metrics:
See at:
PeerJ Computer Science | PeerJ Computer Science | IRIS Cnr | IRIS Cnr | CNR IRIS
2024
Journal article
Open Access
A semantic knowledge graph of European mountain value chains
Bartalesi Lenzi V., Coro G., Lenzi E., Pratelli N., Pagano P., Moretti M., Brunori G.
The United Nations forecast a significant shift in global population distribution by 2050, with rural populations projected to decline. This decline will particularly challenge mountain areas' cultural heritage, well-being, and economic sustainability. Understanding the economic, environmental, and societal effects of rural population decline is particularly important in Europe, where mountainous regions are vital for supplying goods. The present paper describes a geospatially explicit semantic knowledge graph containing information on 454 European mountain value chains. It is the first large-size, structured collection of information on mountain value chains. Our graph, structured through ontology-based semantic modelling, offers representations of the value chains in the form of narratives. The graph was constructed semi-automatically from unstructured data provided by mountain-area expert scholars. It is accessible through a public repository and explorable through interactive Story Maps and a semantic Web service. Through semantic queries, we demonstrate that the graph allows for exploring territorial complexities and discovering new knowledge on mountain areas' environmental, societal, territory, and economic aspects that could help stem depopulation.Source: SCIENTIFIC DATA, vol. 11
DOI: 10.1038/s41597-024-03760-9
Project(s): Mountain Valorization through Interconnectedness and Green Growth
Metrics:
Bartalesi Lenzi V., Coro G., Lenzi E., Pratelli N., Pagano P., Moretti M., Brunori G.
The United Nations forecast a significant shift in global population distribution by 2050, with rural populations projected to decline. This decline will particularly challenge mountain areas' cultural heritage, well-being, and economic sustainability. Understanding the economic, environmental, and societal effects of rural population decline is particularly important in Europe, where mountainous regions are vital for supplying goods. The present paper describes a geospatially explicit semantic knowledge graph containing information on 454 European mountain value chains. It is the first large-size, structured collection of information on mountain value chains. Our graph, structured through ontology-based semantic modelling, offers representations of the value chains in the form of narratives. The graph was constructed semi-automatically from unstructured data provided by mountain-area expert scholars. It is accessible through a public repository and explorable through interactive Story Maps and a semantic Web service. Through semantic queries, we demonstrate that the graph allows for exploring territorial complexities and discovering new knowledge on mountain areas' environmental, societal, territory, and economic aspects that could help stem depopulation.Source: SCIENTIFIC DATA, vol. 11
DOI: 10.1038/s41597-024-03760-9
Project(s): Mountain Valorization through Interconnectedness and Green Growth
Metrics:
See at:
Scientific Data | Scientific Data | IRIS Cnr | IRIS Cnr | Software Heritage | GitHub | CNR IRIS
2024
Conference article
Open Access
Evaluation of LLMs on long-tail entity linking in historical documents
Boscariol M., Bulla L., Draetta L., Fiumano B., Lenzi E., Piano L.
Entity Linking (EL) plays a crucial role in Natural Language Processing (NLP) applications, enabling the disambiguation of entity mentions by linking them to their corresponding entries in a reference knowledge base (KB). Thanks to their deep contextual understanding capabilities, LLMs offer a new perspective to tackle EL, promising better results than traditional methods. Despite the impressive generalization capabilities of LLMs, linking less popular, long-tail entities remains challenging as these entities are often underrepresented in training data and knowledge bases. Furthermore, the long-tail EL task is an understudied problem, and limited studies address it with LLMs. In the present work, we assess the performance of two popular LLMs, GPT and LLama3, in a long-tail entity linking scenario. Using MHERCL v0.1, a manually annotated benchmark of sentences from domain-specific historical texts, we quantitatively compare the performance of LLMs in identifying and linking entities to their corresponding Wikidata entries against that of ReLiK, a state-of-the-art Entity Linking and Relation Extraction framework. Our preliminary experiments reveal that LLMs perform encouragingly well in long-tail EL, indicating that this technology can be a valuable adjunct in filling the gap between head and long-tail EL.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3967. Amsterdam, Netherlands, 26-28/11/2024
Boscariol M., Bulla L., Draetta L., Fiumano B., Lenzi E., Piano L.
