973 research outputs found
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
Application of protege and SPARQL in the field of project knowledge management
Protg is a set of open-source ontology design software developed in Stanford Medical Informatics. SPARQL (Protocol and RDF Query Language) is recommended by W3C, to represent the RDF (Resource Description Framework) graph a set of triples that consist of a subject, a predicate and an object as the basic expression of data stored in OWLbased knowledge base.In this paper, we propose an ontology-based project knowledge management methodology, by means of Protg and SPARQL, to solve the issues in project management activities. By introducing a set of new ontology notations, we present the conceptual model of our ontology to realize the function of knowledge management in project organizations. Following that, we realize the prototype in Protg and validate it by means of SPARQL. Finally we make comments on our project and plan our future work
An ontology-based approach to Automatic Generation of GUI for Data Entry
This thesis reports an ontology-based approach to automatic generation of highly tailored GUI components that can make customized data requests for the end users. Using this GUI generator, without knowing any programming skill a domain expert can browse the data schema through the ontology file of his/her own field, choose attribute fields according to business\u27s needs, and make a highly customized GUI for end users\u27 data requests input. The interface for the domain expert is a tree view structure that shows not only the domain taxonomy categories but also the relationships between classes. By clicking the checkbox associated with each class, the expert indicates his/her choice of the needed information. These choices are stored in a metadata document in XML. From the viewpoint of programmers, the metadata contains no ambiguity; every class in an ontology is unique. The utilizations of the metadata can be various; I have carried out the process of GUI generation. Since every class and every attribute in the class has been formally specified in the ontology, generating GUI is automatic. This approach has been applied to a use case scenario in meteorological and oceanographic (METOC) area. The resulting features of this prototype have been reported in this thesis
Development of an Ontology of Tourist Attractions for Recommending Points of Interest in a Group Recommender System for Tourism
In recent years, the tourism industry has witnessed substantial growth, thanks to the pro liferation of digital technology and online platforms. Tourists now have greater access to
information and the ability to make informed travel decisions. However, the abundance
of available information often leaves tourists overwhelmed when selecting points of inter est (POI) that align with their preferences. Recommender Systems (RS) have emerged as
a solution, personalising recommendations based on tourist behaviour, social networks, and
contextual factors. To enhance RS efficacy, researchers have begun exploring the integration
of psychological factors, such as personality traits. Yet, to meet the demands of modern
tourists, a robust knowledge base, such as a tourist attractions ontology, is essential for
seamless and rapid matching of tourist characteristics and preferences with available POI.
With that in mind, this project aims to enhance a Group Recommender System (GRS)
prototype, GrouPlanner, by creating a robust tourist attractions ontology. This ontology
will facilitate rapid and accurate matching of points of interest with tourists’ character istics, including personality, preferences, and demographic data, ultimately improving POI
recommendations.
First, there needs to be an understanding of the personality of tourists and how it influences
their choices when it comes to picking the best point of interest based on their personality.
With that knowledge acquired, it is time to choose a way to represent this knowledge in the
form of an ontology.
In this project, the Protégé ontology editor was used to design the ontology and the rela tionships between the tourists’ personality and the points of interest. After designing the
ontology, it had to be converted to a database so the Grouplanner system could access it.
So, to do that, a solution was designed to integrate the designed ontology in a triple store
data base, in this case, Apache Fuseki.
With the database implemented, several tests were made to verify if the database would
give the recommended points of interests based on the tourists’ preferences. This tests were
later analysed.Nos anos mais recentes, a indústria do turismo presenciou um crescimento substancial dev ido à tecnologia digital e plataformas online. Cada vez mais, os turistas têm acesso a uma
abundância de informação que influencia a habilidade de tomar decisões sobre viajar. No
entanto, esta informação pode complicar a seleção dos pontos de interesse que alinhem com
as preferências dos turistas. Para combater isso, sistemas de recomendação (SR) emergi ram como uma solução, personalizando as recomendações com base no comportamento do
turista, redes socias e outros fatores. Para aumentar a eficácia destes sistemas, os investi gadores começaram a explorar a possibilidade de integração com fatores psicológicos, como
traços de personalidade. Apesar disso, para cumprir as exigências dos turistas modernos,
uma base de conhecimento robusta, como uma ontologia de atrações turísticas, é essencial
para, de forma eficaz e eficiente, corresponder as características dos turistas com os pontos
de interesse disponíveis.
