Ontology-based data semantic management and application in IoT- and cloud-enabled smart homes

The application of emerging technologies of Internet of Things (IoT) and cloud computing have increasing the popularity of smart homes, along with which, large volumes of heterogeneous data have been generating by home entities. The representation, management and application of the continuously increasing amounts of heterogeneous data in the smart home data space have been critical challenges to the further development of smart home industry. To this end, a scheme for ontology-based data semantic management and application is proposed in this paper. Based on a smart home system model abstracted from the perspective of implementing users’ household operations, a general domain ontology model is designed by defining the correlative concepts, and a logical data semantic fusion model is designed accordingly. Subsequently, to achieve high-efficiency ontology data query and update in the implementation of the data semantic fusion model, a relational-database-based ontology data decomposition storage method is developed by thoroughly investigating existing storage modes, and the performance is demonstrated using a group of elaborated ontology data query and update operations. Comprehensively utilizing the stated achievements, ontology-based semantic reasoning with a specially designed semantic matching rule is studied as well in this work in an attempt to provide accurate and personalized home services, and the efficiency is demonstrated through experiments conducted on the developed testing system for user behavior reasoning

Ontology-Based Data Access and Integration

An ontology-based data integration (OBDI) system is an information management system consisting of three components: an ontology, a set of data sources, and the mapping between the two. The ontology is a conceptual, formal description of the domain of interest to a given organization (or a community of users), expressed in terms of relevant concepts, attributes of concepts, relationships between concepts, and logical assertions characterizing the domain knowledge. The data sources are the repositories accessible by the organization where data concerning the domain are stored. In the general case, such repositories are numerous, heterogeneous, each one managed and maintained independently from the others. The mapping is a precise specification of the correspondence between the data contained in the data sources and the elements of the ontology. The main purpose of an OBDI system is to allow information consumers to query the data using the elements in the ontology as predicates. In the special case where the organization manages a single data source, the term ontology-based data access (ODBA) system is used

Integrating Distributed Sources of Information for Construction Cost Estimating using Semantic Web and Semantic Web Service technologies

A construction project requires collaboration of several organizations such as owner, designer, contractor, and material supplier organizations. These organizations need to exchange information to enhance their teamwork. Understanding the information received from other organizations requires specialized human resources. Construction cost estimating is one of the processes that requires information from several sources including a building information model (BIM) created by designers, estimating assembly and work item information maintained by contractors, and construction material cost data provided by material suppliers. Currently, it is not easy to integrate the information necessary for cost estimating over the Internet. This paper discusses a new approach to construction cost estimating that uses Semantic Web technology. Semantic Web technology provides an infrastructure and a data modeling format that enables accessing, combining, and sharing information over the Internet in a machine processable format. The estimating approach presented in this paper relies on BIM, estimating knowledge, and construction material cost data expressed in a web ontology language. The approach presented in this paper makes the various sources of estimating data accessible as Simple Protocol and Resource Description Framework Query Language (SPARQL) endpoints or Semantic Web Services. We present an estimating application that integrates distributed information provided by project designers, contractors, and material suppliers for preparing cost estimates. The purpose of this paper is not to fully automate the estimating process but to streamline it by reducing human involvement in repetitive cost estimating activities

SNOMED CT standard ontology based on the ontology for general medical science

Background: Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT, hereafter abbreviated SCT) is acomprehensive medical terminology used for standardizing the storage, retrieval, and exchange of electronic healthdata. Some efforts have been made to capture the contents of SCT as Web Ontology Language (OWL), but theseefforts have been hampered by the size and complexity of SCT. Method: Our proposal here is to develop an upper-level ontology and to use it as the basis for defining the termsin SCT in a way that will support quality assurance of SCT, for example, by allowing consistency checks ofdefinitions and the identification and elimination of redundancies in the SCT vocabulary. Our proposed upper-levelSCT ontology (SCTO) is based on the Ontology for General Medical Science (OGMS). Results: The SCTO is implemented in OWL 2, to support automatic inference and consistency checking. Theapproach will allow integration of SCT data with data annotated using Open Biomedical Ontologies (OBO) Foundryontologies, since the use of OGMS will ensure consistency with the Basic Formal Ontology, which is the top-levelontology of the OBO Foundry. Currently, the SCTO contains 304 classes, 28 properties, 2400 axioms, and 1555annotations. It is publicly available through the bioportal athttp://bioportal.bioontology.org/ontologies/SCTO/. Conclusion: The resulting ontology can enhance the semantics of clinical decision support systems and semanticinteroperability among distributed electronic health records. In addition, the populated ontology can be used forthe automation of mobile health applications

Querying a regulatory model for compliant building design audit

The ingredients for an effective automated audit of a building design include a BIM model containing the design information, an electronic regulatory knowledge model, and a practical method of processing these computerised representations. There have been numerous approaches to computer-aided compliance audit in the AEC/FM domain over the last four decades, but none has yet evolved into a practical solution. One reason is that they have all been isolated attempts that lack any form of standardisation. The current research project therefore focuses on using an open standard regulatory knowledge and BIM representations in conjunction with open standard executable compliant design workflows to automate the compliance audit process. This paper provides an overview of different approaches to access information from a regulatory model representation. The paper then describes the use of a purpose-built high-level domain specific query language to extract regulatory information as part of the effort to automate manual design procedures for compliance audit