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Stella Maris Asiimwe thesis.pdf1.81 MBAdobe PDFView/Open
Title: A knowledge acquisition tool to assist case authoring from texts.
Authors: Asiimwe, Stella Maris
Supervisors: Craw, Susan
Wiratunga, Nirmalie
Taylor, Bruce J.
Keywords: Case-based reasoning
Information retrieval
Issue Date: Mar-2009
Publisher: The Robert Gordon University
Citation: ASIIMWE, S., CRAW, S., WIRATUNGA, N. and TAYLOR, B. 2007. Automatically acquiring structured case representations: the SMART way. In: Applications and Innovations in Intelligent Systems XV: Proceedings of the 27th BCS SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence. Spirnger. pp. 45-49
ASIIMWE, S., CRAW, S., WIRATUNGA, N. and TAYLOR, B. 2007. Automatic text standardisation by synonym mapping. In: Proceedings of the 12th UK Workshop on Case-Based Reasoning. CMS Press. pp. 88-98
ASIIMWE, S., CRAW, S., TAYLOR, B. and WIRATUNGA, N. 2007. Case authoring: from textual reports to knowledge-rich cases. In: Proceedings of the 7th International Conference on Case-Based Reasoning. Springer. pp. 179-193
ASIIMWE, S., CRAW, S. and TAYLOR, B. 2006. Discovering a concept hierarchy from Smart-House reports. In: Workshop Proceedings of the 8th European Conference on Case-Based Reasoning. pp. 88-97
Abstract: Case-Based Reasoning (CBR) is a technique in Artificial Intelligence where a new problem is solved by making use of the solution to a similar past problem situation. People naturally solve problems in this way, without even thinking about it. For example, an occupational therapist (OT) that assesses the needs of a new disabled person may be reminded of a previous person in terms of their disabilities. He may or may not decide to recommend the same devices based on the outcome of an earlier (disabled) person. Case-based reasoning makes use of a collection of past problem-solving experiences thus enabling users to exploit the information of others’ successes and failures to solve their own problem(s). This project has developed a CBR tool to assist in matching SmartHouse technology to the needs of the elderly and people with disabilities. The tool makes suggestions of SmartHouse devices that could assist with given impairments. SmartHouse past problem-solving textual reports have been used to obtain knowledge for the CBR system. Creating a case-based reasoning system from textual sources is challenging because it requires that the text be interpreted in a meaningful way in order to create cases that are effective in problem-solving and to be able to reasonably interpret queries. Effective case retrieval and query interpretation is only possible if a domain-specific conceptual model is available and if the different meanings that a word can take can be recognised in the text. Approaches based on methods in information retrieval require large amounts of data and typically result in knowledge-poor representations. The costs become prohibitive if an expert is engaged to manually craft cases or hand tag documents for learning. Furthermore, hierarchically structured case representations are preferred to flat-structured ones for problem-solving because they allow for comparison at different levels of specificity thus resulting in more effective retrieval than flat structured cases. This project has developed SmartCAT-T, a tool that creates knowledge-rich hierarchically structured cases from semi-structured textual reports. SmartCAT-T highlights important phrases in the textual SmartHouse problem-solving reports and uses the phrases to create a conceptual model of the domain. The model then becomes a standard structure onto which each semi-structured SmartHouse report is mapped in order to obtain the correspondingly structured case. SmartCAT-T also relies on an unsupervised methodology that recognises word synonyms in text. The methodology is used to create a uniform vocabulary for the textual reports and the resulting harmonised text is used to create the standard conceptual model of the domain. The technique is also employed in query interpretation during problem solving. SmartCAT-T does not require large sets of tagged data for learning, and the concepts in the conceptual model are interpretable, allowing for expert refinement of knowledge. Evaluation results show that the created cases contain knowledge that is useful for problem solving. An improvement in results is also observed when the text and queries are harmonised. A further evaluation highlights a high potential for the techniques developed in this research to be useful in domains other than SmartHouse. All this has been implemented in the Smarter case-based reasoning system.
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