Profile Reconciliation Through Dynamic Activities Across Social Networks

Multi-Source Spatial Entity Linkage

“…In this section, we keep our QuadSky steps but replace the labeling of the pairs with a supervised learning technique. We decided to compare the SkyEx-* family of algorithms with logistic regression [43], support vector machines (SVM) [44], decision trees [45], and Naive Bayes [46], which are supervised learning techniques commonly used in entity resolution problems [8,10,25,33,47]. We applied these methods on D full pairs that are at most 30 meters apart (dataset description in Table 3).…”

“…What is more, the methods propose arbitrarily attribute weights and score functions without experimentation nor evaluation. In contrast to [11][12][13], the skyline-based algorithm (SkyEx) proposed in [10] is free of scoring functions and semi-arbitrary weights, and achieves good results. However, SkyEx is dependent on a threshold number of skylines k, which can only be discovered through experiments, as the authors do not provide methods for estimating k. To sum up, on the one hand, there is a growing amount of information about spatial entities, both within a single source and across sources, which can improve the quality of the geo-information; on the other hand, the spatial entity linkage problem is hard to resolve not only because of the heterogeneity of the data but also because of the lack of appropriate and effective methods.…”

Multi-Source Spatial Entity Extraction and Linkage

“…Similar synonyms describing the same problem have continuously appeared in the literature such as deduplication, entity resolution, entity matching, record linkage [63,65]. The entities that are matched can be of various fields, for example, profiles in social networks belonging to the same individual [56,66], bioinformatics data [67], biomedical data [68], publication data of the same author [65,69], genealogical data to find the human entities [70], records of the same product [65,69], etc. Regardless the field, the entity linkage follows, in principle, three main steps: blocking, entity comparison, and pair labeling [54,71] (Fig.…”

“…The user-based crawling navigates the geo-social data source using users as query parameters. The most popular method mentioned in several papers is Snowball [15,21,56]. Snowball starts with some initial users, known as the seed.…”

“…The spatial entities have attributes such as name, categories, etc. For measuring these similarities, the traditional similarity metrics such as Levenshtein, Jaccard, Cosine [56,80,83,91] show good results. However, more advanced metrics can better capture the similarity of two entities such as a Soft-TFIDF with Levenshtein [92], and traditional string similarities trained further with supervised machine learning [93].…”