Search (40 results, page 1 of 2)

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Content

Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.

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Abstract

A method to develop a prototype domain ontology has been described. The domain selected for the study is Accelerator Driven Systems. This is a multidisciplinary and interdisciplinary subject comprising Nuclear Physics, Nuclear and Reactor Engineering, Reactor Fuels and Radioactive Waste Management. Since Accelerator Driven Systems is a vast topic, select areas in it were singled out for the study. Both qualitative and quantitative methods such as Content analysis, Facet analysis and Clustering were used, to develop the web-based model.

Date

22. 7.2010 19:41:16

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Abstract

Ontology engineering encompasses the method, tools and techniques used to develop ontologies. Without requiring ontologies, linked data is driving a paradigm shift, bringing benefits and drawbacks to the publishing world. Ontologies may be heavyweight, supporting deep understanding of a domain, or lightweight, suited to simple classification of concepts and more adaptable for linked data. They also vary in domain specificity, usability and reusabilty. Hybrid vocabularies drawing elements from diverse sources often suffer from internally incompatible semantics. To serve linked data purposes, ontology engineering teams require a range of skills in philosophy, computer science, web development, librarianship and domain expertise.

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Abstract

Ontologies have been a hot research topic for the recent decade and have been used for many applications such as information integration, semantic search, knowledge management, etc. Manual engineering of ontologies is a costly process and automatic ontology engineering lacks in precision. Folksonomies have recently emerged as another hot research topic and several research efforts have been made to extract lightweight ontologies automatically from folksonomy data. Due to the high cost of manual ontology engineering and the lack of precision in automatic ontology engineering it is important that we are able to evaluate the structure of the ontology. Detection of problems with the suggested ontology at an early stage can, especially for manually engineered ontologies, be cost saving. In this paper we present an approach to evaluate the quality of hierarchical relations in ontologies and folksonomy based structures. The approach is based on constructing shallow semantic representations of the ontology concepts and folksonomy tags. We specify four hypotheses regarding the semantic representations and different quality aspects of the hierarchical relations and perform an evaluation on two different data sets. The results of the evaluation confirm our hypotheses.

Source

Data and knowledge engineering. 74(2012) April, S.13-25

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Abstract

The development of complex projects in the aerospace industry is based on the collaboration of geographically distributed teams and companies. In this context, the need of sharing different types of data and information is a key factor to assure the successful execution of the projects. In the case of European projects, the ECSS standards provide a normative framework that specifies, among other requirements, the different document types, information items and artifacts that need to be generated. The specification of the characteristics of these information items are usually incorporated as annex to the different ECSS standards, and they provide the intended purpose, scope, and structure of the documents and information items. In these standards, documents or deliverables should not be considered as independent items, but as the results of packaging different information artifacts for their delivery between the involved parties. Successful information integration and knowledge exchange cannot be based exclusively on the conceptual definition of information types. It also requires the definition of methods and techniques for serializing and exchanging these documents and artifacts. This area is not covered by ECSS standards, and the definition of these data schemas would improve the opportunity for improving collaboration processes among companies. This paper describes the development of an OWL-based ontology to manage the different artifacts and information items requested in the European Space Agency (ESA) ECSS standards for SW development. The ECSS set of standards is the main reference in aerospace projects in Europe, and in addition to engineering and managerial requirements they provide a set of DRD (Document Requirements Documents) with the structure of the different documents and records necessary to manage projects and describe intermediate information products and final deliverables. Information integration is a must-have in aerospace projects, where different players need to collaborate and share data during the life cycle of the products about requirements, design elements, problems, etc. The proposed ontology provides the basis for building advanced information systems where the information coming from different companies and institutions can be integrated into a coherent set of related data. It also provides a conceptual framework to enable the development of interfaces and gateways between the different tools and information systems used by the different players in aerospace projects.

Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

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Source

International journal of software engineering and knowledge engineering. 24(2014) no.8, S.1159-1175

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Abstract

Knowledge explosion in various fields during recent years has resulted in the creation of vast amounts of on-line scientific literature. Food Science &Technology (FST) is also an important subject domain where rapid developments are taking place due to diverse research and development activities. As a result, information storage and retrieval has become very complex and current information retrieval systems (IRs) are being challenged in terms of both adequate precision and response time. To overcome these limitations as well as to provide naturallanguage based effective retrieval, a suitable knowledge engineering framework needs to be applied to represent, share and discover information. Semantic web technologies provide mechanisms for creating knowledge bases, ontologies and rules for handling data that promise to improve the quality of information retrieval. Ontologies are the backbone of such knowledge systems. This paper presents a framework for semantic representation of a large repository of content in the domain of FST.

