Entity resolution in geospatial data integration

Sehgal, Vivek Kumar; Getoor, Lise; Viechnicki, Peter D

doi:10.1145/1183471.1183486

Cited by 85 publications

(90 citation statements)

References 9 publications

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“…In geospatial database research, duplicate detection is seen as a part of information integration combining multiple heterogeneous sources [40]. Among existing approaches (e. g., [41], [42], and [43]) we evaluate the work of Bakillah et al [43] due to their notion of spatial evolution taking into account changes in extent. The proposed method handles fuzzy information with similarity measures for spatial on-line analytical processing cubes (spatial OLAP-SOLAP), being an extension to OLAP cubes used in data warehouses [44].…”

Section: Survey Of Duplicate Detection Methodsmentioning

confidence: 99%

“Same, Same but Different” A Survey on Duplicate Detection Methods for Situation Awareness

Baumgartner¹,

Gottesheim

Mitsch

et al. 2009

On the Move to Meaningful Internet Systems: OTM 2009

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Abstract. Systems supporting situation awareness typically deal with a vast stream of information about a large number of real-world objects anchored in time and space provided by multiple sources. These sources are often characterized by frequent updates, heterogeneous formats and most crucial, identical, incomplete and often even contradictory information. In this respect, duplicate detection methods are of paramount importance allowing to explore whether or not information having, e. g., different origins or different observation times concern one and the same real-world object. Although many such duplicate detection methods have been proposed in literature-each of them having different origins, pursuing different goals and often, by nature, being heavily domain-specific-the unique characteristics of situation awareness and their implications on the method's applicability were not the focus up to now. This paper examines existing duplicate detection methods appearing to be suitable in the area of situation awareness and identifies their strengths and shortcomings. As a prerequisite, based on a motivating case study in the domain of road traffic management, an evaluation framework is suggested, which categorizes the major requirements on duplicate detection methods with regard to situation awareness.

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Section: Survey Of Duplicate Detection Methodsmentioning

confidence: 99%

“Same, Same but Different” A Survey on Duplicate Detection Methods for Situation Awareness

Baumgartner¹,

Gottesheim

Mitsch

et al. 2009

On the Move to Meaningful Internet Systems: OTM 2009

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show abstract

“…Baidu map [30] [32]. Strictly speaking, the category of geospatial data is an ontology question, and using ontologies is a privileged method to achieve interoperability among heterogeneous multi-sources system that with semantic feature [33].…”

Section: Category Similaritymentioning

confidence: 99%

“…The type of POI can also aid us in finding matches to some degree; at least, a POI tagged with different types may have more possibilities to represent different entities [32]. Strictly speaking, the category of geospatial data is an ontology question, and using ontologies is a privileged method to achieve interoperability among heterogeneous multi-sources system that with semantic feature [33].…”

Section: Category Similaritymentioning

confidence: 99%

Entropy-Weighted Instance Matching Between Different Sourcing Points of Interest

Xing

Xia

et al. 2016

Entropy

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Abstract:The crucial problem for integrating geospatial data is finding the corresponding objects (the counterpart) from different sources. Most current studies focus on object matching with individual attributes such as spatial, name, or other attributes, which avoids the difficulty of integrating those attributes, but at the cost of an ineffective matching. In this study, we propose an approach for matching instances by integrating heterogeneous attributes with the allocation of suitable attribute weights via information entropy. First, a normalized similarity formula is developed, which can simplify the calculation of spatial attribute similarity. Second, sound-based and word segmentation-based methods are adopted to eliminate the semantic ambiguity when there is a lack of a normative coding standard in geospatial data to express the name attribute. Third, category mapping is established to address the heterogeneity among different classifications. Finally, to address the non-linear characteristic of attribute similarity, the weights of the attributes are calculated by the entropy of the attributes. Experiments demonstrate that the Entropy-Weighted Approach (EWA) has good performance both in terms of precision and recall for instance matching from different data sets.

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“…Adams et al [2010] proposed a general framework for conflation that combines geometric and other attributes. Likewise, work by Sehgal et al [2006] integrated spatial and non-spatial components (names, types and demographic information) of geospatial locations with the goal of consolidating a collection of true locations. Recent work on online social networking applications has proposed matching Qype and Facebook Places to OpenStreetMap POI through geographic distance and name matching [Scheffler et al, 2012].…”

Section: Related Workmentioning

confidence: 99%

A weighted multi-attribute method for matching user-generated Points of Interest

McKenzie

Janowicz

Adams

2014

Cartography and Geographic Information Science

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To a large degree, the attraction of Big Data lies in the variety of its heterogeneous multi-thematic and multidimensional data sources and not merely its volume. To fully exploit this variety, however, requires conflation. This is a two step process. First, one has to establish identity relations between information entities across the different data sources; and second, attribute values have to be merged according to certain procedures which avoid logical contradictions. The first step, also called matching, can be thought of as a weighted combination of common attributes according to some similarity measures. In this work, we propose such a matching based on multiple attributes of Points of Interests (POI) from the Location-based Social Network Foursquare and the Yelp local directory service. While both contain overlapping attributes that can be use for matching, they have specific strengths and weaknesses which makes their conflation desirable. For instance, Foursquare offers information about user check-ins to places, while Yelp specializes in user-contributed reviews. We present a weighted multi-attribute matching strategy, evaluate its performance, and discuss application areas that benefit from a successful matching. Finally, we also outline how the established POI matches can be stored as Linked Data on the Semantic Web. Our strategy can automatically match 97% of randomly selected Yelp POI to their corresponding Foursquare entities.

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Entity resolution in geospatial data integration

Cited by 85 publications

References 9 publications

“Same, Same but Different” A Survey on Duplicate Detection Methods for Situation Awareness

“Same, Same but Different” A Survey on Duplicate Detection Methods for Situation Awareness

Entropy-Weighted Instance Matching Between Different Sourcing Points of Interest

A weighted multi-attribute method for matching user-generated Points of Interest

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