TY - GEN
T1 - A holistic framework of geographical semantic web aligning
AU - Yu, Li
AU - Liu, Xiliang
AU - Li, Mingxiao
AU - Peng, Peng
AU - Lu, Feng
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/10/31
Y1 - 2016/10/31
N2 - Semantic aligning of heterogeneous geographical data from different sources behaves unsatisfactory on Geographical Semantic Web (GSW) due to the flat structure of GSW and the influence of spatial features. To solve this problem, this paper proposes a holistic framework for GSW aligning. This holistic framework firstly produces the initial matched results respectively for classes, properties and instances by the approval voting strategy, and then enhances these results by the mutual cooperating mechanism. Especially, spatial distance and spatial index are introduced to align instances and to improve the performance of aligning class and aligning property. To demonstrate its ability, this holistic framework is tested with two real GSWs. Compared with the state-of-the-art holistic alignment system, namely PARIS, this framework gains a large number of matched pairs. The F1 values of aligning class, aligning property and aligning instance respectively are 0.562, 0.545 and 0.646, all of which are higher than PARIS's.
AB - Semantic aligning of heterogeneous geographical data from different sources behaves unsatisfactory on Geographical Semantic Web (GSW) due to the flat structure of GSW and the influence of spatial features. To solve this problem, this paper proposes a holistic framework for GSW aligning. This holistic framework firstly produces the initial matched results respectively for classes, properties and instances by the approval voting strategy, and then enhances these results by the mutual cooperating mechanism. Especially, spatial distance and spatial index are introduced to align instances and to improve the performance of aligning class and aligning property. To demonstrate its ability, this holistic framework is tested with two real GSWs. Compared with the state-of-the-art holistic alignment system, namely PARIS, this framework gains a large number of matched pairs. The F1 values of aligning class, aligning property and aligning instance respectively are 0.562, 0.545 and 0.646, all of which are higher than PARIS's.
KW - Geographical Semantic Web
KW - Information integration
KW - Ontology mapping
KW - Semantic network alignment
UR - http://www.scopus.com/inward/record.url?scp=85005914670&partnerID=8YFLogxK
U2 - 10.1145/3003464.3003465
DO - 10.1145/3003464.3003465
M3 - Conference contribution
AN - SCOPUS:85005914670
T3 - Proceedings of the 10th Workshop on Geographic Information Retrieval, GIR 2016
BT - Proceedings of the 10th Workshop on Geographic Information Retrieval, GIR 2016
A2 - Purves, Ross S.
A2 - Jones, Christopher B.
PB - Association for Computing Machinery, Inc
T2 - 10th Workshop on Geographic Information Retrieval, GIR 2016
Y2 - 31 October 2016
ER -