Automatic Extraction of Semantic Relations from Text Documents

Ta, Chien D.C.; Thi, Tuoi Phan

doi:10.1007/978-3-319-48057-2_24

Cited by 2 publications

(5 citation statements)

References 6 publications

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“…The module to create knowledge in this study is based on Ta and Thi [13], which is obtaining knowledge from documents [16], and this work extends the requirement module from normal text document to software text requirement. Figure 1 below shows a framework that has several modules.…”

Section: Methodsmentioning

confidence: 99%

“…To extract semantic relation from text documents, Ta and Thi [13] integrated both statistical methods and natural language processing. Ta and Thi [13] only used ACM Digital Library text documents, and these documents would classify automatically when applying stated methods. Ta and Thi [13] stated that this approach is separated into two core modules.…”

Section: B Related Workmentioning

confidence: 99%

“…Ta and Thi [13] only used ACM Digital Library text documents, and these documents would classify automatically when applying stated methods. Ta and Thi [13] stated that this approach is separated into two core modules. The first module is Computing Domain Ontology (CDO), which undergoes the statistical method.…”

Section: B Related Workmentioning

confidence: 99%

“…Meanwhile, Wordnet and other dictionary resources were used to classify the semantic relations among the instances in another module. According to Ta and Thi [13], the documents used in this study are based on the computing domain [13]. Both modules will produce ontology that shows instances with their respective semantic relations from text documents.…”

Section: B Related Workmentioning

confidence: 99%

“…Both modules will produce ontology that shows instances with their respective semantic relations from text documents. Nevertheless, as stated by Ta and Thi [13], not all information can be extracted by the modules.…”

Section: B Related Workmentioning

confidence: 99%

See 4 more Smart Citations

Knowledge Representation Framework for Software Requirement Specification

Jelai

Mit

Juan

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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The need to extract correct information has become one of the main issues when analyzing the software requirement specification (SRS) documentation. The amount of gathered knowledge depends on the size of the information. However, the complexity of software systems is continuously increasing. As software systems change to more complicated systems, the information from the SRS documents may not be easily comprehended. For example, each annotation requirements tasks target the different types of information, and these tasks require the availability of experts specialized in the field. Large scale annotation tasks require multiple experts and very costly. If the number of experts is limited, annotation tasks may overwhelm the experts. The organization would not complete their objectives if they failed to manage their data because poor knowledge management affects many operations within the organization. To extract such vast information and turn it to useful knowledge, a company needs top quality software. This technology should able to input, store, and access systematically. This paper will discuss a framework based on the knowledgebased method, an attempt to improve knowledge representation. In this approach, WordNet 2.1 would be used as the knowledge source used to identify concepts represented by each word in a text from the SRS document.

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Section: Methodsmentioning

confidence: 99%