GETESS—Searching the Web Exploiting German Texts

Staab, Steffen; Braun, Christian; Bruder, Ilvio; Düsterhöft, Antje; Heuer, Andreas; Klettke, Meike; Neumann, Günter; Prager, Bernd; Pretzel, Jan; Schnurr, Hans-Peter; Studer, Rudi; Uszkoreit, Hans; Wrenger, Burkhard

doi:10.1007/3-540-48414-0_7

Cited by 27 publications

(15 citation statements)

References 5 publications

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“…They used a pruned version of the tourism ontology developed within the GETESS project ( [27]). The original ontology consisted of 1043 concepts, while the pruned one consisted of 682.…”

Section: Test Setmentioning

confidence: 99%

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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“…They used a pruned version of the tourism ontology developed within the GETESS project ( [27]). The original ontology consisted of 1043 concepts, while the pruned one consisted of 682.…”

Section: Test Setmentioning

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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“…To evaluate the generality measure and to show that it might be applicable within other projects, we used the finance ontology 7 which was developed within the GETESS project [32] and which has been used to evaluate several other taxonomy learning algorithms, e.g. in [6].…”

Section: Discussionmentioning

confidence: 99%

A dynamic ontology for a dynamic reference work

Niepert

Buckner

Allen

2007

Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries

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The successful deployment of digital technologies by humanities scholars presents computer scientists with a number of unique scientific and technological challenges. The task seems particularly daunting because issues in the humanities are presented in abstract language demanding the kind of subtle interpretation often thought to be beyond the scope of artificial intelligence, and humanities scholars themselves often disagree about the structure of their disciplines. The future of humanities computing depends on having tools for automatically discovering complex semantic relationships among different parts of a corpus. Digital library tools for the humanities will need to be capable of dynamically tracking the introduction of new ideas and interpretations and applying them to older texts in ways that support the needs of scholars and students.This paper describes the design of new algorithms and the adjustment of existing algorithms to support the automated and semiautomated management of domain-rich metadata for an established digital humanities project, the Stanford Encyclopedia of Philosophy. Our approach starts with a "hand-built" formal ontology that is modified and extended by a combination of automated and semiautomated methods, thus becoming a "dynamic ontology". We assess the suitability of current information retrieval and information extraction methods for the task of automatically maintaining the ontology. We describe a novel measure of term-relatedness that appears to be particularly helpful for predicting hierarchical relationships in the ontology. We believe that our project makes a further contribution to information science by being the first to harness the collaboration inherent in a expert-maintained dynamic reference work to the task of maintaining and extending a formal ontology. We place special emphasis on the task of bringing domain expertise to bear on all phases of the development and deployment of the system, from the initial design of the software and ontology to its dynamic use in a fully operational digital reference work.

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“…The input ontology is a tourism ontology developed by the GETESS project [25], which contains 682 concepts but no instances. The experiment process is shown in Fig.…”

Section: Experiments and Evaluationmentioning

confidence: 99%

OntoPop: An Ontology Population System for the Semantic Web

Thongkrau

Lalitrojwong

2012

IEICE Trans. Inf. & Syst.

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SUMMARYThe development of ontology at the instance level requires the extraction of the terms defining the instances from various data sources. These instances then are linked to the concepts of the ontology, and relationships are created between these instances for the next step. However, before establishing links among data, ontology engineers must classify terms or instances from a web document into an ontology concept. The tool for help ontology engineer in this task is called ontology population. The present research is not suitable for ontology development applications, such as long time processing or analyzing large or noisy data sets. OntoPop system introduces a methodology to solve these problems, which comprises two parts. First, we select meaningful features from syntactic relations, which can produce more significant features than any other method. Second, we differentiate feature meaning and reduce noise based on latent semantic analysis. Experimental evaluation demonstrates that the OntoPop works well, significantly out-performing the accuracy of 49.64%, a learning accuracy of 76.93%, and executes time of 5.46 second/instance. key words: ontology population, syntactic feature extraction, latent semantic analysis, semantic web

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GETESS—Searching the Web Exploiting German Texts

Cited by 27 publications

References 5 publications

Towards the self-annotating web

Towards the self-annotating web

A dynamic ontology for a dynamic reference work

OntoPop: An Ontology Population System for the Semantic Web

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