Luigi Asprino

Citizen curation is gaining momentum as a new form of engagement with cultural heritage. Citizen curatorial activities require and produce a wealth of information, ranging from descriptions of the artefacts to visitor experience feedback. Although formalising and integrating such various data is of paramount importance, the domain lacks comprehensive ontologies to enable querying, interpreting and reasoning over the collected data. Social Participation, Cohesion and Inclusion through Cultural Engagement (SPICE) is an EU project dedicated to experimenting with citizen curation activities to foster cultural engagement. SPICE develops technologies that help communities to create and share their own interpretation of cultural artefacts, hence developing a better understanding of, and empathy for, themselves and other communities. Part of the SPICE ecosystem of technologies is the SPICE Ontology Network (SON), which empowers applications with knowledge-level reasoning abilities and supports both applications and users interacting with data involved in citizen curation activities. This article provides an overview of the SON and outlines its main use cases.

This article explores and presents innovative methods and technologies for supporting citizen curation of cultural heritage. Relevant outcomes of the Social Participation, Cohesion and Inclusion through Cultural Engagement (SPICE) project are presented, focusing on enhancing the state of content management and delivery strategies in museums and memory institutions. We argue that citizen curation requires a principled way of managing and integrating citizen responses, contributions and dataflows in the domain of cultural heritage, raising challenges and opportunities such as integration of distributed and diverse data sources, authoritativeness, interdependence, privacy, data reuse and rights management. The solution is a Linked Data Hub (LDH), which integrates museum collections and user-generated content and repurposes them to end-user systems tailored to specific use cases for citizens and museum practitioners. Such LDH must be non-open , by offering an approach that gives citizens and organisations, such as museums or engagement agencies, meaningful control over their data by implementing user-tailored policies and negotiated access and terms of use. Additionally, our solution addresses privacy violations in user-contributed content by offering a near-real-time content monitoring framework. We present the LDH discussing pilot applications within the EU-funded project SPICE, including ‘Deep Viewpoints’, which currently supports the Irish Museum of Modern Art (IMMA) in citizen curation activities. Overall, this article serves as a critical milestone in closing functional gaps and advancing the state of technology in managing citizen responses and contributions in the cultural heritage domain.

In this article we describe and evaluate a VR application, designed with a novel approach that allows personalized interaction and a more immersive virtual experience. This VR system uses both a user-based methodology in which the user expresses his/her emotions by interacting directly with the environment; and an artificial intelligence-driven methodology that is based on automated language systems able to detect user emotion and moral values from verbal speech. In this study we evaluated the usability and immersivity of our system on a sample of 30 museum visitors. The results indicate that our application is easy and pleasant to use for visitors, and that positive assessments are significantly associated with the level of immersion and realism experienced within the virtual environment. Additionally, we found a low incidence of motion sickness among users.

What should a data integration framework for knowledge engineers look like? Recent research on Knowledge Graph construction proposes the design of a façade, a notion borrowed from object-oriented software engineering. This idea is applied to SPARQL Anything, a system that allows querying heterogeneous resources as-if they were in RDF, in plain SPARQL 1.1, by overloading the SERVICE clause. SPARQL Anything supports a wide variety of file formats, from popular ones (CSV, JSON, XML, Spreadsheets) to others that are not supported by alternative solutions (Markdown, YAML, DOCx, Bibtex). Features include querying Web APIs with high flexibility, parametrised queries, and chaining multiple transformations into complex pipelines. In this paper, we describe the design rationale and software architecture of the SPARQL Anything system. We provide references to an extensive set of reusable, real-world scenarios from various application domains. We report on the value-to-users of the founding assumptions of its design, compared to alternative solutions through a community survey and a field report from the industry.

Linked Open Data (LOD) is the largest, collaborative, distributed, and publicly-accessible Knowledge Graph (KG) uniformly encoded in the Resource Description Framework (RDF) and formally represented according to the semantics of the Web Ontology Language (OWL). LOD provides researchers with a unique opportunity to study knowledge engineering as an empirical science: to observe existing modelling practices and possibly understanding how to improve knowledge engineering methodologies and knowledge representation formalisms. Following this perspective, several studies have analysed LOD to identify (mis-)use of OWL constructs or other modelling phenomena e.g. class or property usage, their alignment, the average depth of taxonomies. A question that remains open is whether there is a relation between observed modelling practices and knowledge domains (natural science, linguistics, etc.): do certain practices or phenomena change as the knowledge domain varies? Answering this question requires an assessment of the domains covered by LOD as well as a classification of its datasets. Existing approaches to classify LOD datasets provide partial and unaligned views, posing additional challenges. In this paper, we introduce a classification of knowledge domains, and a method for classifying LOD datasets and ontologies based on it. We classify a large portion of LOD and investigate whether a set of observed phenomena have a domain-specific character.

Data integration is the dominant use case for RDF Knowledge Graphs. However, Web resources come in formats with weak semantics (for example, CSV and JSON), or formats specific to a given application (for example, BibTex, HTML, and Markdown). To solve this problem, Knowledge Graph Construction (KGC) is gaining momentum due to its focus on supporting users in transforming data into RDF. However, using existing KGC frameworks result in complex data processing pipelines, which mix structural and semantic mappings, whose development and maintenance constitute a significant bottleneck for KG engineers. Such frameworks force users to rely on different tools, sometimes based on heterogeneous languages, for inspecting sources, designing mappings, and generating triples, thus making the process unnecessarily complicated. We argue that it is possible and desirable to equip KG engineers with the ability of interacting with Web data formats by relying on their expertise in RDF and the well-established SPARQL query language [ 2 ]. In this article, we study a unified method for data access to heterogeneous data sources with Facade-X, a meta-model implemented in a new data integration system called SPARQL Anything. We demonstrate that our approach is theoretically sound, since it allows a single meta-model, based on RDF, to represent data from (a) any file format expressible in BNF syntax, as well as (b) any relational database. We compare our method to state-of-the-art approaches in terms of usability (cognitive complexity of the mappings) and general performance. Finally, we discuss the benefits and challenges of this novel approach by engaging with the reference user community.