Entity Linking (EL) plays a crucial role in Natural Language Processing (NLP) applications, enabling the disambiguation of entity mentions by linking them to their corresponding entries in a reference knowledge base (KB). Thanks to their deep contextual understanding capabilities, LLMs offer a new perspective to tackle EL, promising better results than traditional methods. Despite the impressive generalization capabilities of LLMs, linking less popular, long-tail entities remains challenging as these entities are often underrepresented in training data and knowledge bases. Furthermore, the long-tail EL task is an understudied problem, and limited studies address it with LLMs. In the present work, we assess the performance of two popular LLMs, GPT and LLama3, in a long-tail entity linking scenario. Using MHERCL v0.1, a manually annotated benchmark of sentences from domain-specific historical texts, we quantitatively compare the performance of LLMs in identifying and linking entities to their corresponding Wikidata entries against that of ReLiK, a state-of-the-art Entity Linking and Relation Extraction framework. Our preliminary experiments reveal that LLMs perform encouragingly well in long-tail EL, indicating that this technology can be a valuable adjunct in filling the gap between head and long-tail EL.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3967. Amsterdam, Netherlands, 26-28/11/2024
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2024
Other
Open Access
AIMH Research Activities 2024
Aloia N., Amato G., Bartalesi Lenzi V., Bianchi L., Bolettieri P., Bosio C., Carraglia M., Carrara F., Casarosa V., Cassese M., Ciampi L., Coccomini D. A., Concordia C., Connor R., Corbara S., De Martino C., Di Benedetto M., Esuli A., Falchi F., Fazzari E., Gennaro C., Iannello L., Negi K., Lagani G., Lenzi E., Leocata M., Malvaldi M., Meghini C., Messina N., Moreo Fernandez A., Nardi A., Pacini G., Pedrotti A., Pratelli N., Puccetti G., Rabitti F., Savino P., Scotti F., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C., Versienti L., Volpi L.
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is committed to advancing the field of Artificial Intelligence, with a special emphasis on its applications in digital media and the humanities. The lab aims to improve AI technologies, particularly in areas such as deep learning, text analysis, computer vision, multimedia information retrieval, content analysis, recognition, and retrieval. This report summarizes the laboratory’s achievements and activities over the course of 2024.DOI: 10.32079/isti-ar-2024/001
Metrics:
Aloia N., Amato G., Bartalesi Lenzi V., Bianchi L., Bolettieri P., Bosio C., Carraglia M., Carrara F., Casarosa V., Cassese M., Ciampi L., Coccomini D. A., Concordia C., Connor R., Corbara S., De Martino C., Di Benedetto M., Esuli A., Falchi F., Fazzari E., Gennaro C., Iannello L., Negi K., Lagani G., Lenzi E., Leocata M., Malvaldi M., Meghini C., Messina N., Moreo Fernandez A., Nardi A., Pacini G., Pedrotti A., Pratelli N., Puccetti G., Rabitti F., Savino P., Scotti F., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C., Versienti L., Volpi L.
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is committed to advancing the field of Artificial Intelligence, with a special emphasis on its applications in digital media and the humanities. The lab aims to improve AI technologies, particularly in areas such as deep learning, text analysis, computer vision, multimedia information retrieval, content analysis, recognition, and retrieval. This report summarizes the laboratory’s achievements and activities over the course of 2024.DOI: 10.32079/isti-ar-2024/001
Metrics:
2023
Journal article
Open Access
From unstructured texts to semantic story maps
Bartalesi Lenzi V., Coro G., Lenzi E., Pagano P., Pratelli N.
Digital maps greatly support storytelling about territories, especially when enriched with data describing cultural, societal, and ecological aspects, conveying emotional messages that describe the territory as a whole. Story maps are interactive online digital narratives that can describe a territory beyond its map by enriching the map with text, pictures, videos, and other multimedia information. This paper presents a semi-automatic workflow to produce story maps from textual documents containing territory data. An expert first assembles one territory-contextual document containing text and images. Then, automatic processes use natural language processing and Wikidata services to (i) extract key concepts (entities) and geospatial coordinates associated with the territory, (ii) assemble a logically-ordered sequence of enriched story-map events, and (iii) openly publish online story maps and an interoperable Linked Open Data semantic knowledge base for event exploration and inter-story correlation analyses. Our workflow uses an Open Science-oriented methodology to publish all processes and data. Through our workflow, we produced story maps for the value chains and territories of 23 rural European areas of 16 countries. Through numerical evaluation, we demonstrated that territory experts considered the story maps effective in describing their territories, and appropriate for communicating with citizens and stakeholders.Source: INTERNATIONAL JOURNAL OF DIGITAL EARTH (ONLINE), vol. 16 (issue 1), pp. 234-250
DOI: 10.1080/17538947.2023.2168774
Project(s): MOVING
Metrics:
Bartalesi Lenzi V., Coro G., Lenzi E., Pagano P., Pratelli N.
