GWC 2008

SemanticNet: a WordNet-based Tool for the Navigation of Semantic… 33
6 Conclusions and Future Work
In this paper we have described the SemanticNet as part of the project DART,
Distributed Agent-Based Retrieval Toolkit, currently under development. We have
focused the efforts into the idea to provide a user friendly tool, able to reach and filter
relevant information by means of a conceptual map based on WordNet. One of the
main aspect of this work has been the right classification of resources. It has been
very important in order to enrich the WordNet semantic net with new contents
extracted from Wikipedia pages and with concepts coming from the GEMET
thesaurus.
The SemanticNet is structured as a highly connected directed graph. Each vertex is
a node of the net and the edges are the relations between them. Each element, vertex
and edge, is labeled in order to give the user a better usability in information
navigation even through a dedicated 3D tool.
Future works will include the improvement of the SemanticNet, by extracting new
nodes and relations, and the measuring of the user preferences in the navigation of the
net in order to give a weight to the more used paths between nodes.
Moreover the structure of nodes, as defined in the net, allows to access the glosses
given by WordNet and Wikipedia contents. The geographic context gives the user
further filtering elements in the search of Web contents. In order to make the
implementation of modules of a specialized context easier, its conceptual mapping
and the definition of the specialized semantic net, a future work will describe the
SemanticNet with a simple formalism like SKOS. Therefore the system could be
more flexible in indexing and searching of web resources.
References
1. Angioni, M. et al.: DART: The Distributed Agent-Based Retrieval Toolkit. In: Proc.
of CEA 07, pp. 425–433. Gold Coast – Australia (2007)
2. Angioni, M. et al.: User Oriented Information Retrieval in a Collaborative and
Context Aware Search Engine. J. WSEAS Transactions on Computer Research,
ISSN: 1991-8755, 2(1), 79–86 (2007)
3. Miller, G. et al.: WordNet: An Electronic Lexical Database. Bradford Books (1998)
4. Angioni, M., Demontis, R., Tuveri, F.: Enriching WordNet to Index and Retrieve
Semantic Information. In: 2nd International Conference on Metadata and Semantics
Research, 11–12 October 2007, Ionian Academy, Corfu, Greece (2007)
5. Wordnet in RDFS and OWL,
http://www.w3.org/2001/sw/BestPractices/WNET/wordnet-sw-20040713.html
6. Sleator, D.D., Temperley, D.: Parsing English with a Link Grammar. In: Third
International Workshop on Parsing Technologies (1993)
7. Scott, S., Matwin, S.: Text Classification using WordNet Hypernyms. In:
COLING/ACL Workshop on Usage of WordNet in Natural Language Processing
Systems, Montreal (1998)
8. Magnini, B., Strapparava, C., Pezzulo, G., Gliozzo, A.: The Role of Domain
Information in Word Sense Disambiguation. J. Natural Language Engineering,
special issue on Word Sense Disambiguation, 8(4), 359-373. Cambridge University
Press (2002)