Ontology in underwater archaeology.

The carried out photogrammetric survey is based on an original approach of underwater photogrammetry that was deployed with the help of a specific instrumental infrastructure provided by COMEX. This photogrammetry process, as well as the body of surveyed objects, were formalized in an ontology expressed in OWL2. The ontology developed in the framework of this project takes into account the manufactured items surveyed, as well as the method used to measure them, in this case, photogrammetry. The surveyed item is therefore represented from the measurement point of view and has access to all the photogrammetric data that contributed to its measurement in space. Two ontologies are aligned in this context; one dedicated to photogrammetric measurement and the geo-localization of the measured items, whereas the other is dedicated to the measured items , principally the archaeological artefacts, describing their dimensional properties, ratios between main dimensions, and default values.

Mapping de l'ontologie photogrammétrie/CIDOC-CRM

These ontologies are developed closely linked to the Java class data structure that manages the photogrammetric process as well as the measured items. Each concept or relationship in the ontology has a counterpart in Java (the opposite is not necessarily true). Moreover, surveyed items are also archaeological items studied and possibly managed by archaeologists or conservators in a museum. It is therefore important to be able to connect the knowledge acquired when measuring the item with the ontology designed to manage the associated archaeological knowledge. CIDOC CRM is a generic ontology that does not support the items that it represents from a photogrammetric point of view, a simple mapping would not be sufficient and an extension with new concepts and new relationships would be necessary. The extension of the CIDOC-CRM ontology is structured around the concept (E22 Man-Made Object). The root of ItemMesurable developed in this project extends this concept. The mapping operation is done in Java by interpreting a set of data held by the Java classes as a current identification of the object: 3D bounding box, specific dimension. These attributes are then computed in order to express the right CRM properties. Several methodologies can be chosen regarding mapping these two ontologies. We see here that the mapping problem is close to an alignment problem , which is really problematic in this case. Aligning two ontologies dealing with digital camera definition is not obvious; a simple observation of the lack of interoperability between photogrammetric software shows the wildness of the problem. We are currently working on an alignment/extension process with Sensor ML which is an ontology dedicated to sensors.

click here or image to open a full screen view of complete Arpenteur ontology

OWL (Web Ontology Language) has been used as a standard for the implementation of ontologies. In its simplest form, it allows for representing concepts (classes), instances (individuals), attributes (data properties) and relations (object properties). The ontology construction in OWL, symmetric to the JAVA taxonomy, cannot be produced automatically. Each concept of the ontology has been constructed with a concern for the representation of accurate knowledge from a particular point of view: measurement. Reading an XML file used to serialize a JAVA instance set representing a statement can immediately populate the ontology; similarly reading an OWL file can generate a set of JAVA instance counterparts of the individuals present in the ontology. The link between individuals and instances persists and it can be used dynamically. The huge advantage of this approach is that it is possible to perform logical queries on both the ontology and the JAVA representation. We can thus read the ontology, visualize in 3D the artefacts present in the ontology, and graphically visualize the result of SQWRL queries in the JAVA viewer.

The ontologie editor: classes, instances and properties edition from OWL file

We decided to develop some tools in order to be display data stored in ontologies as a virtual relational database in order to have a simplified view of the stored data. These tools can provide a static point of view on these ontologies and allow simple manipulations similar to those possible on a relational database. The first one is an editor which displays classes, instances and properties present in a ABox as if the data were structured by table in a relational database. By reading the ABox, the editor is able to display the classes present in the ontology, all their instances and offers a simple and dynamic way to display data properties as normal fields of these instances and the possibility to modify them (an also, as we are in ontology context, to add or remove fields). This of course gives us a lot of other possibilities as the archaeological work is always in progress and a modification of the data structure is always possible and easy to implement through this interface.