A Graph-Based Author Name Disambiguation Method and Analysis via Information Theory

Ma, Yingying; Wu, Youlong; Lu, Chengqiang

doi:10.3390/e22040416

Cited by 10 publications

(7 citation statements)

References 27 publications

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“…Peng et al [16] improved [15], increase the information types, and propose a semi-supervised clustering algorithm. The Graph Auto Encoder, as a classical unsupervised feature extraction algorithm, was also used to extract publication features by researchers [7], and they used the HAC algorithm in clustering. However, it could not mine a deep relationship between publications because the homogeneous graph can not preserve a complex relationship.…”

Section: Related Workmentioning

confidence: 99%

“…The second challenge is determining the number of distinct authors with the same name, that is, to determine the size of clusters. Since common clustering methods that require a specified size of clusters cannot be applied to this task, many methods similar to hierarchical clustering and AP clustering are used [3,7]. However, these clustering algorithms also require specifying the relevant parameters that control the size of clusters.…”

Section: Introductionmentioning

confidence: 99%

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Author Name Disambiguation Based on Heterogeneous Graph

Chuang Ma,

Chuang Ma

2023

Journal of Computers

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<p>Since multiple people share the same name in the real world, this will cause performance degradation to academic search systems and lead to misattribution of publications. The author name disambiguation algorithm has not yet to be well solved. In this paper, we propose a disambiguation method that combines heterogeneous graph-based and improved label propagation, first we construct a publication heterogeneous graph network, then graph neural networks is applied to aggregate the nodes representation and relation types, finally combined with the improved label propagation algorithm to realize clustering. The task of author name disambiguation is completed to improve the retrieval performance. Experimental results on two public datasets show that our method was improved by 2.8% and 4.9% over the suboptimal method, respectively. Our method can effectively reduce the number of publications returning the wrong author and improve the performance of the academic retrieval system.</p> <p> </p>

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Author Name Disambiguation Based on Heterogeneous Graph

Chuang Ma,

Chuang Ma

2023

Journal of Computers

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“…In Shin et al (2014), another framework is constructed based on graph operations such as vertex splitting and vertex merging of co-authorship graphs and shown to mostly outperform three existing unsupervised methods on various standard evaluation metrics. A recent addition to the literature on unsupervised methods, (Ma et al 2020), claims to offer a superior performance than GHOST and other state-of-the-art methods by employing representation learning, evaluated on AMiner, which integrates data from popular databases with several academic bibliographic collections. They use word2vec for document representation, followed by a Graph Auto-Encoder.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, they are applicable to any database and domain, as these attributes are always available, at least to a certain degree of completeness. Almost all approaches, possibly with the notable exception of Ma et al (2020), assume that co-authors are the most important information for author disambiguation. A peculiarity of astronomy in this regard is a generally larger average number of authors per article than in other disciplines, due to the existence of populous collaborations, often comprising hundreds of members.…”

Section: Related Workmentioning

confidence: 99%

Disambiguation of author entities in ADS using supervised learning and graph theory methods

Mihaljević

Santamaría

2021

Scientometrics

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Disambiguation of authors in digital libraries is essential for many tasks, including efficient bibliographical searches and scientometric analyses to the level of individuals. The question of how to link documents written by the same person has been given much attention by academic publishers and information retrieval researchers alike. Usual approaches rely on publications’ metadata such as affiliations, email addresses, co-authors, or scholarly topics. Lack of homogeneity in the structure of bibliographic collections and discipline-specific dissimilarities between them make the creation of general-purpose disambiguators arduous. We present an algorithm to disambiguate authorships in the Astrophysics Data System (ADS) following an established semi-supervised approach of training a classifier on authorship pairs and clustering the resulting graphs. Due to the lack of high-signal features such as email addresses and citations, we engineer additional content- and location-based features via text embeddings and named-entity recognition. We train various nonlinear tree-based classifiers and detect communities from the resulting weighted graphs through label propagation, a fast yet efficient algorithm that requires no tuning. The resulting procedure reaches reasonable complexity and offers possibilities for interpretation. We apply our method to the creation of author entities in a recent ADS snapshot. The algorithm is evaluated on 39 manually-labeled author blocks comprising 9545 authorships from 562 author profiles. Our best approach utilizes the Random Forest classifier and yields a micro- and macro-averaged BCubed $$\mathrm {F}_1$$ F 1 score of 0.95 and 0.87, respectively. We release our code and labeled data publicly to foster the development of further disambiguation procedures for ADS.

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“…Although NDA could be used to assist in NED tasks, NED typically strongly relies on the text, e.g., by characterizing the context in which the named entity occurs (e.g., paper topic) [14]. Similarly, Ma et al [15] proposes a name disambiguation model based on representation learning employing attributes and network connections, by first encoding the attributes of each paper using variational graph auto-encoder, then computing a similarity metric from the relationship of these attributes, and then using graph embedding to leverage the author relationships, heavily relying on NLP.…”

Section: Related Workmentioning

confidence: 99%

FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings

et al. 2021

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Data often have a relational nature that is most easily expressed in a network form, with its main components consisting of nodes that represent real objects and links that signify the relations between these objects. Modeling networks is useful for many purposes, but the efficacy of downstream tasks is often hampered by data quality issues related to their construction. In many constructed networks, ambiguity may arise when a node corresponds to multiple concepts. Similarly, a single entity can be mistakenly represented by several different nodes. In this paper, we formalize both the node disambiguation (NDA) and node deduplication (NDD) tasks to resolve these data quality issues. We then introduce FONDUE, a framework for utilizing network embedding methods for data-driven disambiguation and deduplication of nodes. Given an undirected and unweighted network, FONDUE-NDA identifies nodes that appear to correspond to multiple entities for subsequent splitting and suggests how to split them (node disambiguation), whereas FONDUE-NDD identifies nodes that appear to correspond to same entity for merging (node deduplication), using only the network topology. From controlled experiments on benchmark networks, we find that FONDUE-NDA is substantially and consistently more accurate with lower computational cost in identifying ambiguous nodes, and that FONDUE-NDD is a competitive alternative for node deduplication, when compared to state-of-the-art alternatives.

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A Graph-Based Author Name Disambiguation Method and Analysis via Information Theory

Cited by 10 publications

References 27 publications

Author Name Disambiguation Based on Heterogeneous Graph

Author Name Disambiguation Based on Heterogeneous Graph

Disambiguation of author entities in ADS using supervised learning and graph theory methods

FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings

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