{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:27:51Z","timestamp":1750220871632,"version":"3.41.0"},"reference-count":21,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T00:00:00Z","timestamp":1582156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2020,5,31]]},"abstract":"<jats:p>\n            Today, the knowledge base question answering (KB-QA) system is promising to achieve a large-scale high-quality reply in the e-commerce industry. However, there exist two major challenges to efficiently support large-scale KB-QA systems. On the one hand, it is difficult to serve tens of thousands of online stores (i.e., constrained by the tuning and deployment time), and it would perform poorly if the systems start without a sufficient number of chat records. On the other hand, current KB-QA systems cannot be updated in an efficient way due to the high cost of knowledge base (KB) updating. In this article, we propose an automatic learning scheme for KB-QA systems, called\n            <jats:italic>ALKB-QA<\/jats:italic>\n            , using a vector modeling method to address the preceding two main challenges. The ALKB-QA system provides online stores with basic KB templates that are suitable for many common occasions, and this feature enables the ability to deploy chatbots for a large number of online stores in a short time. Then, the KBs are further updated automatically to adapt to their own businesses (meet different specific needs), leading to increased reply accuracy. Our work has three main contributions. First, the proposed ALKB-QA system has a good business model in the e-commerce industry (serving tens of thousands of online stores with low cost), breaking the scalability limitations of existing KB-QA systems. Second, we assess the reply accuracy of the proposed ALKB-QA system using human evaluations, and the results show that it outperforms human annotation-base approaches. Third, we launched our ALKB-QA system as a real-world business application, and it supports tens of thousands of online stores.\n          <\/jats:p>","DOI":"10.1145\/3377708","type":"journal-article","created":{"date-parts":[[2020,2,25]],"date-time":"2020-02-25T08:41:15Z","timestamp":1582620075000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Dynamic Updating of the Knowledge Base for a Large-Scale Question Answering System"],"prefix":"10.1145","volume":"19","author":[{"given":"Xiao-Yang","family":"Liu","sequence":"first","affiliation":[{"name":"Columbia Universityt, New York, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yimeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Columbia University, New York, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yukang","family":"Liao","sequence":"additional","affiliation":[{"name":"XiaoduoAI Co., Ltd., Chengdu, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ling","family":"Jiang","sequence":"additional","affiliation":[{"name":"XiaoduoAI Co., Ltd., Chengdu, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,2,20]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1181"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1067"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-44848-9_11"},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (Volume 1). 1148--1158","author":"Ji Heng","year":"2011","unstructured":"Heng Ji and Ralph Grishman . 2011 . Knowledge base population: Successful approaches and challenges . In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (Volume 1). 1148--1158 . Heng Ji and Ralph Grishman. 2011. Knowledge base population: Successful approaches and challenges. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (Volume 1). 1148--1158."},{"key":"e_1_2_1_5_1","volume-title":"Improved discourse parsing with two-step neural transition-based model. ACM Transactions on Asian and Low-Resour.ce Language Information Processing 17, 2 (Jan","author":"Jia Yanyan","year":"2018","unstructured":"Yanyan Jia , Yansong Feng , Yuan Ye , Chao Lv , Chongde Shi , and Dongyan Zhao . 2018. Improved discourse parsing with two-step neural transition-based model. ACM Transactions on Asian and Low-Resour.ce Language Information Processing 17, 2 (Jan . 2018 ), Article 11, 21 pages. DOI:https:\/\/doi.org\/10.1145\/3152537 10.1145\/3152537 Yanyan Jia, Yansong Feng, Yuan Ye, Chao Lv, Chongde Shi, and Dongyan Zhao. 2018. Improved discourse parsing with two-step neural transition-based model. ACM Transactions on Asian and Low-Resour.ce Language Information Processing 17, 2 (Jan. 2018), Article 11, 21 pages. DOI:https:\/\/doi.org\/10.1145\/3152537"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v38i2.2715"},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of the NeurIPS Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy.","author":"Li Xinyi","year":"2019","unstructured":"Xinyi Li , Yinchuan Li , Hongyang Yang , Liuqing Yang , and Xiao-Yang Liu . 2019 . DP-LSTM: Differential privacy-inspired LSTM for stock prediction using financial news . In Proceedings of the NeurIPS Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy. Xinyi Li, Yinchuan Li, Hongyang Yang, Liuqing Yang, and Xiao-Yang Liu. 2019. DP-LSTM: Differential privacy-inspired LSTM for stock prediction using financial news. In Proceedings of the NeurIPS Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052675"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1229"},{"key":"e_1_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Bhaskar Mitra Nick Craswell etal 2018. An introduction to neural information retrieval. Foundations and Trends\u00ae in Information Retrieval 13 1 (2018) 1--126.  Bhaskar Mitra Nick Craswell et al. 2018. An introduction to neural information retrieval. Foundations and Trends\u00ae in Information Retrieval 13 1 (2018) 1--126.","DOI":"10.1561\/1500000061"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-2079"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/2832415.2832431"},{"key":"e_1_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Stephen Robertson Hugo Zaragoza etal 2009. The probabilistic relevance framework: BM25 and beyond. Foundations and Trends\u00ae in Information Retrieval 3 4 (2009) 333--389.  Stephen Robertson Hugo Zaragoza et al. 2009. The probabilistic relevance framework: BM25 and beyond. Foundations and Trends\u00ae in Information Retrieval 3 4 (2009) 333--389.","DOI":"10.1561\/1500000019"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1162\/089976604773135104"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767738"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/579"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1071"},{"key":"e_1_2_1_18_1","volume-title":"Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI\u201917)","author":"Yang Shuo","year":"2017","unstructured":"Shuo Yang , Lei Zou , Zhongyuan Wang , Jun Yan , and Ji-Rong Wen . 2017 . Efficiently answering technical questions\u2014A knowledge graph approach . In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI\u201917) . 3111--3118. Shuo Yang, Lei Zou, Zhongyuan Wang, Jun Yan, and Ji-Rong Wen. 2017. Efficiently answering technical questions\u2014A knowledge graph approach. In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI\u201917). 3111--3118."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P14-1090"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1128"},{"volume-title":"Proceedings of the 6th International Conference on Learning Representations. 1211--1220","author":"Yu Adams Wei","key":"e_1_2_1_21_1","unstructured":"Adams Wei Yu , David Dohan , Minh-Thang Luong , Rui Zhao , Kai Chen , Mohammad Norouzi , and Quoc V. Le . 2018. QANet: Combining local convolution with global self-attention for reading comprehension . In Proceedings of the 6th International Conference on Learning Representations. 1211--1220 . Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, and Quoc V. Le. 2018. QANet: Combining local convolution with global self-attention for reading comprehension. 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