Outfit Helper: A Dialogue-Based System for Solving the Problem of Outfit Matching

Zhang, Boyu; Wang, Qin

doi:10.4236/jcc.2019.712006

Cited by 3 publications

(1 citation statement)

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“…Text extraction can be carried out using numerous techniques and tools (13) ; one of them is the Natural Language Processing (NLP) technique. NLP enables computers to process and analyse massive volumes of natural language data to achieve human-like language processing for various tasks and applications (14) , (15) .…”

Section: Introductionmentioning

confidence: 99%

Identifying Terms to Represent Concepts of a Work Process Ontology

Osman,

Noah,

Saad

2024

IJST

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Objectives: To identify ontology concepts from text documents for the construction of work process ontology. Methods: This study proposes a methodology to identify terms representing a work process ontology concept from a document. The methodology encompasses several key steps: document collecting, text pre-processing, term weighting and analysis, terms mapping, and domain expert relevance judgments. A comparison between the results of three different term weighting schemes, namely the Term Frequency (TF), Term Frequency-Inverse Document Frequency (TFIDF), and Mutual Information (MI) is made with the ontology concept that the domain expert has judged. Findings: The approaches adopted in this study have managed to extract ontological concepts from the targeted domain knowledge source. The findings of the comparison analysis suggest that the TFIDF term weighting scheme exhibits better results compared to the TF and MI weighting schemes. Novelty: A work process ontology is a structured knowledge describing daily operations in the government sector. However, there has been little to no effort in building the work process ontology. This study presents an integrated approach for identifying ontology concepts from documents within the domain of the work process. To the utmost extent of our understanding, this research initiative is the initial attempt to introduce a structured methodology for the semi-automatic extraction and evaluation of concepts and relationships within this domain. The findings can be utilised as a foundation for developing an ontology in the specific field. Keywords: Ontology, Work process, Text extraction, Natural language processing, Term weighting

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Section: Introductionmentioning

confidence: 99%