Digital maps greatly support storytelling about territories, especially when enriched with data describing cultural, societal, and ecological aspects, conveying emotional messages that describe the territory as a whole. Story maps are interactive online digital narratives that can describe a territory beyond its map by enriching the map with text, pictures, videos, and other multimedia information. This paper presents a semi-automatic workflow to produce story maps from textual documents containing territory data. An expert first assembles one territory-contextual document containing text and images. Then, automatic processes use natural language processing and Wikidata services to (i) extract key concepts (entities) and geospatial coordinates associated with the territory, (ii) assemble a logically-ordered sequence of enriched story-map events, and (iii) openly publish online story maps and an interoperable Linked Open Data semantic knowledge base for event exploration and inter-story correlation analyses. Our workflow uses an Open Science-oriented methodology to publish all processes and data. Through our workflow, we produced story maps for the value chains and territories of 23 rural European areas of 16 countries. Through numerical evaluation, we demonstrated that territory experts considered the story maps effective in describing their territories, and appropriate for communicating with citizens and stakeholders.Source: INTERNATIONAL JOURNAL OF DIGITAL EARTH (ONLINE), vol. 16 (issue 1), pp. 234-250
DOI: 10.1080/17538947.2023.2168774
Project(s): MOVING
Metrics:
See at:
CNR IRIS | ISTI Repository | ISTI Repository | www.tandfonline.com | CNR IRIS | CNR IRIS
2023
Journal article
Open Access
Using Semantic Web to create and explore an index of toponyms cited in Medieval geographical works
Bartalesi Lenzi V., Pratelli N., Lenzi E., Pontari P.
Western thought in European history was mainly affected by the image of the world created during the Middle Ages and Renaissance. The most popular reason to travel during the Middle Ages was taking a pilgrimage. Jerusalem, Rome, and Santiago de Compostela were the most popular destinations. It is not surprising that a lot of works written by travellers as guides for pilgrims exist. By the beginning of the Renaissance, a more precise image of the world was defined thanks to the discovery of ancient geographical models, especially the work of Ptolemy. The three years (2020-2023) Italian National research project IMAGO - Index Medii Aevi Geographiae Operum - aims to provide a systematic overview of the medieval and renaissance Latin geographical literature using the Semantic Web technologies and the LOD paradigm. Indeed, until now, this literature has not been studied using digital methods. In particular, this paper presents how we formally represented the knowledge about the toponyms, or place names, in the IMAGO ontology. To maximise the interoperability, we developed the IMAGO ontology as an extension of two reference vocabularies: the CIDOC CRM and its extension FRBRoo, including its in-progress reformulation, LRMoo. Furthermore, we used Wikidata as reference knowledge base. As case study, we chose to represent the knowledge related to the toponyms cited by the Italian poet Dante Alighieri in his Latin works. We carried out a first experiment for visualising the knowledge about these toponyms on a map and in the form of tables and CSV files.Source: JOURNAL ON COMPUTING AND CULTURAL HERITAGE
DOI: 10.1145/3582263
Metrics:
Bartalesi Lenzi V., Pratelli N., Lenzi E., Pontari P.
Western thought in European history was mainly affected by the image of the world created during the Middle Ages and Renaissance. The most popular reason to travel during the Middle Ages was taking a pilgrimage. Jerusalem, Rome, and Santiago de Compostela were the most popular destinations. It is not surprising that a lot of works written by travellers as guides for pilgrims exist. By the beginning of the Renaissance, a more precise image of the world was defined thanks to the discovery of ancient geographical models, especially the work of Ptolemy. The three years (2020-2023) Italian National research project IMAGO - Index Medii Aevi Geographiae Operum - aims to provide a systematic overview of the medieval and renaissance Latin geographical literature using the Semantic Web technologies and the LOD paradigm. Indeed, until now, this literature has not been studied using digital methods. In particular, this paper presents how we formally represented the knowledge about the toponyms, or place names, in the IMAGO ontology. To maximise the interoperability, we developed the IMAGO ontology as an extension of two reference vocabularies: the CIDOC CRM and its extension FRBRoo, including its in-progress reformulation, LRMoo. Furthermore, we used Wikidata as reference knowledge base. As case study, we chose to represent the knowledge related to the toponyms cited by the Italian poet Dante Alighieri in his Latin works. We carried out a first experiment for visualising the knowledge about these toponyms on a map and in the form of tables and CSV files.Source: JOURNAL ON COMPUTING AND CULTURAL HERITAGE
DOI: 10.1145/3582263
Metrics:
See at:
dl.acm.org | CNR IRIS | ISTI Repository | ISTI Repository | CNR IRIS | CNR IRIS | CNR IRIS
2023
Conference article
Open Access
A web tool to create and visualise semantic story maps
Bartalesi Lenzi V., Lenzi E., Pratelli N.
