1,756 research outputs found
Some Issues on Ontology Integration
The word integration has been used with different
meanings in the ontology field. This article
aims at clarifying the meaning of the word âintegrationâ
and presenting some of the relevant work
done in integration. We identify three meanings of
ontology âintegrationâ: when building a new ontology
reusing (by assembling, extending, specializing
or adapting) other ontologies already available;
when building an ontology by merging several
ontologies into a single one that unifies all of
them; when building an application using one or
more ontologies. We discuss the different meanings
of âintegrationâ, identify the main characteristics
of the three different processes and proposethree words to distinguish among those meanings:integration, merge and use
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Applying semantic web services to enterprise web
Enterprise Web provides a convenient, extendable, integrated platform for information sharing and knowledge management. However, it still has many drawbacks due to complexity and increasing information glut, as well as the heterogeneity of the information processed. Research in the field of Semantic Web Services has shown the possibility of adding higher level of semantic functionality onto the top of current Enterprise Web, enhancing usability and usefulness of resource, enabling decision support and automation. This paper aims to explore the use of Semantic Web Services in Enterprise Web and discuss the Semantic Web Services (SWS) approach for designing Enterprise Web applications. A Semantic Web Service oriented model is presented, in which resources and services are described by ontology, and processed through Semantic Web Service, allowing integrated administration, interoperability and automated reasoning
Social and Semantic Web Technologies for the Text-To-Knowledge Translation Process in Biomedicine
Currently, biomedical research critically depends on knowledge availability for flexible
re-analysis and integrative post-processing. The voluminous biological data already stored in
databases, put together with the abundant molecular data resulting from the rapid adoption of
high-throughput techniques, have shown the potential to generate new biomedical discovery
through integration with knowledge from the scientific literature.
Reliable information extraction applications have been a long-sought goal of the biomedical
text mining community. Both named entity recognition and conceptual analysis are needed in
order to map the objects and concepts represented by natural language texts into a rigorous
encoding, with direct links to online resources that explicitly expose those concepts semantics
(see Figure 1).P08-TIC-4299 of J. ASevilla and
TIN2009-13489 of DGICT, Madri
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A modular product structure based methodology for seamless information flow in PLM system implementation
Product development process deals with large amount of information generated from market survey, concept design, manufacture, test, limited production, production, service, and obsoleting. The information should be stored systematically so that it is easily traceable and reusable for future product development. This paper presents a methodology for seamless product information flow between the three main enterprise information systems such as Computer Aided Design and Manufacturing (CAD/CAM), Product Data/Lifecycle Management (PDM/PLM) and Enterprise Resource Planning (ERP) used in the process of innovative product development while implementing PLM. PLM implementation deals with various existing product data and information generated over years both from CAD and ERP systems. Data integration is very challenging in multi-national engineering companies and has important impact on future decisions while creating new processes. The aim is to define a modular product structure that can be used to connect the product information throughout the life cycle that can be reused effectively and efficiently for future similar products
tiphys an open networked platform for higher education on industry 4 0
Abstract Objective of Tiphys project is building an Open Networked Platform for the learning of Industry 4.0 themes. The project will create a Virtual Reality (VR) platform, where users will be able to design and create a VR based environment for training and simulating industrial processes but they will be able to study and select among a set of models in order to standardize the learning and physical processes as a virtual representation of the real industrial world and the required interactions so that to acquire learning and training capabilities. The models will be structured in a modular approach to promote the integration in the existing mechanisms as well as for future necessary adaptations. The students will be able to co-create their learning track and the learning contents by collaborative working in a dynamic environment. The paper presents the development and validation of the learning model, built on CONALI learning ontology. The concepts of the ontology will be detailed and the platform functions will be demonstrated on selected use cases
Ontologies for Neuroscience: What are they and What are they Good for?
Current information technology practices in neuroscience make it difficult to understand the organization of the brain across spatial scales. Subcellular junctional connectivity, cytoarchitectural local connectivity, and long-range topographical connectivity are just a few of the relevant data domains that must be synthesized in order to make sense of the brain. However, due to the heterogeneity of the data produced within these domains, the landscape of multiscale neuroscience data is fragmented. A standard framework for neuroscience data is needed to bridge existing digital data resources and to help in the conceptual unification of the multiple disciplines of neuroscience. Using our efforts in building ontologies for neuroscience as an example, we examine the benefits and limits of ontologies as a solution for this data integration problem. We provide several examples of their application to problems of image annotation, content-based retrieval of structural data, and integration of data across scales and researchers
A Hierarchical Core Reference Ontology for New Technology Insertion Design in Long Life Cycle, Complex Mission Critical Systems
Organizations, including government, commercial and others, face numerous challenges in maintaining and upgrading long life-cycle, complex, mission critical systems. Maintaining and upgrading these systems requires the insertion and integration of new technology to avoid obsolescence of hardware software, and human skills, to improve performance, to maintain and improve security, and to extend useful life. This is particularly true of information technology (IT) intensive systems. The lack of a coherent body of knowledge to organize new technology insertion theory and practice is a significant contributor to this difficulty. This research organized the existing design, technology road mapping, obsolescence, and sustainability literature into an ontology of theory and application as the foundation for a technology design and technology insertion design hierarchical core reference ontology and laid the foundation for body of knowledge that better integrates the new technology insertion problem into the technology design architecture
From conceptual design to process design optimization: a review on flowsheet synthesis
International audienceThis paper presents the authorsâ perspectives on some of the open questions and opportunities in Process Systems Engineering (PSE) focusing on process synthesis. A general overview of process synthesis is given, and the difference between Conceptual Design (CD) and Process Design (PD) is presented using an original ternary diagram. Then, a bibliometric analysis is performed to place major research team activities in the latter. An analysis of ongoing work is conducted and some perspectives are provided based on the analysis. This analysis includes symbolic knowledge representation concepts and inference techniques, i.e., ontology, that is believed to become useful in the future. Future research challenges that process synthesis will have to face, such as biomass transformation, shale production, response to spaceflight demand, modular plant design, and intermittent production of energy, are also discussed
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