Open AccessContent-Based Discovery for Web Map Service using Support Vector Machine and User Relevance Feedback
Author(s) -
Kai Hu,
Zhipeng Gui,
Xiaoqiang Cheng,
Kunlun Qi,
Jie Zheng,
Liangzhi You,
Huayi Wu
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0166098
Subject(s) - computer science , information retrieval , metadata , relevance (law) , relevance feedback , geospatial analysis , service discovery , data mining , support vector machine , service (business) , world wide web , process (computing) , geographic information system , semantic web , web service , data science , image retrieval , machine learning , artificial intelligence , remote sensing , cartography , economy , geology , political science , law , economics , image (mathematics) , operating system , geography
Many discovery methods for geographic information services have been proposed. There are approaches for finding and matching geographic information services, methods for constructing geographic information service classification schemes, and automatic geographic information discovery. Overall, the efficiency of the geographic information discovery keeps improving., There are however, still two problems in Web Map Service (WMS) discovery that must be solved. Mismatches between the graphic contents of a WMS and the semantic descriptions in the metadata make discovery difficult for human users. End-users and computers comprehend WMSs differently creating semantic gaps in human-computer interactions. To address these problems, we propose an improved query process for WMSs based on the graphic contents of WMS layers, combining Support Vector Machine (SVM) and user relevance feedback. Our experiments demonstrate that the proposed method can improve the accuracy and efficiency of WMS discovery.