Christophe Cruz

Architectural Reconstruction of 3D Building Objects through Semantic Knowledge Management

This paper presents an ongoing research which aims at combining geometrical analysis of point clouds and semantic rules to detect 3D building objects. Firstly by applying a previous semantic formalization investigation, we propose a classification of related knowledge as definition, partial knowledge and ambiguous knowledge to facilitate the understanding and design. Secondly an empirical implementation is conducted on a simplified building prototype complying with the IFC standard. The generation of empirical knowledge rules is revealed and semantic scopes are addressed both in the bottom up manner along the line of geometry -> topology -> semantic, and a vice versa top down manner. Concrete implementation is on the platform of protégé with Semantic Web Rule Language (SWRL).

Continuum: a spatiotemporal data model to represent and qualify filiation relationships

This work introduces an ontology-based spatio-temporal data model to represent entities evolving in space and time. A dynamic phenomenon generates a complex relationship network between the entities involved in the process. At the abstract level, the relationships can be identity or topological filiations. The existence of an identity filiation depends on whether the object changes its identity or not. On the other hand, topological filiations are based exclusively on the spatial component, like in the case of growth, reduction, merging or splitting. When combining identity and topological filiations, six filiation relationships are obtained, forming a second abstract level. Upper-level filiation relationships provide better semantic vocabulary to describe the modeled phenomena, thus allowing the implementation of spatial, temporal and identity constraints. In this paper, we present a method based on identity and topological filiation relationships, to improve the capabilities of standard knowledge bases using Semantic Web technologies. Our method enables us to check the consistency of spatio-temporal and semantic data. An example is given in the field of urban growth to show the capabilities of the model.

Semantic Building Information Model and Multimedia for Facility Management

In the field of civil engineering, the proliferation of stakeholders and the heterogeneity of modeling tools detract from the quality of the design process, construction and building maintenance. In this paper, we present a Web-based platform lets geographically dispersed project participants—from facility managers and architects to electricians to plumbers—directly use and exchange project documents in a centralized virtual environment using a simple Web browser. A 3D visualization lets participants move around in the building being designed and obtain information about the objects that compose it. This approach is based both on a semantic architecture called CDMF and IFC 2x3. Our framework, based on Building Information Modeling features, facilitates data maintenance (data migration, model evolution) during the building lifecycle and reduces the volume of data.

Managing Semantics Knowledge for 3D Architectural Reconstruction of Building Objects

this work aims at bound geometrical detection of 3D objects from a point cloud using semantic descriptors to improve reusability of architectural building reconstruction and aid automatic reasoning in building information modeling (BIM). Based on exploring cognitive origins of spatial semantics representations, semantics conceptualization and classification is proposed for management of architectural objects. The knowledge classification is formalized with transformations among closed world assumption (CWA) and open world assumption (OWA). Initial case study of a building prototype complying with the IFC standard reveals the organization of empirical knowledge rules and semantics scopes both in a bottom up manner of geometry à topologyà semantics, and vice versa.

Transforming XML documents to OWL ontologies: A survey

The aims of XML data conversion to ontologies are the indexing, integration and enrichment of existing ontologies with knowledge acquired from these sources. The contribution of this paper consists in providing a classification of the approaches used for the conversion of XML documents into OWL ontologies. This classification underlines the usage profile of each conversion method, providing a clear description of the advantages and drawbacks belonging to each method. Hence, this paper focuses on two main processes, which are ontology enrichment and ontology population using XML data. Ontology enrichment is related to the schema of the ontology (TBox), and ontology population is related to an individual (Abox). In addition, the ontologies described in these methods are based on formal languages of the Semantic Web such as OWL (Ontology Web Language) or RDF (Resource Description Framework). These languages are formal because the semantics are formally defined and take advantage of the Description Logics. In contrast, XML data sources are without formal semantics. The XML language is used to store, export and share data between processes able to process the specific data structure. However, even if the semantics is not explicitly expressed, data structure contains the universe of discourse by using a qualified vocabulary regarding a consensual agreement. In order to formalize this semantics, the OWL language provides rich logical constraints. Therefore, these logical constraints are evolved in the transformation of XML documents into OWL documents, allowing the enrichment and the population of the target ontology. To design such a transformation, the current research field establishes connections between OWL constructs (classes, predicates, simple or complex data types, etc.) and XML constructs (elements, attributes, element lists, etc.). Two different approaches for the transformation process are exposed. The instance approaches are based on XML documents without any schema associated. The validation approaches are based on the XML schema and document validated by the associated schema. The second approaches benefit from the schema definition to provide automated transformations with logic constraints. Both approaches are discussed in the text.

