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

Open laboratories (OpenLabs) in Cultural Heritage institutions are an effective way to provide visibility into the behind-the-scenes processes and promote documentation data collected and produced by domain specialists. However, presenting these processes without proper explanation or communication with specialists may cause issues in terms of visitors’ understanding. To support OpenLabs and disseminate information, digital media and efficient data management can be utilized. The CAnTi (Conservation of Ancient Tiryns) project seeks to design and implement virtual and mixed reality applications that visualize conservation and restoration data, supporting OpenLab operations at the Acropolis of Ancient Tiryns. Semantic Web technologies will be used to model the digital content, facilitating organization and interoperability with external sources in the future. These applications will be part of the OpenLab activities on the site, enhancing visitors’ experiences and understanding of current and past conservation and restoration practices.

Ontologies constitute the semantic model of Knowledge Graphs (KGs). This structural association indicates the potential existence of methodological analogies in the development of ontologies and KGs. The deployment of fully and well-defined methodologies for KG development based on existing ontology engineering methodologies (OEMs) has been suggested and efficiently applied. However, most of the modern/recent OEMs may not include tasks that (i) empower knowledge workers and domain experts to closely collaborate with ontology engineers and KG specialists for the development and maintenance of KGs, (ii) satisfy special requirements of KG development, such as (a) ensuring modularity and agility of KGs, (b) assessing and mitigating bias at schema and data levels. Toward this aim, the paper presents a methodology for the Collaborative and Hybrid Engineering of Knowledge Graphs (CHEKG), which constitutes a hybrid (schema-centric/top-down and data-driven/bottom-up), collaborative, agile, and iterative approach for developing modular and fair domain-specific KGs. CHEKG contributes to all phases of the KG engineering lifecycle: from the specification of a KG to its exploitation, evaluation, and refinement. The CHEKG methodology is based on the main phases of the extended Human-Centered Collaborative Ontology Engineering Methodology (ext-HCOME), while it adjusts and expands the individual processes and tasks of each phase according to the specialized requirements of KG development. Apart from the presentation of the methodology per se, the paper presents recent work regarding the deployment and evaluation of the CHEKG methodology for the engineering of semantic trajectories as KGs generated from unmanned aerial vehicles (UAVs) data during real cultural heritage documentation scenarios.

Decision-making (DM) is the backbone of the Conservation and Restoration (CnR) of Cultural Heritage (CH). The demands of the DM process for information organization and management have raised issues that the CnR community attempts to solve by creating DM-support tools and systems, which, among others, exploit Semantic Web (SW) technologies. Regarding the tools and systems that focus on the DM process of selecting an intervention option (CnR-DM-I), they present benefits, as well as limitations, regarding the (1) completeness of representation of the relevant knowledge in a unified manner, (2) facilitation of recording the CnR-DM-I process per se , in terms of the problem at hand as well as the intervention parameters, requirements, and criteria, and (3) recommendation and further exploration of CnR intervention options in a systematic manner. This work proposes an ontology-based framework as a means to overcome those limitations. The proposed framework (DS-CnRI) sets at its core a formal ontology which provides the necessary entities to represent expert knowledge related to CnR-DM-I. The ontology also includes rules which provide useful inferences to assist the CnR-DM-I process. The proposed framework has been deployed and evaluated in collaboration with conservators. Initial evaluation results show that the framework assists conservators in CnR-DM-I to detect and select the most suitable intervention options, to better understand the limitations of different options, and to document the process of reaching their decision.

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