A holistic approach to aligning geospatial data with multidimensional similarity measuring
Semantically aligning the heterogeneous geospatial datasets (GDs) produced by different organizations demands efficient similarity matching methods. However, the strategies employed to align the schema (concept and property) and instances are usually not reusable, and the effects of unbalanced infor...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2018-08-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17538947.2017.1359688 |
| _version_ | 1827811180407685120 |
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| author | Li Yu Peiyuan Qiu Xiliang Liu Feng Lu Bo Wan |
| author_facet | Li Yu Peiyuan Qiu Xiliang Liu Feng Lu Bo Wan |
| author_sort | Li Yu |
| collection | DOAJ |
| description | Semantically aligning the heterogeneous geospatial datasets (GDs) produced by different organizations demands efficient similarity matching methods. However, the strategies employed to align the schema (concept and property) and instances are usually not reusable, and the effects of unbalanced information tend to be neglected in GD alignment. To solve this problem, a holistic approach is presented in this paper to integrally align the geospatial entities (concepts, properties and instances) simultaneously. Spatial, lexical, structural and extensional similarity metrics are designed and automatically aggregated by means of approval voting. The presented approach is validated with real geographical semantic webs, Geonames and OpenStreetMap. Compared with the well-known extensional-based aligning system, the presented approach not only considers more information involved in GD alignment, but also avoids the artificial parameter setting in metric aggregation. It reduces the dependency on specific information, and makes the alignment more robust under the unbalanced distribution of various information. |
| first_indexed | 2024-03-11T23:01:32Z |
| format | Article |
| id | doaj.art-5c7a3f26d7f040a3bb77c1217e6f5fbc |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T23:01:32Z |
| publishDate | 2018-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-5c7a3f26d7f040a3bb77c1217e6f5fbc2023-09-21T14:38:06ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552018-08-0111884586210.1080/17538947.2017.13596881359688A holistic approach to aligning geospatial data with multidimensional similarity measuringLi Yu0Peiyuan Qiu1Xiliang Liu2Feng Lu3Bo Wan4Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesChina University of GeosciencesSemantically aligning the heterogeneous geospatial datasets (GDs) produced by different organizations demands efficient similarity matching methods. However, the strategies employed to align the schema (concept and property) and instances are usually not reusable, and the effects of unbalanced information tend to be neglected in GD alignment. To solve this problem, a holistic approach is presented in this paper to integrally align the geospatial entities (concepts, properties and instances) simultaneously. Spatial, lexical, structural and extensional similarity metrics are designed and automatically aggregated by means of approval voting. The presented approach is validated with real geographical semantic webs, Geonames and OpenStreetMap. Compared with the well-known extensional-based aligning system, the presented approach not only considers more information involved in GD alignment, but also avoids the artificial parameter setting in metric aggregation. It reduces the dependency on specific information, and makes the alignment more robust under the unbalanced distribution of various information.http://dx.doi.org/10.1080/17538947.2017.1359688geospatial datadata alignmentsimilarity matchingsemantic web |
| spellingShingle | Li Yu Peiyuan Qiu Xiliang Liu Feng Lu Bo Wan A holistic approach to aligning geospatial data with multidimensional similarity measuring International Journal of Digital Earth geospatial data data alignment similarity matching semantic web |
| title | A holistic approach to aligning geospatial data with multidimensional similarity measuring |
| title_full | A holistic approach to aligning geospatial data with multidimensional similarity measuring |
| title_fullStr | A holistic approach to aligning geospatial data with multidimensional similarity measuring |
| title_full_unstemmed | A holistic approach to aligning geospatial data with multidimensional similarity measuring |
| title_short | A holistic approach to aligning geospatial data with multidimensional similarity measuring |
| title_sort | holistic approach to aligning geospatial data with multidimensional similarity measuring |
| topic | geospatial data data alignment similarity matching semantic web |
| url | http://dx.doi.org/10.1080/17538947.2017.1359688 |
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