Knowledge-Intensive Conversational Case-Based Reasoning in Software Component Retrieval
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Case based reasoning (CBR) is a problem solving method that reuses the previous problem solving experiences (represented as cases) to solve the new problem. As a type of interactive CBR, conversational CBR has been proposed to help users construct their problem descriptions incrementally through a mixed-initiative question-answering sequence. In software component retrieval, users meet the difficulty in well-defining their component queries. As a solution to release this difficulty, we proposed and implemented a conversational component retrieval model (CCRM) using a knowledge-intensive conversational CBR method in the thesis. The research activities and contributions followed two directions: theoretical research on conversational CBR to provide an efficient and natural conversation process, and applying and adapting conversational CBR to support software component retrieval. In the theoretical research direction, we first provided a framework to classify the similarity methods in CBR from the perspective of what features were taken into account during the similarity calculation, and further analyzed and illustrated that the query-biased similarity methods (only considering the features appearing in the query during the similarity calculation) were more suitable for conversational CBR applications. A knowledge-intensive conversational CBR method was designed that was able to utilize the general domain knowledge to improve the efficiency and naturalness of the conversation process. Four knowledge-intensive question selection tasks, including the feature inferencing, the integrated question ranking, the consistent question clustering, and the coherent question sequencing, were identified and handled in this method. We also proposed a lazy dialog learning mechanism that could continuously improve the performance of conversational CBR. Following the conversational component retrieval application direction, we reviewed and analyzed the current software component retrieval methods and proposed a conversational component retrieval model. In order to represent both the software components and the component queries as cases, it is necessary for a case to have multiple values on some features (generalized cases). In the research, we analyzed the feasibilities and discussed the methods to extend conversational CBR to support generalized cases from three aspects: the case representation, the similarity calculation metric, and the question selection method. At the end, a knowledge-intensive conversational software component retrieval system (TrollCCRM), enhanced by the above research findings, was implemented and evaluated on the image processing software component retrieval application. The evaluation results so far gave us positive results that the TrollCCRM system provided an efficient and natural conversation process guiding users to find their desired software components.
Has partsGu, Mingyang; Aamodt, Agnar. Explanation-Boosted Question Selection in Conversational CBR. Proceedings of the ECCBR 2004 Workshops, the 7th European Conference on Case-Based Reasoning - 30th August -2nd September, 2004, Technical Report, Vol. 142, No.04: 105-114, 2004.
Gu, Mingyang; Aamodt, Agnar; Tong, Xin. Component Retrieval Using Conversational Case-Based Reasoning. Intelligent Information Processing II - International Conference on Intelligent Information Processing (IIP2004) - IFIP International Federation for Information Processing, Vol. 163: 259-271, 2004.
Gu, Mingyang; Tong, Xin; Aamodt, Agnar. Comparing Similarity Calculation Methods in Conversational CBR. Proceedings of the 2005 IEEE International Conference on Information Reuse and Integration - Hilton, Las Vegas, Nevada, USA, August, 2005: 427-432, 2005.
Gu, Mingyang; Aamodt, Agnar. A Knowledge-Intensive Method for Conversational CBR. Case-Based Reasoning Research and Development - Proceedings of the 6th International Conference on Case-Based Reasoning, Chicago, Illinois, Auguest, 2005, Lecture Notes in Artificial Intelligence, Vol.3620: 296-311, 2005.
Gu, Mingyang; Aamodt, Agnar. Dialog Learning in Conversational CBR. Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference - Melbourne Beach, Florida. May 11–13, 2006: 358, 2006.
Gu, Mingyang; Bø, Ketil. Component Retrieval Using Knowledge-Intensive Conversational CBR. Advances in Applied Artificial Intelligence, 19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE - Annecy, France, June 27-30, 2006, Proceedings. Lecture Notes in Computer Science 4031: 554-563, 2006.
Gu, Mingyang; Aamodt, Agnar. Evaluating CBR Systems Using Different Data Sources: A Case Study. 8th European Conference on Case-Based Reasoning, Fethiye, Yurkey, September 2006, 2006.