Abstract
Introduction
Over the past decade, the
The primary aim of CnR of tangible CH lies in the maintenance of the physical, aesthetic, and historical integrity of Values can be artistic, aesthetic, symbolic, historical, social, economic etc. [6,39].
Documentation,3 Documentation refers to the information collected, created and maintained for the purpose of present and future CnR of the conservation objects and for reference [6,39].
However, up to now, CnR actors face the problem of limited means for retrieving and linking information, mainly due to the fact that CnR-related data are usually heterogeneous and often fragmented, for a number of reasons. First, CnR laboratories record their data in databases isolated from each other, each one developed according to different requirements which stem from different specializations [81,83,110]. Second, CnR data can be found in various forms, structured (e.g., in the form of relational databases), semi-structured (XML annotated documents) or unstructured (free texts), and, as such, are not semantically interoperable [84,110]. Lastly, the CnR domain heavily suffers from terminology inconsistency, since domain specialists tend to use specialized terms in diverse ways4 As [115] mentions the term
In response to the increasing interest of the CnR domain in semantic representation methods, this paper reviews semantic models that have been developed and deployed in the context of the CnR domain. The gathered works propose mainly (but not exclusively) formal ontologies. The scope and development methodology of each model are described, while the fundamental aspects of the underlying conceptualization are highlighted. Furthermore, the evaluation and deployment (if any) of each model as part of a SW system is examined, with focus on the types and variety of services provided to support the CnR professional. Based on the study, the following research questions are investigated: (a) To what extent the various aspects of CnR are covered by existing CnR models? (b) To what extent existing CnR models incorporate models of the broader CH domain and of relevant disciplines (e.g., Chemistry)? and (c) In what ways and to what extent services built upon the reviewed models facilitate CnR professionals in their various tasks?
The remainder of the paper is structured as follows. In Section 2, the methodology of the survey is discussed. In Section 3, each reviewed work is presented, based on a set of predefined axes (scope, development, coverage, deployment, evaluation, exploitation). The paper concludes with a discussion that summarizes interesting observations over the reviewed models as well as paths that merit further research, towards a more active and well-rounded support of the CnR process.
As mentioned, the current survey reviews knowledge models in the context of the CnR domain that have been developed using SW technologies and methods. The research of the literature was conducted using the data sources of
In terms of coverage, the models were reviewed according to the basic aspects of CnR information [75]:
In Section 3, along with the presentation of
It is important to clarify that the different CnR information aspects may be collected and recorded during different stages throughout the CnR process. For example, information regarding a CnR intervention may be collected and recorded during the stages of
Models review
CnR is a multidisciplinary domain, which lies within the wider CH domain. As such, formal ontologies which have been developed within the CH domain may cover, at different abstraction levels, CnR requirements regarding data modelling. For example, the
Another, analogous example is the more recent Though the project is still ongoing, a significant amount of ICCD data has been published and is available via a SPARQL endpoint (see
In addition to the use of CH related ontologies for the representation of the various aspects of CnR information, the CnR community has developed specialized models, which in some cases integrate and/or extend existing models of the CH domain. After a thorough bibliographic research, 16 works were identified, which were gathered, studied, and are presented below. The presentation of the reviewed works follows a chronological order (oldest first). The bibliographic research spans from 2011, when the first endeavors are dated, up to today. In case of multiple publications on the same work, the initial publication is taken into account in the ordering. Each work has been reviewed and is presented here according to six study axes:18 In cases where the published documentation of a model does not reveal details regarding a certain study axis, the corresponding sub-section is omitted.
Collaborative project between the The website of the project is no longer available: Other preservation experts are curators, materials scientists, chemists, characterization experts and information scientists.
In this context, [52] proposes the
The development of OPPRA was organized in five stages [87]: i) conceptual modelling of information relevant to conservation of paintings, ii) reuse of existing models, iii) reuse of existing controlled vocabularies, iv) extension and refinement of the reused classes and relationships, v) evaluation of ontology applicability to services developed in the context of the 20th CPaint Project. The ontology is implemented in
OPPRA combines and reuses the CIDOC CRM [25], the
OPPRA classes and relations model information related to the following thematic clusters [52,87]: i)
Correspondence between the CnR aspects and the thematic clusters covered by OPPRA
Based on OPPRA, a system that consists of an online knowledge base and intelligent services, specialized in CnR and CS is defined as the “interdisciplinary study of the maintenance, care, and protection of art, architecture, and other cultural works” [7].
