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Concept Discovery through Information Extraction in Restaurant Domain

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

Standard

Concept Discovery through Information Extraction in Restaurant Domain. / Pathirana, Nadeesha; Seneviratne, Sandaru; Samarawickrama, Rangika et al.
In: Computación y Sistemas, Vol. 23, No. 3, 07.10.2019.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Pathirana, N, Seneviratne, S, Samarawickrama, R, Wolff, S, Chitraranjan, C, Thayasivam, U & Ranasinghe, T 2019, 'Concept Discovery through Information Extraction in Restaurant Domain', Computación y Sistemas, vol. 23, no. 3. https://doi.org/10.13053/cys-23-3-3277

APA

Pathirana, N., Seneviratne, S., Samarawickrama, R., Wolff, S., Chitraranjan, C., Thayasivam, U., & Ranasinghe, T. (2019). Concept Discovery through Information Extraction in Restaurant Domain. Computación y Sistemas, 23(3). https://doi.org/10.13053/cys-23-3-3277

Vancouver

Pathirana N, Seneviratne S, Samarawickrama R, Wolff S, Chitraranjan C, Thayasivam U et al. Concept Discovery through Information Extraction in Restaurant Domain. Computación y Sistemas. 2019 Oct 7;23(3). doi: 10.13053/cys-23-3-3277

Author

Pathirana, Nadeesha ; Seneviratne, Sandaru ; Samarawickrama, Rangika et al. / Concept Discovery through Information Extraction in Restaurant Domain. In: Computación y Sistemas. 2019 ; Vol. 23, No. 3.

Bibtex

@article{ddb5cac3a43b4c2fb7fcfdaaeefea6d3,
title = "Concept Discovery through Information Extraction in Restaurant Domain",
abstract = "Concept identification is a crucial step in understanding and building a knowledge base for any particular domain. However, it is not a simple task in very large domains such as restaurants and hotel. In this paper, a novel approach of identifying a concept hierarchy and classifying unseen words into identified concepts related to restaurant domain is presented. Sorting, identifying, classifying of domain-related words manually is tedious and therefore, the proposed process is automated to a great extent. Word embedding, hierarchical clustering, classification algorithms are effectively used to obtain concepts related to the restaurant domain. Further, this approach can also be extended to create a semi-automatic ontology on restaurant domain.",
author = "Nadeesha Pathirana and Sandaru Seneviratne and Rangika Samarawickrama and Shane Wolff and Charith Chitraranjan and Uthayasanker Thayasivam and Tharindu Ranasinghe",
year = "2019",
month = oct,
day = "7",
doi = "10.13053/cys-23-3-3277",
language = "Undefined/Unknown",
volume = "23",
journal = "Computaci{\'o}n y Sistemas",
number = "3",

}

RIS

TY - JOUR

T1 - Concept Discovery through Information Extraction in Restaurant Domain

AU - Pathirana, Nadeesha

AU - Seneviratne, Sandaru

AU - Samarawickrama, Rangika

AU - Wolff, Shane

AU - Chitraranjan, Charith

AU - Thayasivam, Uthayasanker

AU - Ranasinghe, Tharindu

PY - 2019/10/7

Y1 - 2019/10/7

N2 - Concept identification is a crucial step in understanding and building a knowledge base for any particular domain. However, it is not a simple task in very large domains such as restaurants and hotel. In this paper, a novel approach of identifying a concept hierarchy and classifying unseen words into identified concepts related to restaurant domain is presented. Sorting, identifying, classifying of domain-related words manually is tedious and therefore, the proposed process is automated to a great extent. Word embedding, hierarchical clustering, classification algorithms are effectively used to obtain concepts related to the restaurant domain. Further, this approach can also be extended to create a semi-automatic ontology on restaurant domain.

AB - Concept identification is a crucial step in understanding and building a knowledge base for any particular domain. However, it is not a simple task in very large domains such as restaurants and hotel. In this paper, a novel approach of identifying a concept hierarchy and classifying unseen words into identified concepts related to restaurant domain is presented. Sorting, identifying, classifying of domain-related words manually is tedious and therefore, the proposed process is automated to a great extent. Word embedding, hierarchical clustering, classification algorithms are effectively used to obtain concepts related to the restaurant domain. Further, this approach can also be extended to create a semi-automatic ontology on restaurant domain.

U2 - 10.13053/cys-23-3-3277

DO - 10.13053/cys-23-3-3277

M3 - Journal article

VL - 23

JO - Computación y Sistemas

JF - Computación y Sistemas

IS - 3

ER -