Doc2vec-based link prediction approach using SAO structures: application to patent network

Byungun Yoon, Songhee Kim, Sunhye Kim and Hyeonju Seol ()
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Byungun Yoon: Dongguk University
Songhee Kim: Dongguk University
Sunhye Kim: Dongguk University
Hyeonju Seol: Chungnam National University

Scientometrics, 2022, vol. 127, issue 9, No 15, 5385-5414

Abstract: Abstract As the amount of documents has exploded in the Internet era, many researchers have tried to understand the relationships between documents and predict the links between similar but unconnected documents. However, existing link prediction techniques that use the predefined links of documents might provide incorrect results, because of the generic problem of citation analysis. Moreover, they may fail to reflect important contents of documents in the link prediction process. Thus, we propose a new link prediction approach that employs the Doc2vec algorithm, a document-embedding method, in order to predict potential links between documents, by reflecting the functional context of technological words. For this, first, we collected both citation information and documents of patents of interest, and generated a patent network by using the citation relationship between patents. Second, we identified unconnected links between nodes and transformed the patent document into document vectors, based on the Doc2vec algorithm. In particular, since patent documents include useful functions for solving technological problems, the proposed approach extracts subject-action-object (SAO) structures that we used to generate document vectors. Then, we calculated the similarity between patents in the unconnected links of a patent network, and could predict potential links by using the similarity. Third, we validated the results of the proposed approach by comparing them using the Adamic–Adar technique, one of the traditional link prediction techniques, and word vector-based link prediction. We applied the Doc2vec-based link prediction approach to a real case, the unmanned aerial vehicle (UAV) technology field. We found that the proposed approach makes better predictions performance than the Adamic–Adar technique and the word vector approach. Our results can help analyzers accurately forecast future relationships between nodes in a network, and give R&D managers insightful information on the future direction of technological development by using a patent network.

Keywords: Link prediction; Patent network; Doc2vec; Document embedding; Unmanned aerial vehicle (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11192-021-04187-4

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