IEEE Transactions on Knowledge and Data Engineering
Ontology Learning for the Semantic Web
IEEE Intelligent Systems
MnM: Ontology Driven Semi-automatic and Automatic Support for Semantic Markup
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
Using Text Mining to Infer Semantic Attributes for Retail Data Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
DOSE: A Distributed Open Semantic Elaboration Platform
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Multilingual semantic elaboration in the DOSE platform
Proceedings of the 2004 ACM symposium on Applied computing
Word sense disambiguation using Conceptual Density
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Contextual relevance feedback in web information retrieval
IIiX Proceedings of the 1st international conference on Information interaction in context
Collaborative content and user-based web ontology learning system
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
| Hi-index | 0.00 |
A great jump towards the advent of the Semantic Web will take place when a critical mass of web resources is available for use in a semantic way. This goal can be reached by the creation of semantic meta-data in the publication workflow, or by the development of systems and applications able to associate semantics to resources (i.e., annotating them) automatically. Those applications should analyze the content of a web page and should be able to associate some ontology classes to it. One particular issue in this context is to define a suitable relationship between each concept of the ontology and some words (or, more in general, strings) which are expected to appear in resources dealing with that concept, playing the role of "triggers" suggesting the relevance of a given text fragment to a concept.We hereby propose an approach that, starting from a set of textual representations created by experts (synsets), is able to automatically widen their lexical coverage by computing new, larger synsets, increasing the capability of a semantic application to correctly recognize the ontology classes a document is related to. In such approach, the initial textual representations are integrated and augmented by exploiting lexical networks like WordNet, which contain syntactic information connected through semantic relationships. Some algorithms are proposed to avoid misleading terms and consequently to perform sense disambiguation in WordNet.