Alani, H.; Harris, S. & O'Neil, B.: Winnowing Ontologies Based on Application Use..
ESWC. 2006, S. 185-199
[BibTeX]
de Boer, V.; van Someren, M. & Wielinga, B. J.: Extracting Instances of Relations from Web Documents Using Redundancy..
ESWC. 2006, S. 245-258
[BibTeX]
Gangemi, A.; Catenacci, C.; Ciaramita, M. & Lehmann, J.: Modelling Ontology Evaluation and Validation..
ESWC. 2006, S. 140-154
[BibTeX]
Garcia-Castro, R. & Gómez-Pérez, A.: Benchmark Suites for Improving the RDF(S) Importers and Exporters of Ontology Development Tools..
ESWC. 2006, S. 155-169
[BibTeX]
Ghidini, C. & Serafini, L.: Reconciling Concepts and Relations in Heterogeneous Ontologies..
ESWC. 2006, S. 50-64
[BibTeX]
Giunchiglia, F.; Marchese, M. & Zaihrayeu, I.: Encoding Classifications into Lightweight Ontologies..
ESWC. 2006, S. 80-94
[BibTeX]
Heß, A.: An Iterative Algorithm for Ontology Mapping Capable of Using Training Data..
ESWC. 2006, S. 19-33
[BibTeX]
Hoser, B.; Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.: Semantic Network Analysis of Ontologies. In: Sure, Y. & Domingue, J. (Hrsg.):
The Semantic Web: Research and Applications. Heidelberg: Springer, 2006 (LNAI 4011), S. 514-529
[Volltext] [Kurzfassung]
[BibTeX]
A key argument for modeling knowledge in ontologies is the easy re-use and re-engineering of the knowledge. However, beside consistency checking, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as (labeled, directed) graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA). While social network structures in general currently receive high attention in the Semantic Web community, there are only very few SNA applications up to now, and virtually none for analyzing the structure of ontologies. We illustrate in this paper the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality based on Hermitian matrices, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size.
Kalyanpur, A.; Parsia, B.; Sirin, E. & Grau, B. C.: Repairing Unsatisfiable Concepts in OWL Ontologies..
ESWC. 2006, S. 170-184
[BibTeX]
Lei, Y.; Sabou, M.; Lopez, V.; Zhu, J.; Uren, V. & Motta, E.: An Infrastructure for Acquiring High Quality Semantic Metadata..
ESWC. 2006, S. 230-244
[BibTeX]
Novácek, V. & Smrz, P.: Empirical Merging of Ontologies - A Proposal of Universal Uncertainty Representation Framework..
ESWC. 2006, S. 65-79
[BibTeX]
Plessers, P. & Troyer, O. D.: Resolving Inconsistencies in Evolving Ontologies..
ESWC. 2006, S. 200-214
[BibTeX]
Schmitz, C.; Hotho, A.; Jäschke, R. & Stumme, G.: Content Aggregation on Knowledge Bases using Graph Clustering. In: Sure, Y. & Domingue, J. (Hrsg.):
The Semantic Web: Research and Applications. Heidelberg: Springer, 2006 (LNAI 4011), S. 530-544
[Volltext] [Kurzfassung]
[BibTeX]
Recently, research projects such as PADLR and SWAP have developed tools like Edutella or Bibster, which are targeted at establishing peer-to-peer knowledge management (P2PKM) systems. In such a system, it is necessary to obtain provide brief semantic descriptions of peers, so that routing algorithms or matchmaking processes can make decisions about which communities peers should belong to, or to which peers a given query should be forwarded. This paper provides a graph clustering technique on knowledge bases for that purpose. Using this clustering, we can show that our strategy requires up to 58% fewer queries than the baselines to yield full recall in a bibliographic P2PKM scenario.
Serafini, L.; Zanobini, S.; Sceffer, S. & Bouquet, P.: Matching Hierarchical Classifications with Attributes..
ESWC. 2006, S. 4-18
[BibTeX]
Tempich, C.; Pinto, H. S. & Staab, S.: Ontology Engineering Revisited: An Iterative Case Study..
ESWC. 2006, S. 110-124
[BibTeX]
van Assem, M.; Malaisé, Vé.; Miles, A. & Schreiber, G.: A Method to Convert Thesauri to SKOS.. In: Sure, Y. & Domingue, J. (Hrsg.):
ESWC. Springer, 2006 (Lecture Notes in Computer Science 4011), S. 95-109
[BibTeX]
Wang, T.; Li, Y.; Bontcheva, K.; Cunningham, H. & Wang, J.: Automatic Extraction of Hierarchical Relations from Text..
ESWC. 2006, S. 215-229
[BibTeX]
Zhdanova, A. V. & Shvaiko, P.: Community-Driven Ontology Matching..
ESWC. 2006, S. 34-49
[BibTeX]