This paper presents the Story Map Building and Visualizing Tool (SMBVT), a sofware that allows users to create and visualise semantic story maps using a user-friendly web interface. The tool uses Wikidata as external reference knowledge base and exploits Semantic Web technologies in the back-end system to represent stories modelled on the Narrative ontology, a CRM-based vocabulary for representing narratives. SMBVT is entirely open-source and accessible afer free registration.Source: CEUR WORKSHOP PROCEEDINGS, pp. 163-169. Dublin, Ireland, 02/04/2023
Bartalesi Lenzi V., Lenzi E., Pratelli N.
This paper presents the Story Map Building and Visualizing Tool (SMBVT), a sofware that allows users to create and visualise semantic story maps using a user-friendly web interface. The tool uses Wikidata as external reference knowledge base and exploits Semantic Web technologies in the back-end system to represent stories modelled on the Narrative ontology, a CRM-based vocabulary for representing narratives. SMBVT is entirely open-source and accessible afer free registration.Source: CEUR WORKSHOP PROCEEDINGS, pp. 163-169. Dublin, Ireland, 02/04/2023
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2023
Conference article
Open Access
Towards digital twins of territories through semantic story maps
Bartalesi Lenzi V., Coro G., Lenzi E., Pratelli N., Pagano P.
Digital maps greatly support storytelling about territories, especially when enriched with data describing cultural, societal, and ecological aspects, conveying emotional messages that describe the territory as a whole.Story maps are interactive online digital narratives that can describe a territory beyond its map by enriching themap with text, pictures, videos, and other multimedia information. This paper outlines how online story mapscan fill the gap between a map and a territory in narratives to create a digital twin of different territories asinter-connected semantic stories
Bartalesi Lenzi V., Coro G., Lenzi E., Pratelli N., Pagano P.
Digital maps greatly support storytelling about territories, especially when enriched with data describing cultural, societal, and ecological aspects, conveying emotional messages that describe the territory as a whole.Story maps are interactive online digital narratives that can describe a territory beyond its map by enriching themap with text, pictures, videos, and other multimedia information. This paper outlines how online story mapscan fill the gap between a map and a territory in narratives to create a digital twin of different territories asinter-connected semantic stories
See at:
CNR IRIS | inm.cnr.it | ISTI Repository | CNR IRIS
2023
Journal article
Open Access
Using semantic story maps to describe a territory beyond its map
Bartalesi Lenzi V., Coro G., Lenzi E., Pratelli N., Pagano P., Felici F., Moretti M., Brunori G.
The paper presents the Story Map Building and Visualizing Tool (SMBVT) that allows users to create story maps within a collaborative environment and a usable Web interface. It is entirely open-source and published as a free-to-use solution. It uses Semantic Web technologies in the back-end system to represent stories through a reference ontology for representing narratives. It builds up a user-shared semantic knowledge base that automatically interconnects all stories and seamlessly enables collaborative story building. Finally, it operates within an Open-Science oriented e-Infrastructure, which enables data and information sharing within communities of narrators, and adds multi-tenancy, multi-user, security, and access-control facilities. SMBVT represents narratives as a network of spatiotemporal events related by semantic relations and standardizes the event descriptions by assigning internationalized resource identifiers (IRIs) to the event components, i.e., the entities that take part in the event (e.g., persons, objects, places, concepts). The tool automatically saves the collected knowledge as a Web Ontology Language (OWL) graph and openly publishes it as Linked Open Data. This feature allows connecting the story events to other knowledge bases. To evaluate and demonstrate our tool, we used it to describe the Apuan Alps territory in Tuscany (Italy). Based on a user-test evaluation, we assessed the tool's effectiveness at building story maps and the ability of the produced story to describe the territory beyond the map.Source: SEMANTIC WEB (ONLINE), vol. 14 (issue 6), pp. 1255-1272
DOI: 10.3233/sw-233485
Metrics:
Bartalesi Lenzi V., Coro G., Lenzi E., Pratelli N., Pagano P., Felici F., Moretti M., Brunori G.
