Knowledge conceptualization tool

Fujihara, H.; Simmons, Dick B.; Ellis, Newton C.; Shannon, Robert E.

doi:10.1109/69.591447

Cited by 13 publications

(3 citation statements)

References 24 publications

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“…The work has also been facilitated by studies on the automatic acquisition of shallow knowledge, which is the experience acquired heuristically while solving problems (Okamura, Baba, Takahashi, & Shiomi, 1991). In some cases, instead of suppressing the knowledge engineer as an intermediary, the focus has been on compensating for his lack of domain knowledge, and the resulting knowledge base is accurate and complete (Fujihara, Simmons, Ellis, & Shannon, 1997).…”

Section: Automatic Knowledge Extractionmentioning

confidence: 99%

Knowledge acquisition for decision support systems on an electronic assembly line

Gebus

Leiviskä

2009

Expert Systems with Applications

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Section: Automatic Knowledge Extractionmentioning

confidence: 99%

Knowledge acquisition for decision support systems on an electronic assembly line

Gebus

Leiviskä

2009

Expert Systems with Applications

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“…The aim of knowledge conceptualisation technology is to help knowledge managers extract and classify structured textual knowledge in order to obtain integrity and validity (Fujihara, Simmons, Ellis, and Shannon 1997). Textual knowledge conceptualisation can be processed through grammar structure analysis and the reasoning rule.…”

Section: Textual Knowledge Analysismentioning

confidence: 99%

A knowledge component extraction technology using figures and tables

Yang

Hou

Chen

2013

Journal of Experimental & Theoretical Artificial Intelligen

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With the growing complexity of document contents and the significant increase of domain knowledge, it is difficult for knowledge receivers to understand specific domain knowledge. However, traditional knowledge extraction schemes usually provide complete documents to knowledge receivers and much time is required for knowledge receivers to acquire domain knowledge. The concept of component-based knowledge is to divide documents into several knowledge components corresponding to more specific domains, which can be used to reduce the time required for the knowledge receivers to search the specific domain knowledge. Moreover, since the figures and tables in a document usually contain the important implicit knowledge expressed within the document, the aim of this research is to extract the knowledge components from documents (e.g. industry yearbooks) on the basis of figures and tables. In this research, a knowledge component extraction model with two algorithms, namely the keyword mapping algorithm and sentence mapping algorithm, is developed. In order to demonstrate the applicability of the proposed methodology, a web-based knowledge component extraction system is also established based on the proposed model. Furthermore, Taiwan Logistics Yearbooks are applied as examples to evaluate the proposed model. The verification results show that the developed system is a high-performance knowledge component extraction system. As a whole, this research provides an approach for knowledge receivers to efficiently and accurately acquire domain knowledge. domain knowledge and, in doing so, they spent much time reading irrelevant content (Hou and Lin 2004).If documents can be divided into several knowledge components, each corresponding to more specific domains that are underpinned by component-based knowledge, then the reader can efficiently search for specific domain knowledge. As the reader digests similar knowledge components, in order to acquire specific domain knowledge, he or she can search for domain-specific knowledge quickly and accurately by properly focusing on specific informational components.Critically implicit knowledge from documents is often given in the form of figures and tables, and domain-specific knowledge is embedded within the paragraphs surrounding these figures and tables. Notably, these paragraphs, which hold important information or knowledge, can be efficiently digested using component-based knowledge. If knowledge components can be extracted from documents based on figures and tables, then the time required for the reader to search for specific domain knowledge can be effectively reduced.Previously, when readers searched for specific domain knowledge through traditional knowledge extraction schemes, they had to digest a great deal of irrelevant content from entire documents in order to obtain the necessary domain-specific knowledge. In traditional knowledge extraction schemes, figures and tables served as an auxiliary tool which provided the intended contents for the reader. As such, the time require...

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“…For that reason, generic acquisition shells have been developed (Chien & Ho, 1992) and extended with methods for updating incomplete or partially incorrect knowledge bases (Tecuci, 1992) (Su et al, 2002). The work has also been facilitated by studies on the automatic acquisition of shallow knowledge, which is the experience acquired heuristically while solving problems (Okamura et al, 1991), or by compensating for the knowledge engineer's lack of domain knowledge, so that the resulting knowledge base is accurate and complete (Fujihara et al, 1997).…”

Section: Automatic Knowledge Extractionmentioning

confidence: 99%

Defect-Related Knowledge Acquisition for Decision Support Systems in Electronics Assembly

Gebus¹,

Leiviskä²

2007

Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics

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Real-time process control and production optimization are extremely challenging areas. Traditional approaches often lack in robustness or reliability when dealing with incomplete, inaccurate, or simply irrelevant data. This is a major problem when building decision support systems especially in electronics manufacturing, where blind feature extraction and data mining methods on large databases are common. Performance of these methods can be drastically increased when combined with knowledge or expertise of the process. This paper describes how defect-related knowledge on an electronic assembly line can be integrated in the decision making process at an operational and organizational level. It focuses in particular on the acquisition of shallow knowledge concerning everyday human interventions on the production lines, as well as on the conceptualization and factory wide sharing of the resulting defect information. Software with dedicated interfaces has been developed for that purpose. Semi-automatic knowledge acquisition from the production floor and generation of comprehensive reports for the quality department resulted in an improvement of the usability, usage, and usefulness of the decision support system.

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Knowledge conceptualization tool

Cited by 13 publications

References 24 publications

Knowledge acquisition for decision support systems on an electronic assembly line

Knowledge acquisition for decision support systems on an electronic assembly line

A knowledge component extraction technology using figures and tables

Defect-Related Knowledge Acquisition for Decision Support Systems in Electronics Assembly

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