eprintid: 2939 rev_number: 9 eprint_status: archive userid: 307 dir: disk0/00/00/29/39 datestamp: 2018-04-07 01:08:38 lastmod: 2018-04-07 01:08:38 status_changed: 2018-04-07 01:08:38 type: book_section metadata_visibility: no_search creators_name: Vu, Ngoc Trinh creators_name: Nguyen, Quoc Dat creators_name: Nguyen, Tien Dat creators_name: Nguyen, Manh Cuong creators_name: Vu, Van Vuong creators_name: Ha, Quang Thuy creators_id: thuyhq@vnu.edu.vn title: A Positive-Unlabeled Learning Model for Extending a Vietnamese Petroleum Dictionary Based on Vietnamese Wikipedia Data ispublished: pub subjects: IT divisions: fac_fit abstract: This study provides a positive-unlabeled learning model for extending a Vietnamese petroleum dictionary based on Vietnamese Wikipedia data. Machine learning algorithms with positive and unlabeled data together with separated and combined between Google similarity distance and Cosine similarity distance, used in this study. The data sources used to integrate are English - Vietnamese oil and gas dictionary and the Vietnamese Wikipedia. In the results, a novelty way for data integration with higher accuracy by using a combination of algorithms. The first Vietnamese oil and gas ontology was built in Vietnam. This ontology is a useful tool for staff in the oil and gas industry in training, research, search daily. date: 2018-03 date_type: published publisher: Springer Nature 2018 full_text_status: none volume: 10751 pagerange: 190-199 refereed: TRUE book_title: ACIIDS 2018: Intelligent Information and Database Systems citation: Vu, Ngoc Trinh and Nguyen, Quoc Dat and Nguyen, Tien Dat and Nguyen, Manh Cuong and Vu, Van Vuong and Ha, Quang Thuy (2018) A Positive-Unlabeled Learning Model for Extending a Vietnamese Petroleum Dictionary Based on Vietnamese Wikipedia Data. In: ACIIDS 2018: Intelligent Information and Database Systems. Springer Nature 2018, pp. 190-199.