The paper presents the Story Map Building and Visualizing Tool (SMBVT) that allows users to create story maps within a collaborative environment and a usable Web interface. It is entirely open-source and published as a free-to-use solution. It uses Semantic Web technologies in the back-end system to represent stories through a reference ontology for representing narratives. It builds up a user-shared semantic knowledge base that automatically interconnects all stories and seamlessly enables collaborative story building. Finally, it operates within an Open-Science oriented e-Infrastructure, which enables data and information sharing within communities of narrators, and adds multi-tenancy, multi-user, security, and access-control facilities. SMBVT represents narratives as a network of spatiotemporal events related by semantic relations and standardizes the event descriptions by assigning internationalized resource identifiers (IRIs) to the event components, i.e., the entities that take part in the event (e.g., persons, objects, places, concepts). The tool automatically saves the collected knowledge as a Web Ontology Language (OWL) graph and openly publishes it as Linked Open Data. This feature allows connecting the story events to other knowledge bases. To evaluate and demonstrate our tool, we used it to describe the Apuan Alps territory in Tuscany (Italy). Based on a user-test evaluation, we assessed the tool's effectiveness at building story maps and the ability of the produced story to describe the territory beyond the map.Source: SEMANTIC WEB (ONLINE), vol. 14 (issue 6), pp. 1255-1272
DOI: 10.3233/sw-233485
Metrics:
See at:
content.iospress.com | CNR IRIS | ISTI Repository | ISTI Repository | CNR IRIS | CNR IRIS
2023
Other
Open Access
AIMH Research Activities 2023
Aloia N., Amato G., Bartalesi Lenzi V., Bianchi L., Bolettieri P., Bosio C., Carraglia M., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., De Martino C., Di Benedetto M., Esuli A., Falchi F., Fazzari E., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Molinari A., Moreo Fernandez A., Nardi A., Pedrotti A., Pratelli N., Puccetti G., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C., Versienti L.
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is dedicated to exploring and pushing the boundaries in the field of Artificial Intelligence, with a particular focus on its application in digital media and humanities. This lab's objective is to enhance the current state of AI technology particularly on deep learning, text analysis, computer vision, multimedia information retrieval, multimedia content analysis, recognition, and retrieval. This report encapsulates the laboratory's progress and activities throughout the year 2023.DOI: https://doi.org/10.32079/isti-ar-2023/001
DOI: 10.32079/isti-ar-2023/001
Metrics:
Aloia N., Amato G., Bartalesi Lenzi V., Bianchi L., Bolettieri P., Bosio C., Carraglia M., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., De Martino C., Di Benedetto M., Esuli A., Falchi F., Fazzari E., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Molinari A., Moreo Fernandez A., Nardi A., Pedrotti A., Pratelli N., Puccetti G., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C., Versienti L.
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is dedicated to exploring and pushing the boundaries in the field of Artificial Intelligence, with a particular focus on its application in digital media and humanities. This lab's objective is to enhance the current state of AI technology particularly on deep learning, text analysis, computer vision, multimedia information retrieval, multimedia content analysis, recognition, and retrieval. This report encapsulates the laboratory's progress and activities throughout the year 2023.DOI: https://doi.org/10.32079/isti-ar-2023/001
DOI: 10.32079/isti-ar-2023/001
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2022
Software
Metadata Only Access
Story Map Building and Visualising Tool (SMBVT)
Lenzi E., Bartalesi Lenzi V., Pratelli N., Coro G., Pagano P.
In the context of the MOVING (MOuntain Valorisation through INterconnectedness and Green growth) project, we released an open-source software - the MOVING Story Map Building and Visualization Tool (SMBVT) - that allows users to create and visualise story maps within a collaborative environment and using a user-friendly Web interface. The tool uses Semantic Web technologies and the Narrative Ontology to represent the stories of the MOVING mountain Value Chains. The MOVING community access SMBVT through The MOVING story map Virtual Research Environment and creates the events of the story. For each event, the user can add: a title, a textual description, start and end dates, the geographic coordinates, a media object (i.e. a video or image), notes, and digital objects. The tool takes Wikidata as reference KB and assigns Wikidata Internationalized Resource Identifiers (IRIs) to the story components (i.e. the entities that take part in an event). All the knowledge collected by SMBVT is stored in a JSON Postgres DB. When a story is completed, the tool automatically creates the corresponding visualisation using StoryMapJS library and makes available a corresponding URL that can be freely shared. Finally, SMBVT saves the collected knowledge as a Web Ontology Language (OWL) graph and publishes it as a Linked Open Data.Project(s): "CMG Collaborative Research": A Systematic Approach to Large Amplitude Internal Wave Dynamics: An Integrated Mathematical, Observational, and Remote Sensing Model, Blue Cloud, MOVING
Lenzi E., Bartalesi Lenzi V., Pratelli N., Coro G., Pagano P.
