CONSTRUCTION OF FRAME HIERARCHIES USING MACHINE LEARNING
DOI:
https://doi.org/10.7152/acro.v6i1.12660Abstract
In this paper, we describe an architecture for helping frame hierarchy conception. This architecture is based on machine learning and cognitive psychological studies on categorizotion. Our basic assumption is that categorization should be considered as a goal-driven, context--dependent process and therefore the hierarchical organization of categories should be represented in different perspectives. The core of our architecture is a learning system of categorization that generates multi.perspective hierarchies. Concept hierarchies are, at first, generated in a probabilistic representation and after translated into a frame one.Downloads
Published
1995-10-31
Issue
Section
Articles
License
Authors who publish with this journal agree to the following terms:- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