OPPRA was evaluated by assessing its applicability to the aforementioned services [87]. The evaluation showed that conservators and scientists were able to document and link data using OPPRA, thereby achieving more precise data retrieval. Additionally, the automatic extraction of structured data from relevant publications achieved high accuracy. Finally, the semantic search of integrated cross-disciplinary data hosted in the knowledge base allowed more complex queries, compared to traditional data integration tools.
OPPRA used to be online available, though the url is no longer accessible.28 No longer accessible url:
The main objective of the
MONDIS project developed
The development of MDO was organized in three phases [20]: i) distinguishing the requirements of damage documentation according to literature and international standards, ii) establishment of the relations among damage factors based on CnR methodologies and workflows, iii) validation of the ontology with experts. MDO was implemented in OWL 2 using
MDO is divided in two parts: i) the core, which represents knowledge about damages of immovable CH and ii) special taxonomies which provide particular vocabularies for the documentation and analysis of damages and interventions [11]. MDO integrates (either partially or fully) the following taxonomies, thesauri, and glossaries:
The core part of MDO is divided in five thematic clusters [11,19]: i)
Correspondence between the CnR aspects and the thematic clusters covered by MDO
Based on the MDO, MONDIS knowledge-based system provides a set of tools for data import, editing, integration, processing, and visualization [18]. More specifically MONDIS includes the inputting applications i) MONDIS mobile/desktop app and ii) Ontomind profile, as well as the visualizing and supporting tools iii) MONDIS explorer, iv) knowledge matrix and v) terminology editor. MONDIS application allows the documentation (on-site for the mobile version) of the condition of historical buildings based on measurements and observations about examined damages [18,19]. The data are uploaded to the MONDIS server, and after being validated by the users -in order to verify the quality of the record- they become accessible through the MONDIS explorer. Once shown in the MONDIS explorer records can be integrated with extra information which was not collected and documented during the on-site examination of a building, via Ontomind profile. Ontomind visualizes the ontological mapping of records to the MDO as a simple tree-like structure. The records available in the MONDIS are semantically linked to their diagnosis and possible interventions which are visualized and presented to the user through the knowledge-matrix web-based application. Finally, the MONDIS terminology editor facilitates the browsing or editing of the taxonomies and term lists used in MONDIS software tools. The software tools are available online.32
Each section of MDO was validated by conducting public workshops and internal meetings [20]. Furthermore, MDO has been populated with records which were used for the presentation of MONDIS software tools functionality [18].
Both the core MDO ontology and the MDO version with the taxonomies are available online. According to the bibliographic search, MDO has been re-used by [118] for the development of a new ontology within the CnR domain (see Section 3.9).
The COSCH is the
An important outcome of the COSCH community research is
COSCHKR was developed through an iterative process where the gathered knowledge was first verified by groups of experts [55,114]. The experts participated in the specification of relevant terms and vocabularies as well as in discussions over three representative case studies, contributing to the development of COSCHKR class structure and dependencies as well as the specification of inference rules [114]. The ontology was implemented in OWL 2.
The top-level structure of COSCHKR consists of five classes interrelated through five properties [14]. In general, the COSCHKR ontology contains more than 750 classes, while its taxonomic hierarchy has on average five levels [114]. COSCHKR subclasses are associated with inference rules, which cut across the top-level classes (e.g.,
The core of COSCHKR consists of five top-level classes [14]: i)
Correspondence between the CnR aspects and the top-level classes of COSCHKR
Based on COSCHKR, [114] describes a recommender system that enables experts from different subdomains of CnR and preservation of tangible CH to put forward their queries and get answers related to documentation/analysis strategies for CH objects and applications without worrying about the complexity of the backend model [55]. Particularly, the proposed system would allow seeking answers to queries of varying complexity and invokes the model to infer underlying facts and heuristics. First of all, the system aims to help users to identify useful factors for different documentation/analysis actions as well as factors that cannot be satisfied, using implicit reasoning. Afterwards, based on those factors, the proposed system would be able to recommend solutions, providing experts with an overview of optimal spectral and spatial recording strategies according to their needs.
The three representative case studies were not only used to enrich the knowledge model but also to evaluate the inference mechanism and results [114]. The evaluation results [114] showed that the inferences of the model are satisfying, though it is highlighted that COSCHKR is still under development, and therefore the CH applications, inference rules and corresponding recommendations that are to be added in the future will improve the model and its performance.