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Source

Reuse and Adaptation of Ontologies and Terminologies Workshop at 17th International Conference on Knowledge Engineering and Knowledge Management (EKAW), October 11-15, 2010, Lisbon: Proceedings

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Abstract

This paper presents an overview of automatic methods for building domain knowledge structures (domain models) from text collections. Applications of domain models have a long history within knowledge engineering and artificial intelligence. In the last couple of decades they have surfaced noticeably as a useful tool within natural language processing, information retrieval and semantic web technology. Inspired by the ubiquitous propagation of domain model structures that are emerging in several research disciplines, we give an overview of the current research landscape and some techniques and approaches. We will also discuss trade-offs between different approaches and point to some recent trends.

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Source

Knowledge discovery, knowledge engineering and knowledge management: 7th International Joint Conference, IC3K 2015, Lisbon, Portugal, November 12-14, 2015, Revised Selected Papers. Eds.: A. Fred et al

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Content

Thesis submitted to the Graduate School of Natural and Applied Sciences of Middle East Technical University in partial fulfilment of the requirements for the degree of Master of science in Computer Engineering (XII, 57 S.)

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Abstract

Product family design is one of the prevailing approaches in realizing mass customization. With the increasing number of product offerings targeted at different market segments, the issue of information management in product family design, that is related to an efficient and effective storage, sharing and timely retrieval of design information, has become more complicated and challenging. Product family modelling schema reported in the literature generally stress the component aspects of a product family and its analysis, with a limited capability to model complex inter-relations between physical components and other required information in different semantic orientations, such as manufacturing, material and marketing wise. To tackle this problem, ontology-based representation has been identified as a promising solution to redesign product platforms especially in a semantically rich environment. However, ontology development in design engineering demands a great deal of time commitment and human effort to process complex information. When a large variety of products are available, particularly in the consumer market, a more efficient method for building a product family ontology with the incorporation of multi-faceted semantic information is therefore highly desirable. In this study, we propose a methodology for building a semantically annotated multi-faceted ontology for product family modelling that is able to automatically suggest semantically-related annotations based on the design and manufacturing repository. The six steps of building such ontology: formation of product family taxonomy; extraction of entities; faceted unit generation and concept identification; facet modelling and semantic annotation; formation of a semantically annotated multi-faceted product family ontology (MFPFO); and ontology validation and evaluation are discussed in detail. Using a family of laptop computers as an illustrative example, we demonstrate how our methodology can be deployed step by step to create a semantically annotated MFPFO. Finally, we briefly discuss future research issues as well as interesting applications that can be further pursued based on the MFPFO developed.

Source

Advanced engineering informatics. 25(2011) no.2, S.147-161

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Source

International Journal of Engineering Science and Technology. 2(2010), no.10, S.5679-5688

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Abstract

Research in ontology has, in recent years, become widespread in the field of information systems, in various areas of sciences, in business, in economy, and in industry. The importance of ontologies is increasingly recognized in fields diverse as in e-commerce, semantic web, enterprise, information integration, information science, qualitative modeling of physical systems, natural language processing, knowledge engineering, and databases. Ontologies provide formal specifications and harmonized definitions of concepts used to represent knowledge of specific domains. An ontology supplies a unifying framework for communication, it establishes a basis for knowledge organization and knowledge representation and contributes to theory formation and modeling of a specific domain. In the current paper, we present and discuss principles of organization and representation of knowledge that grew out of the use of formal ontology. The core of the discussed ontological framework is a top-level ontology, called GFO (General Formal Ontology), which is being developed at the University of Leipzig. These principles make use of the onto-axiomatic method, of graduated conceptualizations, of levels of reality, and of top-level-supported methods for ontology-development. We explore the interrelations between formal ontology and knowledge organization, and argue for a close interaction between both fields

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Source

Knowledge discovery, knowledge engineering and knowledge management: 7th International Joint Conference, IC3K 2015, Lisbon, Portugal, November 12-14, 2015, Revised Selected Papers. Eds.: A. Fred et al

