GWC 2008

SemanticNet: a WordNet-based Tool for the Navigation of Semantic… 25
concepts through relations. Formulating the query , the user can move through the net
and extract the concepts which really interest him, limiting the search field and
obtaining a more specific result. The enriched semantic net can also be used directly
by the system without the user being aware of it. In fact, the system receives and
elaborates queries by means of the SemanticNet, extracts from the query the concepts
and their related relations, then shows the user a result set related to the new concepts
found as well as the found categories.
The automatic creation of a conceptual knowledge map using documents coming
from the Web is a very relevant problem and it is very hard because of the difficulty
to distinguish between valid and invalid contents documents. We therefore realized
the importance of being able to access a multidisciplinary structure of documents, so a
great amount of documents included in Wikipedia [12] was used to extract new
knowledge and to define a new semantic net enriching WordNet with new terms, their
classification and new associative relations.
In fact WordNet, as semantic net, is too limited with respect to the web language
dictionary. WordNet contains about 150,000 words organized in over 115,000 synsets
whereas Wikipedia contains about 1.900.000 encyclopedic information; the number
of connections between words related by topics is limited; several word “senses” are
not included in WordNet. These are only some of the reasons that convinced us to
enrich the WordNet semantic net , as emphasized in [13] where authors identify this
and 5 other weaknesses in the WordNet semantic net constitution.
We chose Wikipedia, the free content encyclopedia, excluding other solutions
such as language specific thesaurus or on-line encyclopedia available only in a
specific language. A conceptual map built using Wikipedia pages allows a user to
associate a concept to other ones enriched with some relations that an author points
out. The use of Wikipedia guarantees, with reasonable certainty, that such conceptual
connection is valid because it is produced by someone who, at least theoretically, has
the skills or the experience to justify it. Moreover, the rules and the reviewers controls
set up guarantee reliability and objectivity and the correctness of the inserted topics.
The reviewers also control the conformity of the added or modified voices.
What we are more interested in are terms and their classification in order to build
an enriched semantic net, called “SemanticNet” to be used in the searching phase in
the general context, while in the specific context we build a specialized Semantic Net
(sSN). The reason for this is that varied mental association of places, events, persons
and things depend on the cultural backgrounds of the users' personal history. In fact,
the ability to associate a concept to another is different from person to person. The
SemanticNet is definitely not exhaustive, it is limited by the dictionary of WordNet,
by the contents included in Wikipedia and by the accuracy of the information given
by the system.
Starting from the information contained in Wikipedia about a term of WordNet, the
system is capable of enriching the SemanticNet by adding new nodes, links and
attributes, such as IS-A or PART-OF relations. Moreover, the system is able to
classify the textual contents of web resources, indexed through the Classifier that uses
WordNet Domains and applies a density function ([1], [2]), based on the computation
of the number of synsets related to each term of the document. In this way, it is able to
retrieve the most frequently used “senses” by extracting the synonyms relations given
by the use of similar terms in the document. Through the categorization of the