2 research outputs found
An Ontology Based Approach to Measuring the Semantic Similarity between Information Objects in Personal Information Collections
This paper introduces a semantic approach to personal information management, which employs natural language processing, ontologies and a vector space model to measure the semantic similarity between information objects in personal information collections. The approach involves natural language processing, named entity recognition, and information object integration. In particular, natural language processing is used to detect meaningful and semantically distinguishable information objects within collections of personal information. Then, the named entities are extracted from these information objects and their features (such as weight and category) are used to measure the semantic similarity between them. Further research includes using the semantic similarity measure developed to index and retrieve information objects in a semantic based system for personal information management
Ontology-based semantic reminiscence support system
This thesis addresses the needs of people who find reminiscence helpful in focusing on
the development of a computerised reminiscence support system, which facilitates the
access to and retrieval of stored memories used as the basis for positive interactions
between elderly and young, and also between people with cognitive impairment and
members of their family or caregivers.
To model users’ background knowledge, this research defines a light weight useroriented
ontology and its building principles. The ontology is flexible, and has
simplified knowledge structure populated with semantically homogeneous ontology
concepts. The user-oriented ontology is different from generic ontology models, as it
does not rely on knowledge experts. Its structure enables users to browse, edit and
create new entries on their own.
To solve the semantic gap problem in personal information retrieval, this thesis
proposes a semantic ontology-based feature matching method. It involves natural
language processing and semantic feature extraction/selection using the user-oriented
ontology. It comprises four stages: (i) user-oriented ontology building, (ii) semantic
feature extraction for building vectors representing information objects, (iii) semantic
feature selection using the user-oriented ontology, and (iv) measuring the similarity
between the information objects.
To facilitate personal information management and dynamic generation of content,
the system uses ontologies and advanced algorithms for semantic feature matching.
An algorithm named Onto-SVD is also proposed, which uses the user-oriented
ontology to automatically detect the semantic relations within the stored memories. It
combines semantic feature selection with matrix factorisation and k-means clustering
to achieve topic identification based on semantic relations.
The thesis further proposes an ontology-based personalised retrieval mechanism for
the system. It aims to assist people to recall, browse and re-discover events from their
lives by considering their profiles and background knowledge, and providing them
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with customised retrieval results. Furthermore, a user profile space model is defined,
and its construction method is also described. The model combines multiple useroriented
ontologies and has a self-organised structure based on relevance feedback.
The identification of person’s search intentions in this mechanism is on the conceptual
level and involves the person’s background knowledge. Based on the identified search
intentions, knowledge spanning trees are automatically generated from the ontologies
or user profile spaces. The knowledge spanning trees are used to expand and reform
queries, which enhance the queries’ semantic representations by applying domain
knowledge.
The crowdsourcing-based system evaluation measures users’ satisfaction on the
generated content of Sem-LSB. It compares the advantage and disadvantage of three
types of content presentations (i.e. unstructured, LSB-based and semantic/knowledgebased).
Based on users’ feedback, the semantic/knowledge-based presentation is
considered to have higher overall satisfaction and stronger reminiscing support effects
than the others