Multi-Source Entity Resolution for Genealogical Data

Efremova, Julia; Ranjbar-Sahraei, Bijan; Rahmani, Hossein; Oliehoek, Frans A.; Calders, Toon; Tuyls, Karl; Weiß, Gerhard

doi:10.1007/978-3-319-19884-2_7

Cited by 18 publications

(15 citation statements)

References 29 publications

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“…The entity resolution problem has been referred in the literature with multiple terms including deduplication, entity linkage, and entity matching. Entity resolution has been used in various fields such as matching profiles in social networks [2], bioinformatics data [3], biomedical data [41], publication data [5,6], genealogical data [4], product data [5,6], etc. The attributes of the entities are compared, and a similarity value is assigned.…”

Section: Related Workmentioning

confidence: 99%

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Multi-Source Spatial Entity Linkage

Isaj

Zimányi

Pedersen

2019

Proceedings of the 16th International Symposium on Spatial and Temporal Databases

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Besides the traditional cartographic data sources, spatial information can also be derived from location-based sources. However, even though different location-based sources refer to the same physical world, each one has only partial coverage of the spatial entities, describe them with different attributes, and sometimes provide contradicting information. Hence, we introduce the spatial entity linkage problem, which finds which pairs of spatial entities belong to the same physical spatial entity. Our proposed solution (QuadSky) starts with a spatial blocking technique (QuadFlex) that creates blocks of nearby spatial entities with the time complexity of the quadtree algorithm. After pairwise comparing the spatial entities in the same block, we propose the SkyRank algorithm that ranks the compared pairs using Pareto optimality. We introduce the SkyEx-* family of algorithms that can classify the pairs with 0.85 precision and 0.85 recall for a manually labeled dataset of 1,500 pairs and 0.87 precision and 0.6 recall for a semi-manually labeled dataset of 777,452 pairs. Moreover, our fully unsupervised algorithm SkyEx-D approximates the optimal result with an F-measure loss of just 0.01. Finally, QuadSky provides the best trade-off between precision and recall and the best F-measure compared to the existing baselines.

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Section: Related Workmentioning

confidence: 99%

“…We will add two points in child[0], one coming from child [1] and one from child [3]. Similarly, child [1] and child [3] will receive a point from child [4]. Algorithm 1 details the procedure for retrieving the spatial blocks with QuadFlex.…”

Section: Spatial Blockingmentioning

confidence: 99%

Multi-Source Spatial Entity Linkage

Isaj

Zimányi

Pedersen

2019

Proceedings of the 16th International Symposium on Spatial and Temporal Databases

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“…Efremova et al [11] consider the problem of linking records from multiple genealogical datasets. They cast the linking problem into supervised binary classification tasks, similar to this work, and find name popularity, geographical distance, and co-reference information to be important features.…”

Section: Related Workmentioning

confidence: 99%

Computationally Inferred Genealogical Networks Uncover Long-Term Trends in Assortative Mating

Malmi

Gionis

Solin

2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

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Genealogical networks, also known as family trees or population pedigrees, are commonly studied by genealogists wanting to know about their ancestry, but they also provide a valuable resource for disciplines such as digital demography, genetics, and computational social science. These networks are typically constructed by hand through a very time-consuming process, which requires comparing large numbers of historical records manually. We develop computational methods for automatically inferring large-scale genealogical networks. A comparison with human-constructed networks attests to the accuracy of the proposed methods. To demonstrate the applicability of the inferred large-scale genealogical networks, we present a longitudinal analysis on the mating patterns observed in a network. This analysis shows a consistent tendency of people choosing a spouse with a similar socioeconomic status, a phenomenon known as assortative mating. Interestingly, we do not observe this tendency to consistently decrease (nor increase) over our study period of 150 years.

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“…Record linkage for genealogical data has been previously studied, e.g., by Efremova et al [4] and by Christen et al [3]. Alternative family tree visualization approaches have been proposed by McGuffin and Balakrishnan [9] and by Bezerianos et al [1].…”

Section: Related Workmentioning

confidence: 99%

AncestryAI

Malmi

Rasa²,

Gionis

2017

Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion

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Many people are excited to discover their ancestors and thus decide to take up genealogy. However, the process of finding the ancestors is often very laborious since it involves comparing a large number of historical birth records and trying to manually match the people mentioned in them. We have developed AncestryAI, an open-source tool for automatically linking historical records and exploring the resulting family trees. We introduce a record-linkage method for computing the probabilities of the candidate matches, which allows the users to either directly identify the next ancestor or narrow down the search. We also propose an efficient layout algorithm for drawing and navigating genealogical graphs. The tool is additionally used to crowdsource training and evaluation data so as to improve the matching algorithm. Our objective is to build a large genealogical graph, which could be used to resolve various interesting questions in the areas of computational social science, genetics, and evolutionary studies.

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Multi-Source Entity Resolution for Genealogical Data

Cited by 18 publications

References 29 publications

Multi-Source Spatial Entity Linkage

Multi-Source Spatial Entity Linkage

Computationally Inferred Genealogical Networks Uncover Long-Term Trends in Assortative Mating

AncestryAI

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