Ontology Learning for the Semantic Web

Maedche, Alexander; Staab, Steffen

doi:10.1007/978-1-4615-0925-7

Cited by 838 publications

(531 citation statements)

References 16 publications

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“…In [17] only the concept hierarchy (S C ) was hallmarked from the generic function ρ. We have also made evident the partonomic inclusion (P C ) since it plays an important role in our main source ontologies.…”

Section: Discussionmentioning

confidence: 99%

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A Cell-Cycle Knowledge Integration Framework

Antezana

Tsiporkova

Mironov

et al. 2006

Lecture Notes in Computer Science

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Abstract. The goal of the EU FP6 project DIAMONDS 1 is to build a computational platform for studying the cell-cycle regulation process in several different (model) organisms (S. cerevisiae, S. pombe, A. thaliana and human). This platform will enable wet-lab biologists to use a systems biology approach encompassing data integration, modeling and simulation, thereby supporting analysis and interpretation of biochemical pathways involved in the cell cycle. To facilitate the computational handling of cell-cycle specific knowledge a detailed cell-cycle ontology is essential. The currently existing cell-cycle branch of the Gene Ontology (GO) provides only a static view and it is not rich enough to support in-depth cell-cycle studies.In this work, an enhanced Cell-Cycle Ontology (CCO) is proposed as an extension to existing GO. Besides the classical add-ons given by an ontology (data repository, knowledge sharing, validation, annotation, and so on), CCO is intended to further evolve into a knowledge-based system that provides reasoning services oriented to hypotheses evaluation in the context of cell-cycle studies. A data integration pipeline prototype, covering the entire life cycle of the knowledge base, is presented. Concrete problems and initial results related to the implementation of automatic format mappings between ontologies and inconsistency checking issues are discussed in detail.

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Section: Discussionmentioning

confidence: 99%

“…The following formal definitions, which are introduced for showing some framework elements, have been adapted from [17], which in its turn has its mainstay in the Ogden-Richards' semiotic triangle [20].…”

Section: Appendix: Formal Definitions Of Ontology and Knowledge Base mentioning

confidence: 99%

A Cell-Cycle Knowledge Integration Framework

Antezana

Tsiporkova

Mironov

et al. 2006

Lecture Notes in Computer Science

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show abstract

“…4). In what regards argumentation humans (HumanArgumentation) tend to argue by providing strings of text stating (provides text) their reasons while machines tend to use other kinds of argumentation measures like Frequency and TFIDF [6]. For each algorithm used, new subclasses of argumentation need to be introduced to model the different kinds of measures.…”

Section: An Argumentation Ontology For Diligent Processesmentioning

confidence: 99%

An Argumentation Ontology for DIstributed, Loosely-controlled and evolvInG Engineering processes of oNTologies (DILIGENT)

Tempich

Pinto

Sure

et al. 2005

Lecture Notes in Computer Science

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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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“…Maedche's work on ontology "learning" suggests the potential for mapping between terminology and ontology via an ontology structure (Maedche 2002). If such a mapping between terminology and ontology can be made, large-scale validated terminology collections may be of value to ontology developers.…”

Section: A Terminological Perspective On Ontologymentioning

confidence: 99%

Terminology and the construction of ontology

Gillam¹,

Tariq²,

Ahmad³

2005

TERM

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Abstract. This paper discusses a method for extracting conceptual hierarchies from arbitrary domain-specific collections of text. These hierarchies can form a basis for a concept-oriented terminology collection, and hence may be used as the basis for developing knowledge-based systems via ontology editors. This reference to ontology is explored in the context of collections of terms. The method presented uses both statistical and linguistic techniques. The result of such an extraction may be useful in information retrieval, knowledge management, or in the discipline of terminology science itself.

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Ontology Learning for the Semantic Web

Cited by 838 publications

References 16 publications

A Cell-Cycle Knowledge Integration Framework

A Cell-Cycle Knowledge Integration Framework

An Argumentation Ontology for DIstributed, Loosely-controlled and evolvInG Engineering processes of oNTologies (DILIGENT)

Terminology and the construction of ontology

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