S-CREAM — Semi-automatic CREAtion of Metadata

Handschuh, Siegfried; Staab, Steffen; Ciravegna, Fabio

doi:10.1007/3-540-45810-7_32

Cited by 186 publications

(113 citation statements)

References 8 publications

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“…Fleischman and Hovy's results are certainly very high in comparison to ours -and also to the ones of Hahn et al [15] and Alfonseca et al [2] -but on the other hand, though they address a harder task than the MUC Named Entity Task, they are still quite away from the number of categories we consider here. In [17] we proposed a semi-automatic approach to discovering instances of a concept by using a machine-learning based information extraction system (viz. Amilcare [7]).…”

Section: Related Workmentioning

confidence: 99%

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Towards the self-annotating web

Cimiano

Handschuh

Staab³

2004

Proceedings of the 13th International Conference on World Wide Web

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242

170

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The success of the Semantic Web depends on the availability of ontologies as well as on the proliferation of web pages annotated with metadata conforming to these ontologies. Thus, a crucial question is where to acquire these metadata. In this paper we propose PANKOW (Pattern-based Annotation through Knowledge on the Web), a method which employs an unsupervised, pattern-based approach to categorize instances with regard to an ontology. The approach is evaluated against the manual annotations of two human subjects. The approach is implemented in OntoMat, an annotation tool for the Semantic Web and shows very promising results.

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Section: Related Workmentioning

confidence: 99%

“…The reason is that in spite of methods and tools like large-scale information extraction [9], learning of information extraction rules [7], and the application of both in current annotation toolsets [17,28], the obstacles for producing such markup remain high:…”

Section: Introductionmentioning

confidence: 99%

Towards the self-annotating web

Cimiano

Handschuh

Staab³

2004

Proceedings of the 13th International Conference on World Wide Web

Self Cite

242

170

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“…The latest advances in automating ontology population and text annotation are promising [5,9,11,14]. While, if ever, ontologies and metadata (and the Semantic Web itself) become a worldwide commodity, the lack or incompleteness of available ontologies and KBs is a limitation we shall likely have to live with in the mid term.…”

Section: Introductionmentioning

confidence: 99%

An Ontology-Based Information Retrieval Model

Vallet

Fernández

Castells

2005

Lecture Notes in Computer Science

163

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in:The Abstract. Semantic search has been one of the motivations of the Semantic Web since it was envisioned. We propose a model for the exploitation of ontologybased KBs to improve search over large document repositories. Our approach includes an ontology-based scheme for the semi-automatic annotation of documents, and a retrieval system. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting algorithm, and a ranking algorithm. Semantic search is combined with keyword-based search to achieve tolerance to KB incompleteness. Our proposal is illustrated with sample experiments showing improvements with respect to keyword-based search, and providing ground for further research and discussion.

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“…The concepts and instances in the KB are linked to the documents by means of explicit, non-embedded annotations of the documents. Since we do not address the problem of knowledge extraction from text (Contreras, et al, 2004;Dill, et al, 2003;Handschuh, Staab & Ciravegna, 2002;Kiryakov, Popov, Terziev, Manov & Ognyanoff, 2004;Popov, Kiryakov, Ognyanoff, Manov & Kirilov, 2004), we provide a vocabulary and some simple mechanisms to aid in the semi-automatic annotation of documents, once ontology instances have been created (manually or automatically).…”

Section: Semantic Indexingmentioning

confidence: 99%