TY - GEN
T1 - Topic Crawler for OpenStack QA Knowledge Base
AU - Qiu, Juan
AU - Du, Qingfeng
AU - Wang, Wei
AU - Yin, Kanglin
AU - Lin, Changsheng
AU - Qian, Chongshu
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - With the rapid development of cloud computing, building a knowledge base on a specific cloud platform for developers and operation staff is expected to be of great value and significance. When developers and operation staff encounter problems that cannot be solved, they generally attempt to solve problems through a search engine such as Google. However, sometimes, certain domain-specific problems cannot be effectively resolved by using a generic search engine. Furthermore, the large amount of information obtained from the search engine can have a negative effect on the accuracy of the search results. To solve this problem, a topic crawler is proposed in this paper; this crawler can be used to construct a QA knowledge base for OpenStack, which is a well-known cloud platform. Firstly, a topic clustering algorithm is used to construct an OpenStack topic corpus. A vector space model is used to calculate the topic similarity for the content of the web pages, and a topic feedback model is used to update the topic corpus. Finally, we construct an OpenStack domain-specific knowledge base with the topic crawler.
AB - With the rapid development of cloud computing, building a knowledge base on a specific cloud platform for developers and operation staff is expected to be of great value and significance. When developers and operation staff encounter problems that cannot be solved, they generally attempt to solve problems through a search engine such as Google. However, sometimes, certain domain-specific problems cannot be effectively resolved by using a generic search engine. Furthermore, the large amount of information obtained from the search engine can have a negative effect on the accuracy of the search results. To solve this problem, a topic crawler is proposed in this paper; this crawler can be used to construct a QA knowledge base for OpenStack, which is a well-known cloud platform. Firstly, a topic clustering algorithm is used to construct an OpenStack topic corpus. A vector space model is used to calculate the topic similarity for the content of the web pages, and a topic feedback model is used to update the topic corpus. Finally, we construct an OpenStack domain-specific knowledge base with the topic crawler.
KW - Focused Crawling
KW - OpenStack
KW - QA Knowledge Base
KW - Topic Corpus
KW - Topic Crawler
KW - Vertical Search Engines
KW - Vertical Space Model
UR - https://www.scopus.com/pages/publications/85050103486
U2 - 10.1109/CyberC.2017.80
DO - 10.1109/CyberC.2017.80
M3 - 会议稿件
AN - SCOPUS:85050103486
T3 - Proceedings - 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2017
SP - 309
EP - 317
BT - Proceedings - 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2017
Y2 - 12 October 2017 through 14 October 2017
ER -