Periodic Reporting for period 1 - SPICE (Social cohesion, Participation, and Inclusion through Cultural Engagement)
Berichtszeitraum: 2020-05-01 bis 2021-04-30
The overall aim of the SPICE project is to develop tools and methods to support Citizen Curation, in which citizens actively engage in curatorial activities to learn more about themselves and develop a better understanding of, and empathy for, other communities.
# Social Inclusion
Citizens will be provided with data-centric, AI-informed technologies and methods that support them in interpreting culture for themselves. These will be designed in an inclusive way by framing them as familiar activities, for example, telling a story in which you imagine the life of a character in the painting, or playing a game, in which you link artworks to different emotions. Technologies will also be accessible to people with varied skills and abilities. This will involve, for example, audio narration and text magnification facilities for the visually impaired.
# Personalized Interpretation
Technologies and methods will guide the interpretation process by e.g. prompting citizens to notice features of artwork, or intentions of the artist. Citizens can develop their own interpretations e.g. by prompting them to select artworks they would associate with personal life events such as birth, growing up and marriage.
# Reflection
The varied interpretations contributed by citizens will be combined to help citizens to understand the perspectives of others. For example, visualisations will be provided that show the viewpoints of citizen groups such as older people, children, people with disabilities, and people of distinct cultural and ethnic backgrounds.
# Data-driven Platform
The underlying technology developed to support Citizen Curation will enable citizens to manage and control any digital contributions they make e.g. easily accessing stories or comments they add, editing, or removing them. Citizens will also be able to understand how they contribute to the overall visitor response by e.g. finding themselves within the visualisation of all citizen responses.
# Operationalised Meaning in Citizen Curation
Museums will be able to manage their digital resources and understand how citizens respond to their collections. Citizen Curation will involve the development of new ways of describing artworks. Currently, museums have ways of documenting artworks according to the artist, year of production, and material composition. Citizen Curation will extend this vocabulary, allowing artworks to be described in terms of e.g. how they make citizens feel and respond to artworks that are scary, controversial, or comforting. SPICE ontology-driven knowledge graphs will operationalise this extended meaning of inclusion and citizen curation.
# Case studies
Five case studies will address citizen communities, using participatory, co-design activities to the development of Citizen Curation methods. E.g. Case Study Finland includes elderly citizens, rural dwellers, and asylum seekers: in that case, spatial distance is correlated to exclusion and disadvantage. For the elderly, spatial distance might relate to a lack of physical mobility. For the rural dwellers, spatial distance might relate to great geographical distances that limit frequent physical engagement and interaction. For asylum seekers, it might be a lack of knowledge about the national culture that are going to encounter, thus limiting cultural and social engagement.
Reflective questions arise from those perspectives: How are the elderly viewed and valued in our culture? Is there such a thing as “Rural culture,” “Migrant culture”? How are they related to cultural heritage in Europe?
# Social Inclusion
Citizens will be provided with data-centric, AI-informed technologies and methods that support them in interpreting culture for themselves. These will be designed in an inclusive way by framing them as familiar activities, for example, telling a story in which you imagine the life of a character in the painting, or playing a game, in which you link artworks to different emotions. Technologies will also be accessible to people with varied skills and abilities. This will involve, for example, audio narration and text magnification facilities for the visually impaired.
# Personalized Interpretation
Technologies and methods will guide the interpretation process by e.g. prompting citizens to notice features of artwork, or intentions of the artist. Citizens can develop their own interpretations e.g. by prompting them to select artworks they would associate with personal life events such as birth, growing up and marriage.
# Reflection
The varied interpretations contributed by citizens will be combined to help citizens to understand the perspectives of others. For example, visualisations will be provided that show the viewpoints of citizen groups such as older people, children, people with disabilities, and people of distinct cultural and ethnic backgrounds.
# Data-driven Platform
The underlying technology developed to support Citizen Curation will enable citizens to manage and control any digital contributions they make e.g. easily accessing stories or comments they add, editing, or removing them. Citizens will also be able to understand how they contribute to the overall visitor response by e.g. finding themselves within the visualisation of all citizen responses.
# Operationalised Meaning in Citizen Curation
Museums will be able to manage their digital resources and understand how citizens respond to their collections. Citizen Curation will involve the development of new ways of describing artworks. Currently, museums have ways of documenting artworks according to the artist, year of production, and material composition. Citizen Curation will extend this vocabulary, allowing artworks to be described in terms of e.g. how they make citizens feel and respond to artworks that are scary, controversial, or comforting. SPICE ontology-driven knowledge graphs will operationalise this extended meaning of inclusion and citizen curation.
# Case studies
Five case studies will address citizen communities, using participatory, co-design activities to the development of Citizen Curation methods. E.g. Case Study Finland includes elderly citizens, rural dwellers, and asylum seekers: in that case, spatial distance is correlated to exclusion and disadvantage. For the elderly, spatial distance might relate to a lack of physical mobility. For the rural dwellers, spatial distance might relate to great geographical distances that limit frequent physical engagement and interaction. For asylum seekers, it might be a lack of knowledge about the national culture that are going to encounter, thus limiting cultural and social engagement.
Reflective questions arise from those perspectives: How are the elderly viewed and valued in our culture? Is there such a thing as “Rural culture,” “Migrant culture”? How are they related to cultural heritage in Europe?