In the context of the MOVING (MOuntain Valorisation through INterconnectedness and Green growth) project, we released an open-source software - the MOVING Story Map Building and Visualization Tool (SMBVT) - that allows users to create and visualise story maps within a collaborative environment and using a user-friendly Web interface. The tool uses Semantic Web technologies and the Narrative Ontology to represent the stories of the MOVING mountain Value Chains. The MOVING community access SMBVT through The MOVING story map Virtual Research Environment and creates the events of the story. For each event, the user can add: a title, a textual description, start and end dates, the geographic coordinates, a media object (i.e. a video or image), notes, and digital objects. The tool takes Wikidata as reference KB and assigns Wikidata Internationalized Resource Identifiers (IRIs) to the story components (i.e. the entities that take part in an event). All the knowledge collected by SMBVT is stored in a JSON Postgres DB. When a story is completed, the tool automatically creates the corresponding visualisation using StoryMapJS library and makes available a corresponding URL that can be freely shared. Finally, SMBVT saves the collected knowledge as a Web Ontology Language (OWL) graph and publishes it as a Linked Open Data.Project(s): "CMG Collaborative Research": A Systematic Approach to Large Amplitude Internal Wave Dynamics: An Integrated Mathematical, Observational, and Remote Sensing Model, Blue Cloud
, MOVING
See at:
github.com | CNR IRIS
2022
Software
Metadata Only Access
IMAGO Annotation Tool
Pratelli N., Bartalesi Lenzi V., Lenzi E.
To populate the ontology developed within the Index Medii Aevi Geographiae Operum (IMAGO) -- Italian National Research Project (2020-23), we developed a semi-automatic Web tool, called IMAGO Annotation Tool, to allow scholars to insert knowledge about Medieval and Reinassance works through a user-friendly interface. The tool was created to reduce the time to insert knowledge and to avoid the insertion of mistakes thanks to the use of predefined lists of works, authors, libraries, places, geographic coordinates, and literary genres. Each field of the interface maps a class of the IMAGO ontology. The frontend interface is built using HTML5, CSS3, JavaScript, and the Bootstrap library, using a Python backend, e.i., a Django framework and a PostgreSQL DB. Once the data about a work is inserted through the tool interface, this is encoded as an OWL knowledge base and stored in a triple store. The data is first exported to a JSON object. Indeed our software uses a JSON schema to represent the data, structured according to the IMAGO ontology classes. The JSON object is processed by Java software, which transforms it into an OWL graph encoded in RDF/XML and Turtle formats. This software carries out its task by relying on the Apache Jena library. The graph is finally stored in a Fuseki triple store, and it can be queried through a SPARQL endpoint.
Pratelli N., Bartalesi Lenzi V., Lenzi E.
To populate the ontology developed within the Index Medii Aevi Geographiae Operum (IMAGO) -- Italian National Research Project (2020-23), we developed a semi-automatic Web tool, called IMAGO Annotation Tool, to allow scholars to insert knowledge about Medieval and Reinassance works through a user-friendly interface. The tool was created to reduce the time to insert knowledge and to avoid the insertion of mistakes thanks to the use of predefined lists of works, authors, libraries, places, geographic coordinates, and literary genres. Each field of the interface maps a class of the IMAGO ontology. The frontend interface is built using HTML5, CSS3, JavaScript, and the Bootstrap library, using a Python backend, e.i., a Django framework and a PostgreSQL DB. Once the data about a work is inserted through the tool interface, this is encoded as an OWL knowledge base and stored in a triple store. The data is first exported to a JSON object. Indeed our software uses a JSON schema to represent the data, structured according to the IMAGO ontology classes. The JSON object is processed by Java software, which transforms it into an OWL graph encoded in RDF/XML and Turtle formats. This software carries out its task by relying on the Apache Jena library. The graph is finally stored in a Fuseki triple store, and it can be queried through a SPARQL endpoint.
See at:
CNR IRIS | imagoarchive.it
2022
Conference article
Open Access
A knowledge base of medieval and renaissance geographic Latin works
Bartalesi Lenzi V., Pratelli N., Lenzi E.
