The Role of Information Extraction for Textual CBR

Brüninghaus, Stefanie; Ashley, Kevin D.

doi:10.1007/3-540-44593-5_6

Cited by 50 publications

(25 citation statements)

References 12 publications

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“…(Brüninghaus & Ashley 2001) were the first authors to discuss it, but they do not address the automation of the automated process of the creation and validation of the cases from textual documentation (ie. manuals).…”

Section: Discussionmentioning

confidence: 99%

Case Authoring from Text and Historical Experiences

Zaluski

Japkowicz

Matwin

2003

Advances in Artificial Intelligence

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Abstract. The problem of repair and maintenance of complex systems, such as aircraft, cars and trucks is certainly a nontrivial task. Maintenance technicians must use a great amount of knowledge and information resources to solve problems that may occur. This paper describes a semi-automated tool that sorts through the mass of information a maintenance technician must consult in order to make a repair, thus helping him decide how to tackle the problem and thereby increasing his efficiency and, possibly, his reliability. Our tool was developed using state-of-the-art Case-Based Reasoning and Information Extraction technologies. More specifically, we developed a semi-automated Case Authoring method that creates a Case-Base in two steps. It begins by extracting knowledge from readily available resources such as technical documents and follows by complementing those cases using individual experiences in the maintenance organization. The case-base developed is a reflection of the knowledge encoded in the technical documentation and an authentication of the cases with real historical instances. Our case authoring approach is applied to the real world in the aerospace domain.

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Section: Discussionmentioning

confidence: 99%

Case Authoring from Text and Historical Experiences

Zaluski

Japkowicz

Matwin

2003

Advances in Artificial Intelligence

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“…The slots are obtained by first tuning an information extraction system using a corpus of documents annotated with the filled templates. SMILE [3] makes use of an information extraction system Autoslog [10] to extract relevant information in order to learn indexing concepts for textual cases. Common among these systems is the significant amount of manual intervention required for tuning the information extractors.…”

Section: Related Workmentioning

confidence: 99%

Automatically Acquiring Structured Case Representations: The SMART Way

Asiimwe¹,

Craw²,

Wiratunga³

et al. 2008

Applications and Innovations in Intelligent Systems XV

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“…Over the years, CBR has found wide acceptance in diagnosis [16] and customer support [28]. Digital Equipment Corporation's CASCADE, for instance, was created to diagnose VMS device driver malfunctions [29].…”

Section: Knowledge Manipulation and Operationsmentioning

confidence: 99%

Hybrid case-based reasoning for on-line product fault diagnosis

Lee

2005

Int J Adv Manuf Technol

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This paper presents a hybrid case-based reasoning system for on-line technical support of PC fault diagnosis. HyCase consists of a natural language (keyword) input and the graph-theoretic constraint-net.Natural language or keyword inputs are parsed and then generated into a constraint-net. The constraint-net is validated and its links rationalized and standardized to minimize ambiguity. Cases that partially match either the keywords or the constraint-net are ranked according to their matching scores based on four different preferences, and then retrieved and presented to the user. The case base, which was developed in Microsoft Access 2000, is updated by means of a keyword and constraint-net manager. HyCase was implemented on a PC with a Pentium III processor running at 500 MHz and 128 MB of SDRAM.Twenty typical queries from customers were tested on a collection of 174 cases based on different cut-off overall matching scores. The effectiveness of case retrieval was measured by the proportion of relevant cases retrieved from the case base (recall) and, of these, the proportion directly applicable to the problem at hand (precision). The accuracy of the natural language parser was ascertained to range between 62.5% and 87.7%, while a parsing accuracy of 60% is sufficient to ensure a reasonable recall and precision. A minimum overall matching score of about 0.5 ensures a precision of 0.60. The parsing time gets noticeably longer when there are more than 15 words. Except for minimum overall matching scores exceeding 0.1, the parsing time is largely independent of the minimum score.The merits and limitations of HyCase are also discussed.

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The Role of Information Extraction for Textual CBR

Cited by 50 publications

References 12 publications

Case Authoring from Text and Historical Experiences

Case Authoring from Text and Historical Experiences

Automatically Acquiring Structured Case Representations: The SMART Way

Hybrid case-based reasoning for on-line product fault diagnosis

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