The ontology is not yet available online, but it is intended to be documented and made publicly available [114]. Additionally, the OWL file may be provided (as noted in the project website35
The
In the context of the DOC-CULTURE project a model which is based on CIDOC CRM [25] and
For developing the CIDOC CRM extension, the intended user groups, the documentation requirements and the different CnR processes and stages were defined, and the main entities and properties of NDT&E were specified [58,109]. Next, different standards for modelling data related to the CH domain (namely
The five main entities of DOC-CULTURE model are [109]: i)
Correspondence between CnR aspects and the main entities of DOC-CULTURE
The proposed model has been used for modelling data related to conservation interventions provided by the
The model and CIDOC CRM extensions are not available online. The bibliographic search showed no evidence of the model’s re-use in later projects for the development of ontologies within the CnR domain.
In [69] a correlation pipeline is proposed for the integration of the three dimensions of a masonry structure: i)
The pipeline uses an ontology (ODPA-3DR) for recording and integrating multidisciplinary observations of the conservation state of masonry structures, spatialized into a
ODPA-3DR was developed based on
Based on the presentation of ODPA-3DR ontology in [70], five main thematic clusters can be identified: i)
Correspondence between the CnR aspects and the thematic clusters covered by ODPA-3DR
ODPA-3DR is deployed in a system for
In order to be populated for testing purposes, ODPA-3DR has been mapped to a database holding data generated by experts during digital acquisition and observation of a masonry structure (3D point cloud, scientific imagery, documents, etc.). The first results showed the possibility to calculate the overlapping degree between different annotations (associated with different description concept types) of the same structure (e.g., a wall) [70].
The ontology is not available online. The bibliographic search showed no evidence of the model’s re-use in later projects for the development of ontologies within the CnR domain.
The
In this context, [84] proposed a semantic model of CnR of tangible CH, the
The development of PARCOURS semantic model started with the definition of a core structure and the main CnR requirements of the ontology [10]. During this process scientists and domain experts of the CnR field were involved. Next, a set of sample data structures and example data related to the CnR processes was mapped to existing CH domain ontologies (EDM [24], ABC [36] and CIDOC CRM [25]). CIDOC CRM was considered as the most appropriate choice for CnR data modelling, and it was used and extended -where necessary- for the representation of the domain of interest. Finally, the developed model integrated a set of thesauri in order to tackle the problem of inconsistency among different CnR terms, at both the syntactic and semantic level. The
PARCOURS semantic model adopts a layered ontology architecture: i) a top-level ontology, ii) extensions of the top-level ontology with specialized classes, iii) specialized thesauri [83]. The PARCOURS semantic model reuses CIDOC CRM [25] and its official compatible model CRMsci [31]. Additionally, it introduces CRMcr, an extension of CIDOC CRM and CRMsci, developed in the context of the PARCOURS project [10]. The PARCOURS semantic model consists of i) 93 concepts and 82 relationships of CIDOC CRM ontology, ii) 22 concepts and 24 relationships of CRMsci and iii) 63 new concepts and 27 new relationships. Regarding the use of specialized thesauri, most of them were built during the PARCOURS project. They were managed by the
The classes and relations of the PARCOURS semantic model represent knowledge related to five main thematic clusters [10]: i)
Correspondence between the CnR aspects and the thematic clusters covered by PARCOURS semantic model
In the context of the PARCOURS project, a data integration and querying system for the CnR domain was developed based on PARCOURS semantic model, providing search and retrieval services [83,85]. The system has the form of a web portal that allows users to retrieve CnR data from multiple datasets. In order to achieve unified access to multiple datasets, the system follows a mediator approach which tackles restrictions imposed by CH institutions, allowing them to keep managing their repositories autonomously. In particular, all data sources involved in the integration process (namely
The ontology is not available online. According to the bibliographic research the extension CRMcr has been re-used by [121] for the development of a new ontology within the CnR domain (see Section 3.16).
The GRAVITATE project39 The website of the project is no longer available:
A basic outcome of the GRAVITATE project is the
The main classes of CHAP were defined based on an archaeological corpus of texts (i.e., archaeological publications, catalogues, excavation reports) as well as fundamental archeological knowledge [23]. First, the main hierarchy was developed. Next, the hierarchy was aligned to a semantic scheme suitable for representing knowledge about both artefacts and their digital counterparts. The CHAP model was edited in Protégé software [80].
The CHAP meronomy has been modelled as a SKOS hierarchy40
SKOS uses RDF in order to provide a standard way to represent knowledge organization systems.