* Methods for activating the Interpretation-Reflection-Loop (IRL) have been selected, adapted to case studies, and partly implemented with prototypes and co-design workshops.
* Prototypes to configure, detect, visualize, understand, explain, and navigate through citizen communities have been developed. The tools are designed to understand citizen social structures, relations between communities, and to help citizens to build representations of themselves within groups (social bonding) and to facilitate understanding and perspective-taking across groups (social bridging).
* We started the design of tools for the semantic annotation of curatorial products such as stories and presentations. A semantic multilingual annotator has been implemented for English, Finnish, Hebrew, Italian and Spanish. It annotates sentiment, emotion and public identity of entities. The reference system for annotation comes from the SON ontologies.
* We designed a technical ecosystem for the SPICE project. It stems from our survey of data infrastructures for Cultural Heritage, the requirements for citizen curation activities, and content/data management/copyright practices from the museum members of SPICE. Its implementation has focused on creating a linked data ecosystem that supports the management of data streams from diverse sources, with fine-grained access control. It consists of a novel universal transformer for arbitrary data (SPARQLAnything), a novel platform for linked data storage (the Linked Data Hub), and a Linked Data Intelligence layer to connect to loca applications.
* We developed the SPICE Ontology Network (SON) GitHub repository, including newly designed (or integrated) state-of-the-art ontology modules enabling the description of cultural objects and their sense-making aggregation during IRL activities. Notable novel ontologies represent emotional, narrative and value-based aspects that are at the core of cultural sense-making.
* Regarding human-machine interaction, we have carried out an extensive analysis of previous citizen curation systems to identify the most appropriate interaction paradigms for SPICE, and the necessary interfaces for citizen curation. Lo-fi prototypes have been designed for the interface components.
* We organised several events, including two mini-conferences and ethnographic interviews, to co-design methods for inclusive citizen curation and social cohesion. Case studies have been using co-design to bootstrap engagement with mediators and end-user communities prior to conducting workshops with them.
* Prototypes to configure, detect, visualize, understand, explain, and navigate through citizen communities have been developed. The tools are designed to understand citizen social structures, relations between communities, and to help citizens to build representations of themselves within groups (social bonding) and to facilitate understanding and perspective-taking across groups (social bridging).
* We started the design of tools for the semantic annotation of curatorial products such as stories and presentations. A semantic multilingual annotator has been implemented for English, Finnish, Hebrew, Italian and Spanish. It annotates sentiment, emotion and public identity of entities. The reference system for annotation comes from the SON ontologies.
* We designed a technical ecosystem for the SPICE project. It stems from our survey of data infrastructures for Cultural Heritage, the requirements for citizen curation activities, and content/data management/copyright practices from the museum members of SPICE. Its implementation has focused on creating a linked data ecosystem that supports the management of data streams from diverse sources, with fine-grained access control. It consists of a novel universal transformer for arbitrary data (SPARQLAnything), a novel platform for linked data storage (the Linked Data Hub), and a Linked Data Intelligence layer to connect to loca applications.
* We developed the SPICE Ontology Network (SON) GitHub repository, including newly designed (or integrated) state-of-the-art ontology modules enabling the description of cultural objects and their sense-making aggregation during IRL activities. Notable novel ontologies represent emotional, narrative and value-based aspects that are at the core of cultural sense-making.
* Regarding human-machine interaction, we have carried out an extensive analysis of previous citizen curation systems to identify the most appropriate interaction paradigms for SPICE, and the necessary interfaces for citizen curation. Lo-fi prototypes have been designed for the interface components.
* We organised several events, including two mini-conferences and ethnographic interviews, to co-design methods for inclusive citizen curation and social cohesion. Case studies have been using co-design to bootstrap engagement with mediators and end-user communities prior to conducting workshops with them.
* The operational deployment of the IRL is an important expected result of SPICE, which can gather benefits beyond social inclusion through cultural heritage, e.g. in public discussion platforms, ethical and political issues, social categorization of minorities, etc.
* Annotation tools, based on deep cultural requirements and carefully designed ontologies, and standing on a pragmatic computational attitude, offer an alternative to bulk, off-the-shelf generic tools for e.g. sentiment analysis and emotion detection.
* The technical ecosystem including a new universal data transformer and a new streamlined linked data storage system including communication to multiple local applications, is a major software architecture result, which extends its impact beyond cultural heritage and social sciences.
* The SPICE Ontology Network aligns to the nouvelle vague of networked ontologies in the humanities, such as the reused ArCo network for cultural entities, but it delves into areas that are not typically considered in mainstream projects.
* A novel paradigm of data- and semantics-centric human-machine interaction is being paved in SPICE, building on requirements, use cases, data layers, and ontologies.
* Annotation tools, based on deep cultural requirements and carefully designed ontologies, and standing on a pragmatic computational attitude, offer an alternative to bulk, off-the-shelf generic tools for e.g. sentiment analysis and emotion detection.
* The technical ecosystem including a new universal data transformer and a new streamlined linked data storage system including communication to multiple local applications, is a major software architecture result, which extends its impact beyond cultural heritage and social sciences.
* The SPICE Ontology Network aligns to the nouvelle vague of networked ontologies in the humanities, such as the reused ArCo network for cultural entities, but it delves into areas that are not typically considered in mainstream projects.
* A novel paradigm of data- and semantics-centric human-machine interaction is being paved in SPICE, building on requirements, use cases, data layers, and ontologies.