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Abstract

Knowledge representation, which is today used in database applications, artificial intelligence (AI), software engineering and many other disciplines of computer science has deep roots in logic and philosophy. In the beginning, there was Aristotle (384 bc-322 bc) who developed logic as a precise method for reasoning about knowledge. Syllogisms were introduced as formal patterns for representing special figures of logical deductions. According to Aristotle, the subject of ontology is the study of categories of things that exist or may exist in some domain. In modern times, Descartes considered the human brain as a store of knowledge representation. Recognition was made possible by an isomorphic correspondence between internal geometrical representations (ideae) and external situations and events. Leibniz was deeply influenced by these traditions. In his mathesis universalis, he required a universal formal language (lingua universalis) to represent human thinking by calculation procedures and to implement them by means of mechanical calculating machines. An ars iudicandi should allow every problem to be decided by an algorithm after representation in numeric symbols. An ars iveniendi should enable users to seek and enumerate desired data and solutions of problems. In the age of mechanics, knowledge representation was reduced to mechanical calculation procedures. In the twentieth century, computational cognitivism arose in the wake of Turing's theory of computability. In its functionalism, the hardware of a computer is related to the wetware of the human brain. The mind is understood as the software of a computer.

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Abstract

Purpose - This paper sets out to discuss the use of information extraction (IE), a natural language-processing (NLP) technique to assist "rich" semantic indexing of diverse archaeological text resources. The focus of the research is to direct a semantic-aware "rich" indexing of diverse natural language resources with properties capable of satisfying information retrieval from online publications and datasets associated with the Semantic Technologies for Archaeological Resources (STAR) project. Design/methodology/approach - The paper proposes use of the English Heritage extension (CRM-EH) of the standard core ontology in cultural heritage, CIDOC CRM, and exploitation of domain thesauri resources for driving and enhancing an Ontology-Oriented Information Extraction process. The process of semantic indexing is based on a rule-based Information Extraction technique, which is facilitated by the General Architecture of Text Engineering (GATE) toolkit and expressed by Java Annotation Pattern Engine (JAPE) rules. Findings - Initial results suggest that the combination of information extraction with knowledge resources and standard conceptual models is capable of supporting semantic-aware term indexing. Additional efforts are required for further exploitation of the technique and adoption of formal evaluation methods for assessing the performance of the method in measurable terms. Originality/value - The value of the paper lies in the semantic indexing of 535 unpublished online documents often referred to as "Grey Literature", from the Archaeological Data Service OASIS corpus (Online AccesS to the Index of archaeological investigationS), with respect to the CRM ontological concepts E49.Time Appellation and P19.Physical Object.

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Abstract

Mathematics is a ubiquitous foundation of science, technology, and engineering. Specific areas, such as numeric and symbolic computation or logics, enjoy considerable software support. Working mathematicians have recently started to adopt Web 2.0 environment, such as blogs and wikis, but these systems lack machine support for knowledge organization and reuse, and they are disconnected from tools such as computer algebra systems or interactive proof assistants.We argue that such scenarios will benefit from Semantic Web technology. Conversely, mathematics is still underrepresented on the Web of [Linked] Data. There are mathematics-related Linked Data, for example statistical government data or scientific publication databases, but their mathematical semantics has not yet been modeled. We argue that the services for the Web of Data will benefit from a deeper representation of mathematical knowledge. Mathematical knowledge comprises logical and functional structures - formulæ, statements, and theories -, a mixture of rigorous natural language and symbolic notation in documents, application-specific metadata, and discussions about conceptualizations, formalizations, proofs, and (counter-)examples. Our review of approaches to representing these structures covers ontologies for mathematical problems, proofs, interlinked scientific publications, scientific discourse, as well as mathematical metadata vocabularies and domain knowledge from pure and applied mathematics. Many fields of mathematics have not yet been implemented as proper Semantic Web ontologies; however, we show that MathML and OpenMath, the standard XML-based exchange languages for mathematical knowledge, can be fully integrated with RDF representations in order to contribute existing mathematical knowledge to theWeb of Data. We conclude with a roadmap for getting the mathematical Web of Data started: what datasets to publish, how to interlink them, and how to take advantage of these new connections.