The semantic scheme can be divided in two main conceptualization aspects [23]: i) the
Correspondence between the CnR aspects and the conceptualization aspects of CHAP semantic model
CHAP is deployed in the knowledge base of the GRAVITATE platform and it is exploited by the tools provided by the platform for analysis and annotation of 3D models of artefacts [23,90]. In particular, the GRAVITATE platform’s tools are i)
CHAP has been used for modelling the characteristics and production techniques of statues/figurines in three case studies [23]. CHAP has also been used for semantic annotation (automatic or manual), of 3D reconstructions of objects (whether whole artefacts or fragments of artefacts). The annotation of features observed on objects allows experts to search, retrieve and examine objects in juxtaposition (e.g., based on the morphological analysis of their decoration) in order to validate hypotheses regarding their production or original form (e.g., fragments that belong to the same statue) [23].
The ontology is not available online. The bibliographic search showed no evidence of the model’s re-use in later projects for the development of ontologies within the CnR domain.
The
BIM is an environment that allows the creation of virtual building models, which can be linked to numerical data, texts, images, and other types of information. It is used in the fields of
For the purposes of the BHIMM project the
For the development of CPM, the main thematic clusters of built heritage were defined. For the formalization of CPM, existing models were taken into account, namely CIDOC CRM [25], FRBRoo [35] and
Classes and relations of CPM are organized in five thematic clusters [42]: i)
Correspondence between the CnR aspects and the thematic clusters covered by CPM
Based on CPM, the authors of [1,101] present
CPM has been used to represent and manage CnR information related to the 6th-century
The CPM model file is not available online. According to the bibliographic search CPM has been re-used for the representation of conservation management of urban buildings, including their main features together with vulnerability and transformation index, using the
The authors of [118], present the
BCHO was developed using
BCHO reuses three external ontologies: i)
The basic classes of BCHO are organized in four thematic clusters [119]: i)
Correspondence between the CnR aspects and the thematic clusters covered by BCHO
So far there has been no deployment of BCHO in a particular SW system or service supporting the CnR process, though the possibility of using BCHO for data integration, 3D representation of buildings, inference making etc. is discussed [117].
In order to be evaluated, BCHO was tested in terms of its ability to represent information regarding the preventive conservation cycle of the
BCHO is available online,47
In the context of the
The development of the
The thesauri and vocabularies which were studied, include i) AAT [47], ii)
The main concepts of the
Correspondence between the CnR aspects and the main concepts of the polygnosis platform model
In the first workshop the users were young researchers, scholars, graduate students, and professionals, while in the second the users were conservation students, graduates and professionals.
Both the
The European project HERACLES (
An important output of this research was the HERACLES application ontology, a semantic model which covers the preservation of immovable CH. As such, the primary object of the HERACLES ontology is the efficient integration, exchange and retrieval of data related to climate change impact, which are often unstructured, incompatible or in some cases partial.
The HERACLES ontology was developed following a workshop-based approach, while the
The sources used as reference material for the HERACLES ontology, include: i) SWEET ontologies [96], ii) the
HERACLES core classes are [48,49]: i)
Correspondence between the CnR aspects and the core classes of HERACLES
Correspondence between the CnR aspects and the core classes of HERACLES
The HERACLES ontology serves as the backbone of the HERACLES knowledge base: every entry in the knowledge base is an instance of the ontology [49]. HERACLES knowledge base collects and integrates multisource information in order to effectively i) provide complete and up-to-date awareness about the conditions occurring in a CH site and ii) support retrieval and decision making for innovative measurements improving CH resilience. Particularly, HERACLES platform provides input forms, through which data are semantically integrated. The input form contains several text fields (e.g., for textual descriptions), while links to other instances can be created through selecting elements from lists. Additionally, an online endpoint is provided to facilitate instance creation/deletion. Regarding presentation of data, for each entry the system provides images and quick links to useful related information (e.g., damages, reports, sensor data). This endpoint is also used by the HERACLES mobile application, which allows reporting of damages on site, by delivering information such as location coordinates and description, as well as pictures, video footage etc. to the HERACLES knowledge base, in order to be presented to the back-end user.
Both the HERACLES platform and the HERACLES ontology have been evaluated in the context of four use-cases [49]. The use-cases included: i) Minoan Palace of Knossos in Heraklion, Crete, ii) Venetian Sea Fortress of Koules in Crete, iii) Consoli Palace in Gubbio, Italy and iv) the town walls in Gubbio, Italy. These test sites represent key case-studies for the impact of climate change on European CH assets. Using the HERACLES ontology made possible the semantic integration of collected data, while the platform effectively supported the retrieval of data required by experts in order to prepare reports for monuments condition state and environmental conditions [49].
