Extracting knowledge from web communities and linked data for case‐based reasoning systems

Sauer, Christian; Roth–Berghofer, Thomas

doi:10.1111/exsy.12034

Cited by 6 publications

(7 citation statements)

References 16 publications

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“…Finally, a comparison of the presented KCM-CD methodology with the state-of-the-art knowledge construction methodologies (Jindal & Taneja, 2013;Leao et al, 2013;Reuss et al, 2015;Sauer & Roth-Berghofer, 2014) was performed, which is illustrated in Table 11. All these methodologies convert unstructured text into a structured form to construct executable knowledge.…”

Section: Model Evaluationmentioning

confidence: 99%

A knowledge construction methodology to automate case‐based learning using clinical documents

et al. 2019

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The case‐based learning (CBL) approach has gained attention in medical education as an alternative to traditional learning methodology. However, current CBL systems do not facilitate and provide computer‐based domain knowledge to medical students for solving real‐world clinical cases during CBL practice. To automate CBL, clinical documents are beneficial for constructing domain knowledge. In the literature, most systems and methodologies require a knowledge engineer to construct machine‐readable knowledge. Keeping in view these facts, we present a knowledge construction methodology (KCM‐CD) to construct domain knowledge ontology (i.e., structured declarative knowledge) from unstructured text in a systematic way using artificial intelligence techniques, with minimum intervention from a knowledge engineer. To utilize the strength of humans and computers, and to realize the KCM‐CD methodology, an interactive case‐based learning system(iCBLS) was developed. Finally, the developed ontological model was evaluated to evaluate the quality of domain knowledge in terms of coherence measure. The results showed that the overall domain model has positive coherence values, indicating that all words in each branch of the domain ontology are correlated with each other and the quality of the developed model is acceptable.

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Section: Model Evaluationmentioning

confidence: 99%

A knowledge construction methodology to automate case‐based learning using clinical documents

et al. 2019

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“…Various methods which make use of techniques for semantic analysis such as semantic relation concept triplets (Paik, Liddy, Liddy, Niles, & Allen, ) and Noun‐Verb‐Noun searching are introduced in the work of Rajaraman and Tan (). Another solution is discussed in the work of Sauer and Roth‐Berghofer (), and it is a case‐based reasoning system, which extracts vocabularies and generate taxonomies from Linked Data sources and from web community data in the form of semistructured texts.…”

Section: Related Workmentioning

confidence: 99%

“…Another solution is discussed in the work of Sauer and Roth-Berghofer (2014), and it is a case-based reasoning system, which extracts vocabularies and generate taxonomies from Linked Data sources and from web community data in the form of semistructured texts.…”

Section: Related Workmentioning

confidence: 99%

A case study of duplications detection for educational domain thorough ad hoc search and identification NLP‐based method

et al. 2017

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During the organization and planning of lecture courses for a discipline, its content may be overlapped and partially delivered in more than one course. Sometimes this action causes time loss through unnecessary repeating. This paper introduces an automated tool for duplications detections adapting methods of natural language processing used for Web search. The experiment for unstructured electronic document repositories clustering for thematic duplicate identification in different documents in the case of educational domain is presented. A prototype of this Web service‐based software search engine is being designed and discussed. The experiment aimed to identify thematic duplicates of various courses within one of the teaching disciplines is also presented.

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“…inconsistent scheduling due to short-term changes in the planning sequences), based on domain-specific similarity measures similar cases to the new problem situation are identified and retrieved from the case base (retrieval phase). The solution knowledge contained in the retrieved case is used as a first proposal for solving the new problem situation (Sauer and Roth-Berghofer, 2014). If the new problem and its specific…”

Section: Introductionmentioning

confidence: 99%

Case-based reasoning for complexity management in Industry 4.0

Schott

Lederer

Eigner

et al. 2020

JMTM

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PurposeIncreasingly, dynamic market environments lead to growing complexity in manufacturing and pose a severe threat for the competitiveness of manufacturing companies. Systematic guidance to manage this complexity, especially in the context of Industry 4.0 and the therewith rising trends such as digitalization and data-driven production optimization, is lacking. To address this deficit a case-based reasoning (CBR) system for providing knowledge about managing complexity in Industry 4.0 is presented.Design/methodology/approachFirst, the explicit knowledge representation for managing complexity in IT-based manufacturing is introduced. Second, the CBR process step to retrieve knowledge from an artificially composed case base with in total 70 cases of data-based complexity management in the context of Industry 4.0 is set out. Third, knowledge transfer alongside several maturity levels of information technology capabilities of manufacturing systems for reuse in new problem scenarios is introduced.FindingsThe paper comprises the conceptual approach for designing a CBR system to support data-based complexity management in manufacturing systems. Furthermore, the appropriateness of the CBR system to provide applicable knowledge for reducing and managing complexity in corporate practice is shown.Research limitations/implicationsThe presented research results are evaluated in the course of an embedded single case study and may therefore lack generalizability. Future research to test and enhance the appropriateness of the developed CBR system will strengthen the research contribution.Originality/valueThe paper provides a novel approach to systematically support knowledge transfer for data-based complexity management by transferring the well-known and established methodology of CBR to the rising application domain of manufacturing systems in the context of Industry 4.0.

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Extracting knowledge from web communities and linked data for case‐based reasoning systems

Cited by 6 publications

References 16 publications

A knowledge construction methodology to automate case‐based learning using clinical documents

A knowledge construction methodology to automate case‐based learning using clinical documents

A case study of duplications detection for educational domain thorough ad hoc search and identification NLP‐based method

Case-based reasoning for complexity management in Industry 4.0

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