Data Driven Domain Appraisal: Extracting Information from Short Dense Texts

Liu，Jian; Zeng，Xiangdong; Ghandar，Adam; Theodoropoulos，Georgios

10.1109/SSCI44817.2019.9002904

2019-12-01

978-1-7281-2486-5

2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019

2489-2496

6-9 Dec. 2019

Xiamen, China

345 E 47TH ST, NEW YORK, NY 10017 USA

Institute of Electrical and Electronics Engineers Inc.

Domain names can be traded via online market places and auctions. Speculation can be lucrative, with high value transactions reaching into tens of millions of dollars. This paper proposes a framework for automated domain name appraisal and evaluates several formulations of the problem with real world data. A dynamic nonlinear valuation modelling process is defined using machine learning techniques. Attributes or value factors are derived from short domain name text strings as well as a variety of other contextual data able to be obtained online from open sources. A data set of 9.975 million domains is used for evaluation and results show that search engine query data is a primary driver of value but using extracted text features can facilitate higher performance in distinguishing high value domains particularly when used with ensemble learning.

big data DNS natural language processing valuation

第一

英语

EI ; CPCI-S

Computer Science

Computer Science, Artificial Intelligence

WOS:000555467202081

20201108276761

Artificial intelligence ; Big data ; Data mining ; Learning algorithms ; Learning systems ; Natural language processing systems ; Resource valuation

Computer Software, Data Handling and Applications:723

2-s2.0-85080963807

Scopus

Southern University of Science and Technology (SUSTech),Department of Computer Science and Engineering,Shenzhen,China

Liu，Jian,Zeng，Xiangdong,Ghandar，Adam,et al. Data Driven Domain Appraisal: Extracting Information from Short Dense Texts[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:Institute of Electrical and Electronics Engineers Inc.,2019:2489-2496.