The ontology is available online.50
The COST action
In the context of COST-TD1406, an ontology for the
HBCO was developed using METHONTOLOGY methodology [41], while it was encoded in OWL, using Protégé [80].
The following terminologies and ontologies were reused for the development of HBCO:
The main subgroups of the HBCO concepts are [104]: i)
Correspondence between the CnR aspects and the main concepts’ subgroups of HBCO
HBCO was populated with HB data about 12 building projects [105,106]. Furthermore, [104] theoretically describes a platform for storage and management of HB data.
The ontology is not available online. The bibliographic search showed no evidence of the model’s re-use in later projects for the development of ontologies within the CnR domain.
The work presented in [78] proposes the
The CORE ontology extends certain classes from CIDOC CRM [25], in order to facilitate CnR data modelling. The extension of CIDOC CRM was conducted in a bottom-up manner based on empirical analysis, scientific knowledge and existing CnR vocabularies [78]. Finally, inference rules were formulated in order to facilitate semantic querying [78].
For the development of CORE, a number of CIDOC CRM classes and relations were reused in combination with existing CnR vocabularies (namely AAT [47], CAMEO [72] and AIC wiki [116]). The ontology includes general concepts that refer broadly to CnR of artwork, as well as concepts specific to CnR of byzantine icons. The ontology was developed in OWL using Protégé [78,80].
The main classes of the CORE ontology are [78]: i)
Correspondence between the CnR aspects and the main classes of CORE
Correspondence between the CnR aspects and the main classes of CORE
In order to be evaluated, CORE was populated with selected data from conservation reports, while CQs were formed as SPARQL and SPARQL DL queries. According to the evaluation results [78], modelling CnR data using CORE allowed for the formulation of queries such as “Which are the structural layers that have ever been recorded about an object?” and “Which are the structural layers of an object since the last conservation treatment?”. Such queries can assist the CnR professional during the collection of information about the history and condition of a conservation object.
The ontology is not available online. The model has been re-used for the representation of data related to preventive conservation and sensor data [76].
An ontology for the CnR of ancient buildings (CABD) is proposed in [113]. CABD was developed in order to facilitate querying and case-based reasoning of CH information related to ancient Chinese building damages and repairs.
For the development of CABD, information related to damages in ancient Chinese buildings was analyzed, focusing on the process of selecting the appropriate CnR method for damage repair. The mapping of specific repair methods to specific damage cases was implemented in the form of SWRL rules [113]. The ontology was developed in Protégé [80].
The main classes of CABD are [113]: i)
Correspondence between the CnR aspects and the main classes of CABD
Correspondence between the CnR aspects and the main classes of CABD
To be tested, the proposed model was populated with data describing cracks on the surfaces of buildings of WuDian type, together with the appropriate repair method. Based on these data, the ontology was used for retrieving cases of damages and corresponding repair methods that present similarities with a given case (target case), by formulating SPARQL queries [113].
The ontology is not available online. The bibliographic search showed no evidence of the model’s re-use in later projects for the development of ontologies within the CnR domain.
A framework for a
NMOHB adopts a layered architecture that incorporates: i) core ontologies, which provide the basic concepts related to construction, ii) specialized taxonomies which extend the core ontologies, and iii) metadata ontologies, which are used for data exchange.
In particular, a number of complementary ontologies were developed in the context of the research (namely
The concepts of the core ontologies of the NMOHB are organized in the following groups [13]: i)
Correspondence between the CnR aspects and the main concepts’ groups of NMOHB
Correspondence between the CnR aspects and the main concepts’ groups of NMOHB
To be tested, the developed ontology network was deployed in five example cases, inspired by two built heritage projects in Ghent [13]. The example cases demonstrate the key functionalities of NMOHB and underlying technologies, which are: i) the possibility of linking and using geometry from a variety of common geometry formats, ii) the use of flexible and integrated classification systems, iii) the provision of combined views on previously disparate datasets, iv) the provision of feedback mechanisms for construction damages and tasks based on structured data and v) the provision of metadata about construction datasets to manage the flow of internal and exchanged datasets. As discussed in [13] the outcome of the evaluation phase indicated that most requirements related to the data structure and management workflows (e.g., data exchange) have been addressed successfully by the proposed modular ontology network. However, it is highlighted that more contributions of domain experts are considered necessary to maintain, extend and verify the content of the NMOHB taxonomies. Especially the property taxonomies, damage and task classification are currently not extensive enough for use in real projects.
