Using character n-grams to match a list of publications to references in bibliographic databases

Abdulhayoǧlu, Mehmet Ali; Thijs, Bart; Jeuris, Wouter

doi:10.1007/s11192-016-2066-3

Cited by 13 publications

(8 citation statements)

References 23 publications

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“…On the other hand, it is found that BI-DIST is a case of N-DIST (Kondrak, 2005). In Abdulhayoglu, Thijs & Jeuris (2016) each matrix element NDIST s,t i,j is calculated according to Eq. (3), where the cost in Eq.…”

Section: Background and Related Workmentioning

confidence: 99%

“…For the dataset 5 (Portuguese 120 pairs), using different Edit Distance. The best results were retrieved with the threshold values for a correct match of 0.65, 0.70, 0.75, 0.80, 0.85 and 0.90 for LD, DLD, N-DIST, MDLD and Soft-Bidist, respectively (Abdulhayoglu, Thijs & Jeuris, 2016). Table 9 shows F-measure vs.…”

Section: Comparative Study For Soft-bidist Algorithm and Compared Algorithmsmentioning

confidence: 99%

“…Therefore, this paper aims to develop an algorithm for name matching, that consider an approximate string-matching algorithm to allow dealing with possible technical or computational errors. Such matching algorithms have been used in several applications such as spelling correction (Park et al, 2020), linking databases (Hand & Christen, 2018), text retrieval (Abdulhayoglu, Thijs & Jeuris, 2016), handwriting recognition (Chowdhury, Bhattacharya & Parui, 2013), computational biology ''DNA'' (Berger, Waterman & Yu, 2020), and name recognition (Delgado et al, 2016), etc. Consequently, in this work, a new softened distance measure is proposed, based on the BI-DIST distance to increase the efficiency and accuracy of the name-matching method.…”

Section: Introductionmentioning

confidence: 99%

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Soft Bigram distance for names matching

Hadwan

Al-Hagery

Al-Sanabani

et al. 2021

PeerJ Computer Science

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Background Bi-gram distance (BI-DIST) is a recent approach to measure the distance between two strings that have an important role in a wide range of applications in various areas. The importance of BI-DIST is due to its representational and computational efficiency, which has led to extensive research to further enhance its efficiency. However, developing an algorithm that can measure the distance of strings accurately and efficiently has posed a major challenge to many developers. Consequently, this research aims to design an algorithm that can match the names accurately. BI-DIST distance is considered the best orthographic measure for names identification; nevertheless, it lacks a distance scale between the name bigrams. Methods In this research, the Soft Bigram Distance (Soft-Bidist) measure is proposed. It is an extension of BI-DIST by softening the scale of comparison among the name Bigrams for improving the name matching. Different datasets are used to demonstrate the efficiency of the proposed method. Results The results show that Soft-Bidist outperforms the compared algorithms using different name matching datasets.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Comparative Study For Soft-bidist Algorithm and Compared Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Soft Bigram distance for names matching

Hadwan

Al-Hagery

Al-Sanabani

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…On the other hand, it is found that BI-DIST is a case of N-DIST (Kondrak 2005). In (Abdulhayoglu et al 2016) each matrix element is 𝑁𝐷𝐼𝑆𝑇 s,t (i,j) calculated according to Eq. ( 3), where the cost in Eq.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Therefore, this paper aims to develop an algorithm for name matching, that consider an approximate string-matching algorithm to allow dealing with possible technical or computational errors. Such matching algorithms have been used in several applications such as Spelling correction (Park et al 2020), Linking database (Hand and Christen 2018), Text retrieval (Abdulhayoglu, Thijs, and Jeuris 2016), Handwriting recognition (Chowdhury, Bhattacharya, and Parui 2013), Computational biology "DNA" (Berger, Waterman, and Yu 2020), and Name recognition (Delgado et al 2016)… etc. Consequently, in this work, a new softened distance measure is proposed, based on the BI-DIST distance to increase the efficiency and accuracy of the name-matching method.…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "Soft Bigram distance for names matching (v0.1)"

García¹

2021

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Background: Bi-gram distance (BI-DIST) is a recent approach to measure the distance between two strings that have an important role in a wide range of applications in various areas. The importance of BI-DIST is due to its representational and computational efficiency, which has led to extensive research to further enhance its efficiency. However, developing an algorithm that can measure the distance of strings accurately and efficiently has posed a major challenge to many developers. Consequently, this research aims to design an algorithm that can match the names accurately.BI-DIST distance is considered the best orthographic measure for names identification; nevertheless, it lacks a distance scale between the name bigrams. Methods: In this research, the Soft Bigram Distance (Soft-Bidist) measure is proposed. It is an extension of BI-DIST by softening the scale of comparison among the name Bigrams for improving the name matching. Different datasets are used to demonstrate the efficiency of the proposed method. Results: The results show that Soft-Bidist outperforms the compared algorithms using different name matching datasets.

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Use of ResearchGate and Google CSE for author name disambiguation

Abdulhayoǧlu

Thijs

2017

Scientometrics

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Using character n-grams to match a list of publications to references in bibliographic databases

Cited by 13 publications

References 23 publications

Soft Bigram distance for names matching

Soft Bigram distance for names matching

Peer Review #1 of "Soft Bigram distance for names matching (v0.1)"

Use of ResearchGate and Google CSE for author name disambiguation

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