The geography of the world created during the Middle Ages and Renaissance (VI-XV centuries) was crucial to the development of Western thought in the European history. Until now, to the best of our knowledge, Medieval and Renaissance geographic Latin literature has not been studied using digital methods. The Italian National research project IMAGO - Index Medii Aevi Geographiae Operum - (2020-2023) aims at providing a systematic overview of this literature using Semantic Web technologies and the Linked Open Data paradigm. As the first step to develop tools to support scholars in creating, evolving and consulting a knowledge base (KB) of the geographic works, we created an OWL 2 DL ontology. To maximize its interoperability, we developed the ontology as an extension of two reference vocabularies: the CIDOC CRM and FRBRoo (including its in-progress reformulation LRMoo). In this paper, we briefly present the project, the ontology, and the automatic and semi-automatic tools we developed to populate it. The final aim of the project is the creation of a Web application allowing scholars to freely access and visualise the data collected in the IMAGO knowledge base.Source: CEUR WORKSHOP PROCEEDINGS. Padua, Italy, 24-25/02/2022
Bartalesi Lenzi V., Pratelli N., Lenzi E.
The geography of the world created during the Middle Ages and Renaissance (VI-XV centuries) was crucial to the development of Western thought in the European history. Until now, to the best of our knowledge, Medieval and Renaissance geographic Latin literature has not been studied using digital methods. The Italian National research project IMAGO - Index Medii Aevi Geographiae Operum - (2020-2023) aims at providing a systematic overview of this literature using Semantic Web technologies and the Linked Open Data paradigm. As the first step to develop tools to support scholars in creating, evolving and consulting a knowledge base (KB) of the geographic works, we created an OWL 2 DL ontology. To maximize its interoperability, we developed the ontology as an extension of two reference vocabularies: the CIDOC CRM and FRBRoo (including its in-progress reformulation LRMoo). In this paper, we briefly present the project, the ontology, and the automatic and semi-automatic tools we developed to populate it. The final aim of the project is the creation of a Web application allowing scholars to freely access and visualise the data collected in the IMAGO knowledge base.Source: CEUR WORKSHOP PROCEEDINGS. Padua, Italy, 24-25/02/2022
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | ISTI Repository | CNR IRIS | CNR IRIS
2022
Journal article
Open Access
Linking different scientific digital libraries in Digital Humanities: the IMAGO case study
Bartalesi Lenzi V., Pratelli N., Lenzi E.
In the last years, several scientific digital libraries (DLs) in digital humanities (DH) field have been developed following the Open Science principles. These DLs aim at sharing the research outcomes, in several cases as FAIR data, and at creating linked information spaces. In several cases, to reach these aims the Semantic Web technologies and Linked Data have been used. This paper presents how the current scientific DLs in the DH field can provide the creation of linked information spaces and navigational services that allow users to navigate them, using Semantic Web technologies to formally represent, search and browsing knowledge. To support the argument, we present our experience in developing a scientific DL supporting scholars in creating, evolving and consulting a knowledge base related to Medieval and Renaissance geographical works within the three years (2020-2023) Italian National research project IMAGO--Index Medii Aevi Geographiae Operum. In the presented case study, a linked information space was created to allow users to discover and navigate knowledge across multiple repositories, thanks to the extensive use of ontologies. In particular, the linked information spaces created within the IMAGO project make use of five different datasets, i.e. Wikidata, the MIRABILE digital archive, the Nuovo Soggettario thesaurus, Mapping Manuscript Migration knowledge base and the Pleiades gazetteer. The linking among different datasets allows to considerably enrich the knowledge collected in the IMAGO KB.Source: INTERNATIONAL JOURNAL ON DIGITAL LIBRARIES (INTERNET)
DOI: 10.1007/s00799-022-00331-4
Metrics:
Bartalesi Lenzi V., Pratelli N., Lenzi E.
