The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Evaluating the Construction of Domain Ontologies for Recommender Systems Based on Texts
|
|
Author(s): Stanley Loh (Catholic University of Pelotas, Brazil), Daniel Lichtnow (Lutheran University of Brazil, Brazil), Thyago Borges (Lutheran University of Brazil, Brazil)and Gustavo Piltcher (Lutheran University of Brazil, Brazil)
Copyright: 2008
Pages: 14
Source title:
Data Mining with Ontologies: Implementations, Findings, and Frameworks
Source Author(s)/Editor(s): Hector Oscar Nigro (Universidad Nacional del Centro de la Provincia de Buenos Aires, Argentina), Sandra Elizabeth Gonzalez Cisaro (Universidad Nacional del Centro de la Provincia de Buenos Aires, Argentina)and Daniel Hugo Xodo (Universidad Nacional del Centro de la Provincia de Buenos Aires, Argentina)
DOI: 10.4018/978-1-59904-618-1.ch008
Purchase
|
Abstract
This chapter investigates different aspects in the construction of a domain ontology to a content-based recommender system. The recommender systems suggests textual electronic documents from a Digital Library, based on documents read by the users and based on textual messages posted in electronic discussions through a web chat. The domain ontology is used to represent the user’s interest and the content of the documents. In this context, the ontology is composed by a hierarchy of concepts and keywords. Each concept has a vector of keywords with weights associated. Keywords are used to identify the content of the texts (documents and messages), through the application of text mining techniques. The chapter discusses different approaches for constructing the domain ontology, including the use of text mining software tools for supervised learning, the interference of domain experts in the engineering process and the use of a normalization step.
Related Content
|
.
© 2023.
34 pages.
|
|
.
© 2023.
15 pages.
|
|
.
© 2023.
15 pages.
|
|
.
© 2023.
18 pages.
|
|
.
© 2023.
24 pages.
|
|
.
© 2023.
32 pages.
|
|
.
© 2023.
21 pages.
|
|
|