U-STRUCT: A Framework for Conversion of Unstructured Text Documents into Structured Form

Jindal, Rajni; Taneja, Shweta

doi:10.1007/978-3-642-36321-4_6

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…In the literature, most systems/methodologies (Friedman et al, 2004;Leao et al, 2013;Rajni & Taneja, 2013) require a knowledge engineer to translate unstructured text into fully structured form and most systems have been developed using NLP techniques and without the support of controlled natural language (Friedman et al, 2004;Houser, 2004;Jindal & Taneja, 2013). Regarding structured knowledge construction, some studies do not support lexical ambiguity (Rajni & Taneja, 2013;Reuss et al, 2015).…”

Section: Domain Knowledge Construction Literaturementioning

confidence: 99%

“…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%

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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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