The MONDIS system is a knowledge-based tool accessible from this web portal (link). It is grounded on an ontological representation of the field of heritage conservation, called Monument Damage Ontology, which allows replicating in a computer readable form the basic dependence among factors influencing the description, diagnosis and intervention of damages to immovable objects. In fact, deriving from the complex nature of cultural heritage conservation it is the need for enhancing a systematic but flexible organization of expert knowledge in the field. Such organization should address comprehensively the interrelations and complementariness among the different factors that come into play in the understanding of diagnostic and intervention problems. The purpose of MONDIS is to endorse this kind of organization.

System1  System2

MONDIS system supports the digitalization of a wide range of data including professional reports, books, articles and scientific papers. Differently from conventional databases, MONDIS is also able to provide automatic reasoning supporting and facilitating user's interaction and retrieving more efficiently relevant information from stored data. Furthermore the system is characterised by its capacity to continuously extend its contents and update its reasoning schemes accordingly with the level of knowledge stored by reporters. Moreover the system allows processing of incomplete information (i.e. no minimum input is required).

MONDIS provides its potential users with two basic actions to be performed, namely inputting and searching. 

Inputting allows users to create an entry in the system and to insert data following a flexible and adaptable sequence of steps in a way that resembles professional reasoning. Two typologies of entries can be distinguished: general knowledge entries and case study entries. The first one refers to ´generalised rules´ for connecting two concepts in the model. These rules can be found in books, paper and articles: e.g. the information ´masonry has very low tensile strength' provides a general rule for connecting ´masonry´ material to ´low tensile strength´ qualitative measurement. Its generality derives from the fact that such statement can be considered valid for all masonries. Case study entries, on the other hand, provide exact rules that cannot be generalised. Case studies present in fact peculiar rules for relating concepts within the model. An example can be ' Prague Castle has 2mm wide cracks on the external side of the walls due to freeze and thaw cycles'. The statement ´crack has 2mm width´ is not in fact true for all cracks and therefore is specific to the case is being inputted. Throughout the inputting function, the user is guided through the model by the artificial intelligence constituted by the formalised relationships in the model and also by previously stored information. 

Searching function permits exploring existing knowledge by computer-aided retrieval tailored to users´ query. The relationships among concepts formalized in the ontological model allow narrowing down searching to logically related information only maximizing the contextualization of the output. Searching the system performs a computer-aided retrieval of information tailored to user's level of knowledge. Considering searching for a general manifestation of damage ´biological colonization´, the system would provide to the user the stored information concerning for example the components on which this damage can be found, the mechanism that has generated it, possible repairs etc. Whenever the search if refined, by setting further parameters such as ´insect infestation of wooden beam´, the knowledge retrieved narrows down to a smaller set of data with higher granularity (i.e. with deeper level of knowledge). As for inputting, searching function is also supported by automatic reasoning that direct user´s query towards meaningful data.

The simple input and search examples outlined above well explain the powerful tool that the ontology might represent in the context of a vast domain, such that of cultural heritage protection, which lacks adequate knowledge organization and non-expert user accessibility.


The current graphical interface is based on OntoMinD tool for the visualization of the ontological model and for testing of its functions. A finalised GUI is expected to be accessible on this website in the near future. 

Monument Damage Ontology

What is an ontology?

An ontology defines a set of representational primitives with which to model a domain of knowledge or discourse.  The representational primitives are typically classes (or sets), attributes (or properties), and relationships (or relations among class members).  The definitions of the representational primitives include information about their meaning and constraints on their logically consistent application. Due to their independence from lower level data models, ontologies are used for integrating heterogeneous databases, enabling interoperability among disparate systems, and specifying interfaces to independent, knowledge-based services. 

Semantic web ontologies are an excellent choice for representing knowledge about cultural heritage resources, because of several reasons. First of all, ontologies naturally model incomplete and heterogeneous knowledge intrinsic to the cultural heritage domain. Second, ontologies help facing poor terminology standardization in the cultural heritage domain – even if domain experts use own terminology for representing same concepts, the terms can be comfortably identified by an ontological mapping. Third, semantic web ontologies can be published in the form of Linked Data [2], thus fostering exploitation by search engines – this semantization of knowledge significantly improves accuracy and relevance of search results. Nevertheless semantic web technologies offer an important contribution to artificial data processing and they can positively affect pursuing the main scopes of built heritage conservation. 


