User-oriented ontology-based clustering of stored memories

Shi, Lei and Setchi, Rossitza ORCID: https://orcid.org/0000-0002-7207-6544 2012. User-oriented ontology-based clustering of stored memories. Expert Systems with Applications 39 (10) , pp. 9730-9742. 10.1016/j.eswa.2012.02.087

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Abstract

This research addresses the needs of people who find reminiscence helpful. It focuses on the development of a computerised system called a Life Story Book (LSB), which facilitates access and retrieval of stored memories used as the basis for positive interactions between elderly and young, and especially between people with cognitive impairment and members of their family or caregivers. To facilitate information management and dynamic generation of content, this paper introduces a semantic model of LSB which is based on the use of ontologies and advanced algorithms for feature selection and dimension reduction. Furthermore, the paper defines a light weight user-oriented domain ontology and its building principles. It then proposes an algorithm called Onto-SVD, which uses the user-oriented ontology to automatically detect the semantic relations within the stored memories. It combines semantic feature selection with k-means clustering and Singular Value Decomposition (SVD) to achieve topic identification based on semantic similarity. The experiments conducted explore the effect of semantic feature selection as a result of establishing indirect relations, with the help of the ontology, within the information content. The results show that Onto-SVD considerably outperforms SVD in both topic identification and semantic disambiguation.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TA Engineering (General). Civil engineering (General)
Uncontrolled Keywords:	Reminiscence; Life Story Book; Semantic technology; User-oriented ontology; Clustering; Topic identification; Assistive technology
Publisher:	Elsevier
ISSN:	0957-4174
Last Modified:	06 Jul 2023 10:17
URI:	https://orca.cardiff.ac.uk/id/eprint/37466

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