All the different ontologies of the network (BOT,52
The
In this context, [121] proposes the
CRMBnF was developed in close collaboration with domain experts at the BnF and is encoded in RDF/S 2 and OWL. For developing the ontology three external ontologies were re-used [121]: CIDOC CRM [25], CRMsci [31] and CRMcr [10].
CRMBnF includes two main classes [121]: i)
Correspondence between the CnR aspects and the main classes of CRMBnF
CRMBnF has been used for the comparison of the events between conservation trajectories, a process which serves as a base to build an adequate predictive model for the decision support system [121]. The comparison is performed by computing a similarity score using the ontology, considering the relative position of the concepts corresponding to the names of the events in the ontology.
Three experiments were conducted, using real conservation and communication datasets from the BnF. The first two experiments aimed to assess the effectiveness of using the ontology for finding matching events and for evaluating the similarity of trajectories. The third experiment aimed to show the effectiveness of using the ontology and the LCESS measure by comparing the computed similarity to a gold standard [121]. According to the results, the proposed ontological approach improves the precision of the matching process [121].
The ontology is not available online. The bibliographic search showed no evidence of the model’s re-use in later projects for the development of ontologies within the CnR domain.
The study of the different representations of CnR knowledge and their deployment in systems and services that exploit SW technologies and methods revealed some interesting points of convergence or divergence, which are discussed in the following sections. The study findings are overviewed in Table 17 and are organized and discussed according to three axes: i) content, ii) re-use of existing models, and iii) deployment.
Overview of the reviewed works according to i) content, ii) re-use of existing models and iii) deployment
Overview of the reviewed works according to i) content, ii) re-use of existing models and iii) deployment
(Continued)
ADM: administration, MAT: materials & technology, ALT: alteration, INV: investigation, INT: intervention.
INTEG: data integration, SEA: semantic search, VIS: visualization, ANN: semantic annotation, FEA: feature recognition, REC: recommendation of digitization and analysis methods.
Obviously, a common requirement is the modelling of the
Figure 1 depicts the degree to which the CnR aspects defined in Section 2 are covered by the reviewed models. The
Coverage of CnR aspects.
An interesting observation regarding the content is that different models allow different granularities for categorization of the various concepts. For example, the CPM model represents a building (i.e., the conservation object) using classes of specific building types (
While the scope and context of the reviewed works may differ, there is a common interest for providing interoperability of CnR data. Towards that direction, most of the reviewed models were developed either from scratch and were then mapped/aligned to existing ontologies, or they were built entirely by extending existing ontologies (with the exception of MDO, COSCHKR, and CABD).
The CIDOC CRM ontology, as well as its compatible models, were largely adopted by the majority of the projects. Additionally, specialized ontologies from other knowledge domains relevant to CnR were adopted for the development of the models. Figure 2 depicts the origin domain of the various ontologies, metadata standards, term lists and thesauri that are re-used by the reviewed models.
Most of the works took into account and adopted ontologies from other fields such as the field of
The origin domain of the ontologies, metadata standards, terms lists and thesauri that are reused by the reviewed models.
All the models included in the current survey have been employed for developing SW systems that offer various domain-specific services. In the course of the survey, we identified a number of services that are common among those systems. In particular:
Figure 3 depicts the degree to which the identified services are provided by the systems built upon the reviewed models. It should be mentioned that 5 out of 16 reviewed models (namely BHO, HBCO, CORE, CABD and CRMBnF) have not been deployed in some system, though they have been used for data modelling, testing SPARQL queries and comparison of annotated data. It should be mentioned that CRMBnF is supposed to be used in a decision-support system, for conservation objects classification based on their history.
Apparently,
Decision-making plays a central role in the
Provision of SW services by systems built upon the reviewed models.
On the other hand,
As discussed in the previous section, decision-making constitutes the backbone of the CnR task. Through decision-making the expert transforms various chunks of possibly diverse information relevant to a conservation object, such as scientific information (e.g., material ageing), administrative (e.g., loaning preconditions) or even cultural information (e.g., historical value), into concrete and specific
Drawing on the above, it is strongly suggested that further research should be conducted in order to analyze and conceptualize intervention decision-making at a granularity that will allow a more thorough representation, suitable to drive the implementation of services that will deliver intervention recommendations, as an explicit