This work presents an experimental semantic approach for mining knowledge from the World Wide Web (WWW). The main goal is to build a context-specific knowledge base from web documents. The basic idea is to use a reference knowledge provided by a dictionary as the indexing structure of domain-specific computed knowledge instances organised in the form of interlinked text words. The WordNet lexical database has been used as reference knowledge for the English web documents. Both the reference and the computed knowledge are actually conceived as word graphs. Graph is considered here as a powerful way to represent structured knowledge. This assumption has many consequences on the way knowledge can be explored and similar knowledge patterns can be identified. In order to identify context-specific elements in knowledge graphs, the novel semantic concept of “minutia” has been introduced. A preliminary evaluation of the efficacy of the proposed approach has been carried out. A fair comparison strategy with other non-semantic competing approaches is currently under investigation.
Mining Context-Specific Web Knowledge : an Experimental Dictionary-based Approach / V. Di Lecce, M. Calabrese, D. Soldo - In: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence / [a cura di] D.S. Huang, D.C. Wunsch, D.S. Levine, K.H. Jo. - Berlin : Springer-Verlag, 2008. - ISBN 978-3-540-85983-3. - pp. 896-905 (( Intervento presentato al 4. convegno International Conference on Intelligent Computing, ICIC tenutosi a Shanghai nel 2008.
|Titolo:||Mining Context-Specific Web Knowledge : an Experimental Dictionary-based Approach|
CALABRESE, MARCO (Secondo)
|Parole Chiave:||Knowledge Discovery; Semantic Web; Web Mining; WordNet|
|Settore Scientifico Disciplinare:||Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni|
|Data di pubblicazione:||2008|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1007/978-3-540-85984-0_108|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|