Event.Locky: System of Event-Data Extraction from Webpages based on Web Mining

• Item type: Journal(1)
• 公開日: 2017-04-15
• タイトル: Event.Locky: System of Event-Data Extraction from Webpages based on Web Mining
• タイトル / 言語: en
• タイトル: Event.Locky: System of Event-Data Extraction from Webpages based on Web Mining
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [一般論文] event data, web mining, text classification, spatial-temporal visualization
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Graduate School of Engineering, Nagoya University
• 著者所属: Institute of Innovation for Future Society, Nagoya University
• 著者所属: Faculty of Information Science, Aichi Institute of Technology
• 著者所属: Graduate School of Engineering, Nagoya University
• 著者所属: Graduate School of Engineering, Nagoya University／Institute of Innovation for Future Society, Nagoya University
• 著者所属(英): en
• 著者所属(英): Graduate School of Engineering, Nagoya University
• 著者所属(英): en
• 著者所属(英): Institute of Innovation for Future Society, Nagoya University
• 著者所属(英): en
• 著者所属(英): Faculty of Information Science, Aichi Institute of Technology
• 著者所属(英): en
• 著者所属(英): Graduate School of Engineering, Nagoya University
• 著者所属(英): en
• 著者所属(英): Graduate School of Engineering, Nagoya University / Institute of Innovation for Future Society, Nagoya University
• 著者名: Chenyi, Liao; Kei, Hiroi; Katsuhiko, Kaji; Ken, Sakurada; Nobuo, Kawaguchi
• 著者名(英): Chenyi, Liao; Kei, Hiroi; Katsuhiko, Kaji; Ken, Sakurada; Nobuo, Kawaguchi
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Nearby event data, such as those for exhibitions and sales promotions, may help users spend their free time more efficiently. However, most event data are hidden in millions of webpages, which is very time-consuming for a user to find such data. To address this issue, we use web mining that extracts event data from webpages. In this paper, we propose and discuss the implementation of Event.Locky - a system for extracting event data from webpages in a user-defined area and displaying them to a user in a spatial-temporal structure. Furthermore, we design two core algorithms for event data extraction in Event.Locky: webpage-data-record extraction and event-record classification. The former is used to convert a semi-structural HTML document into processable structured data. The latter filters out non-event data from extracted data records using machine learning. We trained and evaluated Event.Locky with an actual dataset composed by 96 restaurants and shops at Nagoya train station. As a result, our event-classification algorithm achieved an F1 score of 91.61%, an increase of 3.07% from current event-classification algorithms. The combination of our event-classification algorithm and our data-record-extraction algorithm achieved F1 score 83.96% to extract event records from webpages. That increased 1.6% from current algorithm. Finally, we discuss the feasibility of Event.Locky in an actual online environment through the implementation of a demonstration application.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.25(2017) (online); DOI http://dx.doi.org/10.2197/ipsjjip.25.321; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Nearby event data, such as those for exhibitions and sales promotions, may help users spend their free time more efficiently. However, most event data are hidden in millions of webpages, which is very time-consuming for a user to find such data. To address this issue, we use web mining that extracts event data from webpages. In this paper, we propose and discuss the implementation of Event.Locky - a system for extracting event data from webpages in a user-defined area and displaying them to a user in a spatial-temporal structure. Furthermore, we design two core algorithms for event data extraction in Event.Locky: webpage-data-record extraction and event-record classification. The former is used to convert a semi-structural HTML document into processable structured data. The latter filters out non-event data from extracted data records using machine learning. We trained and evaluated Event.Locky with an actual dataset composed by 96 restaurants and shops at Nagoya train station. As a result, our event-classification algorithm achieved an F1 score of 91.61%, an increase of 3.07% from current event-classification algorithms. The combination of our event-classification algorithm and our data-record-extraction algorithm achieved F1 score 83.96% to extract event records from webpages. That increased 1.6% from current algorithm. Finally, we discuss the feasibility of Event.Locky in an actual online environment through the implementation of a demonstration application.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.25(2017) (online); DOI http://dx.doi.org/10.2197/ipsjjip.25.321; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 58, 号 4, 発行日 2017-04-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764