The MONDIS project focusses primarily on the development of an ontological framework able to coordinate an automated reasoning behind the documentation of damages to built heritage, their diagnosis and possible interventions. The Monument Damage Ontology (Blaško et Al., 2012) aims at producing a conceptual model in which the factors relevant to cultural heritage domain and their interrelations are formalised. The approach employed in building the ontology consists of three different phases: individuation of relevant parameters necessary for the documentation of damages, taken from distinguished literature and international standards such as surveying forms and object classification guidelines; establishment of the relationships among factors, deriving from professional methodologies and workflows; testing the validity of the ontology section by section at public workshops and internal meetings.


The model includes the following:

The component and construction description section (in green) allows assigning the cultural heritage object with physical and functional characteristics: complex and normal objects can be described by defining respectively multiple or single constructions (defined as a distinguishable ´whole´ which contains components); structural and functional types provide further details concerning the resisting scheme of the construction and its functionality; component and its sub-components can be defined by determining a hierarchical organization of object´s parts (e.g. floor has sub component flooring which has subcomponent plank), finally material can be set for each component/subcomponent. Other parameters such as use and style complete the basic description of the construction.

The events cluster (in yellow) individuates those occurrences which can influence the conditions of the object (specified by a temporal reference). These include natural disasters, object changes and location characteristics changes. Natural disaster class is modelled as the activation of a hazard (e.g. earthquake hazard, flood hazard, landslide hazard etc.); object change class includes those sub classes which can produce a relevant impact on the state of the object, namely functional change (e.g. farm changes to a museum), structural change (e.g. component addition, removal or substitution), property change (effects of an event on some physical characteristics e.g. bending strength change due to an intervention or due to a damage that occurred on a beam) relevant manifestations of damage (crack, deformation, collapse) and past (intrusive) interventions(strengthening, cleaning, rehabilitating); location characteristics change class refers instead to changes in the geo-morphological, hydro-geological and environmental conditions of the site. 

Events strongly relate to the damage diagnosis and intervention section of the model (in orange). The diagnostic phase is in fact represented by the interplay between events, mechanisms, agents and manifestation of damage. Events can induce damaging processes (referred to as mechanisms). Mechanisms (e.g. bending, capillary rise) in turn might result in the formation of a tangible and detectable damage (referred to as manifestation of damage) such as cracks, deflection, loss of material etc. Agents are defined as the carrying factors of a damaging mechanism (e.g. temperature or water). Intervention individuates those actions taken in order to prevent an event (e.g. fencing to prevent vandalism), to repair a manifestation of damage (e.g. filling to repair crack gaps), to stop a mechanism (e.g. strengthening to stop buckling) and to eliminate an agent (e.g. introduction of water proof membrane to eliminate water infiltration).

Risk assessment cluster (in pink) represents the interaction between hazard at a location, component vulnerability and component value. This section of the ontology provides useful insights on possible actions which could help mitigating the risk (such as preventive interventions) and, more importantly, on whether the situation is risky enough to require interventions or not. 

In order to provide the possibility to assess the magnitude of some of the factors of the model, an independent measurement assessment cluster (in light blue) is proposed. This part of the ontology allows documenting qualitatively and/or quantitatively measurable entities (individuated in the model by ´ ' icon): data concerning surveyed components (e.g. height or thickness of a wall), reported damages (e.g. width of a crack), measured agents (e.g. stresses in a pillar, moisture content in masonry) and risks (e.g. high risk of slender structures to earthquake).

The other topics cluster includes those classes necessary for the functioning of the model which are usually integrated from already existing ontologies (e.g. temporal entity, spatial thing). 

It should be underlined that each class introduced in the model presents an internal structure called taxonomy. Taxonomies involve a hierarchical ordering of terms that enhances an appropriate categorisation of concepts based on the selection of a governing parameter (e.g. taxonomy of walls based on their structural characteristic). Taxonomies can be extracted directly from relevant literature such as for example damage catalogues (Snethlage, 2010).