Com isso em mente, este projeto tem como objetivo melhorar um protótipo de um sistema
de recomendação (GrouPlanner), criando uma ontologia robusta de atrações turísticas. Essa
ontologia facilitará a correspondência rápida e precisa de pontos de interesse com as car acterísticas dos turistas, incluindo a sua personalidade e as suas preferências, melhorando
assim as recomendações de pontos de interesse.
Em primeiro lugar, é necessário compreender a personalidade dos turistas e como ela influ encia as suas escolhas ao selecionar o melhor ponto de interesse com base na sua person alidade. Com esse ponto adquirido, é necessário escolher uma maneira de representar esse
conhecimento na forma de uma ontologia.
Neste projeto, o editor de ontologias Protégé foi utilizado para projetar a ontologia e as
relações entre a personalidade dos turistas e os pontos de interesse. Após a construção da
ontologia, foi necessário convertê-la numa base de dados para que o sistema Grouplanner
pudesse ter acesso. Para isso, foi desenhada uma solução para integrar a ontologia projetada
numa base de dados "triple store", neste caso, o Apache Fuseki.
Com a base de dados implementada, foram realizados vários testes para verificar se esta
forneceria os pontos de interesse recomendados com base nas preferências dos turistas.
Esses testes foram depois analisados
Experiences with creating a Precision Dairy Farming Ontology (DFO) and a Knowledge Graph for the Data Integration Platform in agriOpenLink
One of the central problems in creating information management solutions for precision dairy farming is integration and interpretation of heterogeneous data coming from different equipment and data sources. Establishing a unifying data model is recognized as a cornerstone to such solutions. Here, the challenge lies both in agreeing on a common information context, and in selecting appropriate model representations, model query and update techniques, which guarantee model extensibility. While already existing ISOagriNET Data Dictionaries capture broad variety of the livestock and dairy farming concepts, the existing model representation technics cannot support efficient model extension. In this paper, we present our experience with using the representation and query standards and tools of the Semantic Web, endorsed by the World Wide Web Consortium (W3C), in particular the Resource Description Framework (RDF), the Web Ontology Language (OWL), and the SPARQL query standard, to encode and manipulate the dairy farming domain knowledge in a form of the Dairy Farming Ontology (DFO). Within the research project agriOpenLink the DFO has been created in two phases. The first phase focused on using the Semantic Web tools to facilitate easy encoding and manipulation of the dairy farming domain knowledge and the platform operational data models. The second phase focused on translating ISOagriNET Data Dictionaries into their semantic representations. Resulting DFO is maintained in a semantic repository, and it presents a knowledge graph and as integration backbone for the agriOpenLink decision support platform.</jats:p
Semantic Integration of Coastal Buoys Data using SPARQL
Currently, the data provided by the heterogeneous buoy sensors/networks (e.g. National Data Buoy center (NDBC), Gulf Of Maine Ocean Observing System (GoMoos) etc. is not amenable to the development of integrated systems due to conflicts in the data representation at syntactic and structural levels. With the rapid increase in the amount of information, the integration of heterogeneous resources is an important issue and requires integrative technologies such as semantic web. In distributed data dissemination system, normally querying on single database will not provide relevant information and requires querying across interrelated data sources to retrieve holistic information. In this thesis we develop system for integrating two different Resource Description Framework (RDF) data sources through intelligent querying using Simple Protocol and RDF Query Language (SPARQL). We use Semantic Web application framework from AllegroGraph that provides functionality for developing triple store for the ontological representations, forming federated stores and querying it through SPARQL
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