In the last years, several scientific digital libraries (DLs) in digital humanities (DH) field have been developed following the Open Science principles. These DLs aim at sharing the research outcomes, in several cases as FAIR data, and at creating linked information spaces. In several cases, to reach these aims the Semantic Web technologies and Linked Data have been used. This paper presents how the current scientific DLs in the DH field can provide the creation of linked information spaces and navigational services that allow users to navigate them, using Semantic Web technologies to formally represent, search and browsing knowledge. To support the argument, we present our experience in developing a scientific DL supporting scholars in creating, evolving and consulting a knowledge base related to Medieval and Renaissance geographical works within the three years (2020-2023) Italian National research project IMAGO--Index Medii Aevi Geographiae Operum. In the presented case study, a linked information space was created to allow users to discover and navigate knowledge across multiple repositories, thanks to the extensive use of ontologies. In particular, the linked information spaces created within the IMAGO project make use of five different datasets, i.e. Wikidata, the MIRABILE digital archive, the Nuovo Soggettario thesaurus, Mapping Manuscript Migration knowledge base and the Pleiades gazetteer. The linking among different datasets allows to considerably enrich the knowledge collected in the IMAGO KB.Source: INTERNATIONAL JOURNAL ON DIGITAL LIBRARIES (INTERNET)
DOI: 10.1007/s00799-022-00331-4
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS | CNR IRIS
2022
Other
Restricted
MOVING D3.3 - Tools for science-society-policy interfaces. Using semantic story maps to describe a territory beyond its map
Bartalesi Lenzi V., Coro G., Lenzi E., Pratelli N., Pagano P.
Maps have always stimulated people's imagination and spatiotemporally supported storytelling; however, they cannot alone represent the life and emotions associated with the territories they describe. Story maps are an IT solution to enrich maps with such information. They are online applications enriched with multimedia and textual information that tell map-based stories. Current software for story map building is either commercial or requires advanced IT skills that make it hardly used by environmental experts. This deliverable describes the Story Map Building and Visualizing Tool (SBVMT), an open-source and free-to-use tool to build and publish story maps, which overcomes common drawbacks of other software by operating within the open-science e-Infrastructure (D4Science) used in the MOVING Project. SBVMT includes new features such as an ontology to represent story maps, Semantic Web technologies for data representation, automatic connection to Wikidata, secure multi-user collaboration in story building, and visualisation of the narrative either as a story map or a timeline. This deliverable shows how SBVMT can overcome the perceptual gap between territory and map. We evaluated its usability and effectiveness from both the point of view of experts building the story map and users interacting with it. Using SMBVT we created the story maps related to the MOVING selected regions. Furthermore, exploiting the semantic web technologies, we implemented several SPARQL queries that allow linking different stories and discovering new knowledge.Project(s): MOVING
Bartalesi Lenzi V., Coro G., Lenzi E., Pratelli N., Pagano P.
Maps have always stimulated people's imagination and spatiotemporally supported storytelling; however, they cannot alone represent the life and emotions associated with the territories they describe. Story maps are an IT solution to enrich maps with such information. They are online applications enriched with multimedia and textual information that tell map-based stories. Current software for story map building is either commercial or requires advanced IT skills that make it hardly used by environmental experts. This deliverable describes the Story Map Building and Visualizing Tool (SBVMT), an open-source and free-to-use tool to build and publish story maps, which overcomes common drawbacks of other software by operating within the open-science e-Infrastructure (D4Science) used in the MOVING Project. SBVMT includes new features such as an ontology to represent story maps, Semantic Web technologies for data representation, automatic connection to Wikidata, secure multi-user collaboration in story building, and visualisation of the narrative either as a story map or a timeline. This deliverable shows how SBVMT can overcome the perceptual gap between territory and map. We evaluated its usability and effectiveness from both the point of view of experts building the story map and users interacting with it. Using SMBVT we created the story maps related to the MOVING selected regions. Furthermore, exploiting the semantic web technologies, we implemented several SPARQL queries that allow linking different stories and discovering new knowledge.Project(s): MOVING
2022
Other
Open Access
AIMH research activities 2022
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A. D., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability.This report summarize the 2022 activities of the research group.DOI: https://doi.org/10.32079/isti-ar-2022/002
DOI: 10.32079/isti-ar-2022/002
Metrics:
Aloia N., Amato G., Bartalesi Lenzi V., Benedetti F., Bolettieri P., Cafarelli D., Carrara F., Casarosa V., Ciampi L., Coccomini D. A., Concordia C., Corbara S., Di Benedetto M., Esuli A., Falchi F., Gennaro C., Lagani G., Lenzi E., Meghini C., Messina N., Metilli D., Molinari A., Moreo Fernandez A. D., Nardi A., Pedrotti A., Pratelli N., Rabitti F., Savino P., Sebastiani F., Sperduti G., Thanos C., Trupiano L., Vadicamo L., Vairo C.
The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability.This report summarize the 2022 activities of the research group.DOI: https://doi.org/10.32079/isti-ar-2022/002
DOI: 10.32079/isti-ar-2022/002
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS