A Rule Based System for Semantical Enrichment of Building Information Exchange

International audienceIn the area of building construction and management, the dematerial-ization of data and processes has been a global issue for the past 10 years. Go-ing beyond the geometric representation of a building, Building Information Modeling (BIM) is an approach that aims at integrating into one single system heterogeneous data and processes from different actors. Such integration is a complex and fastidious task. The implementation of the related processes for data querying, retrieval or modification is not less difficult. To tackle this prob-lem, we have developed a novel approach based on Semantic Web technolo-gies. In doing so, we have defined an ontology inspired on IFC standard for rep-resenting building information. On top of this ontology, we have defined and implemented a set of SWRL rules. The paper at hand describes these rules and their application in the context of building information handling (notably by means of IFC files

Ontology: Core Process Mining and Querying Enabling Tool

Ontology permits the addition of semantics to process models derived from mining the various data stored in many information systems. The ontological schema enables for automated querying and inference of useful knowledge from the different domain processes. Indeed, such conceptualization methods particularly ontologies for process management which is currently allied to semantic process mining trails to combine process models with ontologies, and are increasingly gaining attention in recent years. In view of that, this chapter introduces an ontology-based mining approach that makes use of concepts within the extracted event logs about domain processes to propose a method which allows for effective querying and improved analysis of the resulting models through semantic labelling (annotation), semantic representation (ontology) and semantic reasoning (reasoner). The proposed method is a semantic-based process mining approach that is able to induce new knowledge based on previously unobserved behaviours, and a more intuitive and easy way to represent and query the datasets and the discovered models compared to other standard logical procedures. To this end, the study claims that it is possible to apply effective reasoning methods to make inferences over a process knowledge-base (e.g. the learning process) that leads to automated discovery of learning patterns and/or behaviour

An ontology-based semantic building post-occupancy evaluation framework and its application

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonCatering to sustainable development in Architecture, Engineering and Construction (AEC) industry, many building performance evaluation (BPE) schemas have been developed to support building assessment and aim to narrow down the performance gap. Post-Occupancy Evaluation (POE), viewed as a sub-process of BPE, is a systematic method to obtain feedback on building performance in use. However, building evaluation is a complex and knowledge-intensive process with scattered and fragmented knowledge, it is time-consuming and error-prone to acquire explicit knowledge. Benefiting from the advantages of Semantic Web technology in knowledge conceptualization, ontology, as the core of the Semantic Web, has been widely taken as an effective method for knowledge management, information representation and extraction, and logical inference in the AEC industry, especially in the BPE field. However, most of the existing ontologies in the AEC industry are lightweight ontologies that mainly focus on building a structured system to represent the specific domain knowledge or information, without developing formal axioms and constraints to provide higher expressivity. Moreover, the research focus of ontology in building assessment is mainly on energy-related fields, and there is not a comprehensive POE ontology yet, especially with the focus on building occupant satisfaction, which is the starting point of this research. This research develops an ontology-based post-occupancy evaluation framework dedicated to building performance assessment, with the ultimate aim of optimizing building operation and improving building occupants' use experience quality and well-being. In the developed framework, a heavyweight ontology is developed to structure the fragmented building performance assessment knowledge in the POE domain. In POE ontology, the building occupants' needs for building performance are generalized and classified, and the corresponded building performance assessment knowledge is formalized. In addition, a set of SWRL (Semantic Web Rule Language) rules and SQWRL (Semantic Query-Enhanced Web Rule Language) query rules are developed based on the benchmarking evaluation axioms to enable automatic rule-based reasoning and query in different identified application scenarios. This ontology model enables effective POE-related knowledge retrieving and sharing, and promotes its implementation in the POE domain. To validate the developed framework, a case study is carried out facilitated by the Building Use Studies (BUS) Methodology to illustrate its feasibility and effectiveness in different application scenarios. This research concludes that the proposed ontology-based POE framework has the capability to conduct a multi-objective and multi-criteria POE assessment at the building operation stage and provide a multi-criteria optimised solution

Multilevel Security Policy Implementation Using OWL Ontology

This project is an experimental implementation of Multi-Level Security (MLS) lattice model by using semantic web technologies (OWL) to create and test Mandatory Access Control (MAC) with Bell-LaPadula (BLP) properties. Semantic web (web of data) is building on top of the World Wide Web (web of documents), aiming to make data machine-readable so that to improve data processing and management. OWL is a semantic web computational logic-base language which is designed to represent complex knowledge in semantic format. With the MLS ontology, we are able to define dominance relationship between variables within the lattice model and perform different queries to verify if the subject (with security clearance) can access (read/write) to the object (with security classification). Moreover, by leveraging BLP properties, the ontology would only allow information to flow from entities with lower classification to entities with higher classification

A process model in platform independent and neutral formal representation for design engineering automation

An engineering design process as part of product development (PD) needs to satisfy ever-changing customer demands by striking a balance between time, cost and quality. In order to achieve a faster lead-time, improved quality and reduced PD costs for increased profits, automation methods have been developed with the help of virtual engineering. There are various methods of achieving Design Engineering Automation (DEA) with Computer-Aided (CAx) tools such as CAD/CAE/CAM, Product Lifecycle Management (PLM) and Knowledge Based Engineering (KBE). For example, Computer Aided Design (CAD) tools enable Geometry Automation (GA), PLM systems allow for sharing and exchange of product knowledge throughout the PD lifecycle. Traditional automation methods are specific to individual products and are hard-coded and bound by the proprietary tool format. Also, existing CAx tools and PLM systems offer bespoke islands of automation as compared to KBE. KBE as a design method incorporates complete design intent by including re-usable geometric, non-geometric product knowledge as well as engineering process knowledge for DEA including various processes such as mechanical design, analysis and manufacturing. It has been recognised, through an extensive literature review, that a research gap exists in the form of a generic and structured method of knowledge modelling, both informal and formal modelling, of mechanical design process with manufacturing knowledge (DFM/DFA) as part of model based systems engineering (MBSE) for DEA with a KBE approach. There is a lack of a structured technique for knowledge modelling, which can provide a standardised method to use platform independent and neutral formal standards for DEA with generative modelling for mechanical product design process and DFM with preserved semantics. The neutral formal representation through computer or machine understandable format provides open standard usage. This thesis provides a contribution to knowledge by addressing this gap in two-steps: • In the first step, a coherent process model, GPM-DEA is developed as part of MBSE which can be used for modelling of mechanical design with manufacturing knowledge utilising hybrid approach, based on strengths of existing modelling standards such as IDEF0, UML, SysML and addition of constructs as per author’s Metamodel. The structured process model is highly granular with complex interdependencies such as activities, object, function, rule association and includes the effect of the process model on the product at both component and geometric attributes. • In the second step, a method is provided to map the schema of the process model to equivalent platform independent and neutral formal standards using OWL/SWRL ontology for system development using Protégé tool, enabling machine interpretability with semantic clarity for DEA with generative modelling by building queries and reasoning on set of generic SWRL functions developed by the author. Model development has been performed with the aid of literature analysis and pilot use-cases. Experimental verification with test use-cases has confirmed the reasoning and querying capability on formal axioms in generating accurate results. Some of the other key strengths are that knowledgebase is generic, scalable and extensible, hence provides re-usability and wider design space exploration. The generative modelling capability allows the model to generate activities and objects based on functional requirements of the mechanical design process with DFM/DFA and rules based on logic. With the help of application programming interface, a platform specific DEA system such as a KBE tool or a CAD tool enabling GA and a web page incorporating engineering knowledge for decision support can consume relevant part of the knowledgebase

Improving data management through automatic information extraction model in ontology for road asset management

lRoads are a critical component of transportation infrastructure, and their effective maintenance is paramount in ensuring their continued functionality and safety. This research proposes a novel information management approach based on state-of-the-art deep learning models and ontologies. The approach can automatically extract, integrate, complete, and search for project knowledge buried in unstructured text documents. The approach on the one hand facilitates implementation of modern management approaches, i.e., advanced working packaging to delivery success road management projects, on the other hand improves information management practices in the construction industry

Ontop: answering SPARQL queries over relational databases

We present Ontop, an open-source Ontology-Based Data Access (OBDA) system that allows for querying relational data sources through a conceptual representation of the domain of interest, provided in terms of an ontology, to which the data sources are mapped. Key features of Ontop are its solid theoretical foundations, a virtual approach to OBDA, which avoids materializing triples and is implemented through the query rewriting technique, extensive optimizations exploiting all elements of the OBDA architecture, its compliance to all relevant W3C recommendations (including SPARQL queries, R2RML mappings, and OWL2QL and RDFS ontologies), and its support for all major relational databases

Ontology-Based Data Integration in Multi-Disciplinary Engineering Environments: A Review

Today's industrial production plants are complex mechatronic systems. In the course of the production plant lifecycle, engineers from a variety of disciplines (e.g., mechanics, electronics, automation) need to collaborate in multi-disciplinary settings that are characterized by heterogeneity in terminology, methods, and tools. This collaboration yields a variety of engineering artifacts that need to be linked and integrated, which on the technical level is reflected in the need to integrate heterogeneous data. Semantic Web technologies, in particular ontologybased data integration (OBDI), are promising to tackle this challenge that has attracted strong interest from the engineering research community. This interest has resulted in a growing body of literature that is dispersed across the Semantic Web and Automation System Engineering research communities and has not been systematically reviewed so far. We address this gap with a survey reflecting on OBDI applications in the context of Multi-Disciplinary Engineering Environment (MDEE). To this end, we analyze and compare 23 OBDI applications from both the Semantic Web and the Automation System Engineering research communities. Based on this analysis, we (i) categorize OBDI variants used in MDEE, (ii) identify key problem context characteristics, (iii) compare strengths and limitations of OBDI variants as a function of problem context, and (iv) provide recommendation guidelines for the selection of OBDI variants and technologies for OBDI in MDEE

A Semantically Enhanced Approach for Orchestration of Web Services in Factory Automation Systems

The Service-oriented Architecture (SOA) paradigm makes it possible to build systems from several independent components. Most typically, web services are chosen as the building blocks of such a system. A web service is essentially a passive software entity, which listens for request messages sent to it over the network, possibly reacts to the requests by performing some operations, and finally sends response messages to the request senders. The traditional application domain of web services belongs to the so-called IT domain. While opening new horizons in software development life-cycles, web services have been adopted in various new application domains, including the domain of factory automation (software development for factory automation). Indeed, recent research projects have experimented with controlling production system equipment through web service interfaces. When migrated from pure software to the physical realm involving industrial equipment, web services set additional demands for the application domains. For example, since the domains involve operations with physical effects, roll-back or application recovery procedures become challenging. This research work targets the orchestration of factory automation systems encapsulated as web services and presents various techniques for overcoming the difficulties. Orchestrating web services to accomplish a complicated production task can be difficult due to the transitoriness of both production equipment states and the set of available web services. Nevertheless, the selection of appropriate web services can be facilitated by augmenting each service with semantic information describing its conditions and effects. Web services augmented with such descriptions are termed semantic web services. While Web Ontology Language, OWL, is ideal for describing application domain concepts and property relationships, the OWL-S ontology, which is based on OWL, has been specifically developed for describing web services. Once the semantic service descriptions have been analyzed to find the appropriate web services, the selected services can be invoked using their syntactic WSDL descriptions. In addition to automated web service selection, semantic descriptions allow the composition of web services to achieve production tasks. Service composition involves first analyzing the descriptions to determine the appropriate service invocation process for achieving the desired goal and then executing the process. This dissertation presents an approach in which the production equipment and their states are represented using an ontology, and the model is dynamically used in decision-making. In particular, the devices in the considered production systems provide web service interfaces through which they can be controlled, while semantic web service descriptions formulated in OWL-S make it possible to determine the conditions and effects of invoking the web services. The approach presented in this research work additionally involves a set of specialized web services that co-operate to achieve production goals using the domain web services. One of the services maintains a semantic model of the current system state, while another uses the model to compose the domain web services so that they jointly achieve the desired goals. The semantic model of the system is automatically updated based on event notifications sent by the domain services. Software agents controlling production devices must maintain an up-to-date view of the physical world state in order to efficiently reason and plan their actions. Especially in a factory automation system, the world state undergoes rapid evolution, and the world view must remain synchronized with the changes. This research discusses two approaches to updating the world view based on event notifications sent by web services representing production devices in a manufacturing system. One of the approaches is based on separately specified update rules, and one automatically uses semantic web service descriptions formulated in OWL-S. While all of the examples presented in this research work specifically focus on the factory automation domain, the presented approaches are applicable to all domains involving semantic web services. Semantic Web Service descriptions facilitate the automated discovery and composition of web services. Particularly in the production system domain, the service condition and effect descriptions are essential in selecting the appropriate service or service composition for a given task. OWL-S is one of the most popular semantic web service description languages, and due to its XML syntax, OWL-S can be effortlessly incorporated into service WSDL descriptions. However, developing OWL-S documents for each service instance is laborious. This dissertation presents an approach to automatically generating executable OWL-S descriptions from semantically annotated service WSDL files. Computing clouds facilitate rapid and effortless resource allocation. Cloud consumers can generally be ignorant of the physical computing resources used or their geographical location, as the resources are abstracted into a commodity that can be dynamically leased from the cloud provider. In particular, Infrastructure-as-a-Service clouds allow clients to dynamically lease virtual machines that behave similarly to physical servers. However, executing an application by directly using computing cloud resources is complicated and typically involves similar steps as installing and executing an application on a physical machine. Moreover, starting numerous application instances on a single virtual machine may result in poor performance. Thus, this dissertation considers the development of a web service that facilitates the use of cloud resources by abstracting them. When the web service is used, an application can be effortlessly started in a computing cloud by invoking simple web service operations. Furthermore, when multiple applications are started, the workload can be automatically distributed between several virtual machines, resulting in higher performance. To conclude, the results presented in this research work demonstrate that semantic web service descriptions can indeed facilitate automatic web service composition and invocation. However, the effort of developing semantic web service descriptions can partly undermine the benefits achieved through their application. Therefore, new tools and methods should be developed to minimize the effort of developing such descriptions