Examples of damage documentation

The following examples show the ability of MONDIS system to process a variety of data deriving from damage analysis, surveys, testing and other actions aimed at deepening the knowledge concerning cultural heritage objects. All examples have been modelled through the trial interface that can be tested here (link to OntoMind). The development of an enhanced tool capable to offer a more user friendly environment is currently under development and will be published soon on this portal.  



Let´s consider documenting a damage produced by a seismic event on a masonry bell tower. The record would fit the model as follows: 

OBJECT DESCRIPTION: construction type: tower; structural type: cantilever; functional type: bell tower; material: masonry; EVENT:  initial construction: 1300; earthquake: 2012 –induces → MECHANISM: In-plane mechanism (shear) –produces → MANIFESTATION OF DAMAGE: Crack—has measurement → QUANTITATIVE MEASUREMENT:     (width) 4mm. The deductive processing of such information by the system allows the user to obtain for instance clues concerning possible interventions aimed at repairing the manifestation of damage (e.g. by injecting the crack) and at stopping the mechanism (e.g. by increasing shear resistance of the structure). 

It should be noted that regardless the modelling approach (i.e. the patterns used in the model to represent the case) the data have the same ontological meaning denoting the stability of the model itself. Information is therefore independent on input sequence.




Let´s consider the case of inputting the results from laboratory mechanical tests carried out on mortar samples and in-situ physical assessment of bricks taken from an old masonry wall.

System12  System13  System14

Such record can be modelled as follows:


MATERIAL ' Brick masonry' is composed of hydraulic lime mortar and brick units (by using relation ' consists of'). Mortar samples (only two measurements are shown for simplifying the example) are tested for flexural and compressive strengths. Mortar ' has quantitative measurement' sampleF1 presents the results of one flexural test: 'hasAssessmentMethod' specifies the typology of test (laboratory testing); ' hasMeasuredQuantitativeAspect' allows to input the quantity measured, in this case ' FlexuralStrength'; 'hasNumericValue' concerns the figure measured, in this case 3.4 which together with ' hasUnitOfMeasure' and 'hasMeasurementScale' allows of inputting the information MPascal as units and strength as scale of the measurement  (such class is extremely useful in case of predefined qualitative scela, for example low/medium/high); 'hasTemporalEntity' provides the testing date, with different degrees of approximation (e.g. year; month and year; exact date dd/mm/yyyy). The second measurment Mortar ' has quantitative measurement' sampleC1 refers to the compression test. This part of the record follows exactly the same structure as the one for sampleF1 only with different details concerning the measured quantity, in this case compressive strength, the result of the test (i.e. 'NumericValue') and the date of testing. Considering standard testing procedure, the record can be inputted by replicating the model section described for three measurements of flexural strength (sampleF1, sampleF2 and sampleF3) and six of compressive strength (sampleC1, etc.). Bricks are assessed in-situ. The record includes an example of qualitative assessment: brick sampleB1 ' hasQualitativeMeasurement' physicalMeasurement with 'hasMeasuredQualitativeAspect' color and 'EnumeratedValue', i.e. the measured value of that quality, being red. Furthermore a quantitative assessment concerning water absorption properties, measured onsite (by using relation 'hasAssessmentMethod' 'in-situTestingMethod'), is included following the same structure as per the measurement of mortar strengths.


Let's consider documenting the structural evolution of an abandoned country house, accordingly to the diagram.


EVENTS ´componentAddition´ are added to ´complex´ object. Starting from ComponentInitialConstruction ´chateau´ (has dating → 1699), which includes subcomponent ´tower´ (has component addition → ComponentInitialConstruction → has IntroducingComponent → tower) the component ´floor elevation´ is added in 1747/1748 by using ´has dating´ relationship. Following phases of the structural evolution of the complex is described by two more events ´componentAddition´ respectively components ´East wing´ and ´West wing´ (has dating → 1812) and component ´chapel´ (has dating → 1816) which has a subcomponent addition ´front façade´(has dating → 1897).


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