| Author | Title | Year | Journal/Proceedings | Reftype | DOI/URL |
|---|---|---|---|---|---|
| Hoser, B., Hotho, A., Jäschke, R., Schmitz, C. & Stumme, G. | Semantic Network Analysis of Ontologies | 2006 | Vol. 4011The Semantic Web: Research and Applications, pp. 514-529 |
inproceedings | URL |
| Abstract: A key argument for modeling knowledge in ontologies is the easy -use and re-engineering of the knowledge. However, beside nsistency checking, current ontology engineering tools provide ly basic functionalities for analyzing ontologies. Since tologies can be considered as (labeled, directed) graphs, graph alysis techniques are a suitable answer for this need. Graph alysis has been performed by sociologists for over 60 years, and sulted in the vivid research area of Social Network Analysis NA). While social network structures in general currently receive gh attention in the Semantic Web community, there are only very w SNA applications up to now, and virtually none for analyzing the ructure of ontologies. e illustrate in this paper the benefits of applying SNA to |
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BibTeX:
@inproceedings{hoser2006semantic,
author = {Hoser, Bettina and Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
title = {Semantic Network Analysis of Ontologies},
booktitle = {The Semantic Web: Research and Applications},
publisher = {Springer},
year = {2006},
volume = {4011},
pages = {514-529},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hoser2006semantic.pdf}
}
|
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| Stumme, G., Hotho, A. & Berendt, B. | Semantic Web Mining - State of the Art and Future Directions | 2006 | Journal of Web Semantics Vol. 4(2), pp. 124-143 |
article | URL |
| Abstract: SemanticWeb Mining aims at combining the two fast-developing research areas SemanticWeb andWeb Mining. is survey analyzes the convergence of trends from both areas: an increasing number of researchers is working on proving the results ofWeb Mining by exploiting semantic structures in theWeb, and they make use ofWeb Mining chniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic b itself. e Semantic Web is the second-generation WWW, enriched by machine-processable information which supports e user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of ese resources. Therefore, automated schemes for learning the relevant information are increasingly being used. b Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily ntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore, rmalizations of the semantics of Web sites and navigation behavior are becoming more and more common. rthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web ning and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not t realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer tegration could be profitable. |
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BibTeX:
@article{jws2006Semantic,
author = {Stumme, Gerd and Hotho, Andreas and Berendt, Bettina},
title = {Semantic Web Mining - State of the Art and Future Directions},
journal = {Journal of Web Semantics},
publisher = {Elsevier},
year = {2006},
volume = {4},
number = {2},
pages = {124-143},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/stumme2006semantic.pdf}
}
|
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Created by JabRef on 28/04/2024.