Integration of knowledge to support automatic object reconstruction from images and 3D data

Object reconstruction is an important task in many fields of application as it allows to generate digital representations of our physical world used as base for analysis, planning, construction, visualization or other aims. A reconstruction itself normally is based on reliable data (images, 3D point clouds for example) expressing the object in his complete extent. This data then has to be compiled and analyzed in order to extract all necessary geometrical elements, which represent the object and form a digital copy of it. Traditional strategies are largely based on manual interaction and interpretation, because with increasing complexity of objects human understanding is inevitable to achieve acceptable and reliable results. But human interaction is time consuming and expensive, why many researches has already been invested to use algorithmic support, what allows to speed up the process and to reduce manual work load. Presently most of such supporting algorithms are data-driven and concentate on specific features of the objects, being accessible to numerical models. By means of these models, which normally will represent geometrical (flatness, roughness, for example) or physical features (color, texture), the data is classified and analyzed. This is successful for objects with low complexity, but gets to its limits with increasing complexness of objects. Then purely numerical strategies are not able to sufficiently model the reality. Therefore, the intention of our approach is to take human cognitive strategy as an example, and to simulate extraction processes based on available human defined knowledge for the objects of interest. Such processes will introduce a semantic structure for the objects and guide the algorithms used to detect and rexognize objects, which will yield a higher effectiveness. Hence, our research proposes an approach using knowledge to guide the algorithms in 3D point cloud and image processing.

Integration of spatial processing and knowledge processing through the semantic web stack

This paper presents the integration process of spatial technologies and Semantic Web technologies and its associated tool. The result of this work is a spatial query and rule engine of spatial. To do so, existing ontology with spatial elements is adjusted in order to process the spatial knowledge through spatial technologies. This paper outlines the methods and the processes of these adjustments and how results are returned by our tool. The SWRL and the SPARQL language are extended for spatial purpose and the existing OWL ontology wine is used as an application example.

Use of geospatial analyses for semantic reasoning

This work focuses on the integration of the spatial analyses for semantic reasoning in order to compute new axioms of an existing OWL ontology. To make it concrete, we have defined Spatial Built-ins, an extension of existing Built-ins of the SWRL rule language. It permits to run deductive rules with the help of a translation rule engine. Thus, the Spatial SWRL rules are translated to standard SWRL rules. Once the spatial functions of the Spatial SWRL rules are computed with the help of a spatial database system, the resulting translated rules are computed with a reasoning engine such as Racer, Jess or Pellet.

Modeling Value Evaluation of Semantics Aided Secondary Language Acquisition as Model Driven Knowledge Management

Many theories and solutions have been proposed for the improvement of the learning efficiency of secondary language(L2) learning. However neither an unified view on the functionality of semantics in aiding learning nor an objective measure of the efficiency improvement at theoretical level has been presented in existing literature. This situation hinders the efficient adoption of the semantics aided learning and the explicit planning of a semantics aided learning process. We aim to fill these gaps by adopting an evolutionary strategy towards approaching a holistic solution. Firstly we model the general learning process from cognitive linguistic perspective at the memory level. Then we justify the functionality of semantics aided approach according to specific conditions. Thereafter we propose the quantity measure for the improvement of learning efficiency in terms of reuse level for semantics aided secondary language learning from the perspective of value based analysis.

From Unstructured 3D Point Clouds to Structured Knowledge - A Semantics Approach

Over the last few years, formal ontologies has been suggested as a solution for several engineer problems, since it can efficiently replace standard data bases and relational one with more flexibility and reliability. In fact, well designed ontologies own lots of positive aspects, like those related to defining a controlled vocabulary of terms, inheriting and extending existing terms, declaring a relationship between terms, and inferring relationships by reasoning on existent ones. Ontologies are used to represent formally the knowledge of a domain where the basic idea was to present knowledge using graphs and logical structure to make computers able to understand and process it, (Boochs, et al., 2011). As most recent works, the tendency related to the use of semantic has been explored, (Ben Hmida, et al., 2010) (Hajian, et al., 2009) (Whiting, 2006) where the automatic data extraction from 3D point clouds presents one of the new challenges, especially for map updating, passenger safety and security improvements. However such domain is characterized by a specific vocabulary containing different type of object. In fact, the assumption that knowledge will help the improvement of the automation, the accuracy and the result quality is shared by specialists of the point cloud processing.