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An argumentation ontology for DIstributed, loosely-controlled and evolving engineering processes of oNTologies (DILIGENT)

TLDR
This paper presents an ontology which formalizes the main concepts which are used in an DILIGENT ontology engineering discussion and thus enables tracking arguments and allows for inconsistency detection, and enables the integration of manual, semi-automatic and automatic ontology creation approaches.
Abstract
A prerequisite to the success of the Semantic Web are shared ontologies which enable the seamless exchange of information between different parties. Engineering a shared ontology is a social process. Since its participants have slightly different views on the world, a harmonization effort requires discussing the resulting ontology. During the discussion, participants exchange arguments which may support or object to certain ontology engineering decisions. Experience from software engineering shows that tracking exchanged arguments can help users at a later stage to better understand the assumptions underlying the design decisions. Furthermore, as the constructed ontology becomes larger, ontology engineers might argue in a contradictory way without knowing so. In this paper we present an ontology which formalizes the main concepts which are used in an DILIGENT ontology engineering discussion and thus enables tracking arguments and allows for inconsistency detection. We provide an example which is drawn from experiments in an ontology engineering process to construct an ontology for knowledge management in our institute. Having constructed the ontology we also show how automated ontology learning algorithms could be taken as participants in the OE discussion. Hence, we enable the integration of manual, semi-automatic and automatic ontology creation approaches.

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Citations
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The two cultures: Mashing up Web 2.0 and the Semantic Web

TL;DR: This paper outlines a semantic weblogs scenario that illustrates the potential for combining Web 2.0 and Semantic Web technologies, while highlighting the unresolved issues that impede its realization.

Ontologies and the Semantic Web

TL;DR: This special issue of IC focuses on the use of the Web by computer systems and agents and argues that by supporting the notion of “getting work done,” the Semantic Web will become more useful, valuable, and pragmatic.
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A review of argumentation for the Social Semantic Web

TL;DR: This article provides a comprehensive and comparative overview of approaches to modeling argumentation for the Social Semantic Web from theoretical foundational models to Social Web tools for argumentation, following the path to a global World Wide Argument Web.
Journal ArticleDOI

The DILIGENT knowledge processes

TL;DR: The ontology engineering methodology DILIGENT is presented, a methodology focussing on the evolution of ontologies instead of the initial design, thus recognizing that knowledge is a tangible and moving target.
References
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Journal ArticleDOI

Toward principles for the design of ontologies used for knowledge sharing

TL;DR: The role of ontology in supporting knowledge sharing activities is described, and a set of criteria to guide the development of ontologies for these purposes are presented, and it is shown how these criteria are applied in case studies from the design ofOntologies for engineering mathematics and bibliographic data.
Book

Ontology Learning for the Semantic Web

TL;DR: The authors present an ontology learning framework that extends typical ontology engineering environments by using semiautomatic ontology construction tools and encompasses ontology import, extraction, pruning, refinement and evaluation.
BookDOI

The Semantic Web: Research and Applications

TL;DR: DODDLE-R, a support environment for user-centered ontology development, consists of two main parts: pre-processing part and quality improvement part, which generates a prototype ontology semi-automatically and supports the refinement of it interactively.

Rhetorical structure theory: a theory of text organization

TL;DR: This paper establishes a new definitional foundation for RST, Definitions are made more systematic and explicit, they introduce a new functional element, and incidentally reflect more experience in text analysis.