Author name ambiguity causes inadequacy and inconvenience in academic information retrieval, which raises the necessity of author name disambiguation (AND). Existing AND methods can be divided into two categories: the models focusing on content information to distinguish whether two papers are written by the same author, the models focusing on relation information to represent information as edges on the network and to quantify the similarity among papers. However, the former requires adequate labeled samples and informative negative samples, and are also ineffective in measuring the high-order connections among papers, while the latter needs complicated feature engineering or supervision to construct the network. We propose a novel generative adversarial framework to grow the two categories of models together: (i) the discriminative module distinguishes whether two papers are from the same author, and (ii) the generative module selects possibly homogeneous papers directly from the heterogeneous information network, which eliminates the complicated feature engineering. In such a way, the discriminative module guides the generative module to select homogeneous papers, and the generative module generates high-quality negative samples to train the discriminative module to make it aware of high-order connections among papers. Furthermore, a self-training strategy for the discriminative module and a random walk based generating algorithm are designed to make the training stable and efficient. Extensive experiments on two real-world AND benchmarks demonstrate that our model provides significant performance improvement over the state-of-the-art methods.
In this work, we introduce a novel method for entity resolution author disambiguation in bibliographic networks. Such a method is based on a 2-steps network traversal using topological similarity measures for rating candidate nodes. Topological similarity is widely used in the Link Prediction application domain to assess the likelihood of an unknown link. A similarity function can be a good approximation for equality, therefore can be used to disambiguate, basing on the hypothesis that authors with many common co-authors are similar. Our method has experimented on a graph-based representation …
Following the work of Krumov et al. [Eur. Phys. J. B 84, 535 (2011)] we revisit the question whether the usage of large citation datasets allows for the quantitative assessment of social (by means of coauthorship of publications) influence on the progression of science. Applying a more comprehensive and well-curated dataset containing the publications in the journals of the American Physical Society during the whole 20th century we find that the measure chosen in the original study, a score based on small induced subgraphs, has to be used with caution, since the obtained results are highly sen…
We present a manually-labeled Author Name Disambiguation(AND) Dataset called WhoisWho, which consists of 399,255 documents and 45,187 distinct authors with 421 ambiguous author names. To label such a great amount of AND data of high accuracy, we propose a novel annotation framework where the human and computer collaborate efficiently and precisely. Within the framework, we also propose an inductive disambiguation model to classify whether two documents belong to the same author. We evaluate the proposed method and other state-of-the-art disambiguation methods on WhoisWho. The experiment result…
Several studies have found that collaboration networks are scale-free, proposing that such networks can be modeled by specific network evolution mechanisms like preferential attachment. This study argues that collaboration networks can look more or less scale-free depending on the methods for resolving author name ambiguity in bibliographic data. Analyzing networks constructed from multiple datasets containing 3.4M ~ 9.6M publication records, this study shows that collaboration networks in which author names are disambiguated by the commonly used heuristic, i.e., forename-initial-based name ma…
Scholars have often relied on name initials to resolve name ambiguities in large-scale coauthorship network research. This approach bears the risk of incorrectly merging or splitting author identities. The use of initial-based disambiguation has been justified by the assumption that such errors would not affect research findings too much. This paper tests this assumption by analyzing coauthorship networks from five academic fields - biology, computer science, nanoscience, neuroscience, and physics - and an interdisciplinary journal, PNAS. Name instances in datasets of this study were disambigu…
Beihang University（北京航空航天大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Lehigh University（理海大学）
Name disambiguation aims to identify unique authors with the same name. Existing name disambiguation methods always exploit author attributes to enhance disambiguation results. However, some discriminative author attributes (e.g., email and affiliation) may change because of graduation or job-hopping, which will result in the separation of the same author's papers in digital libraries. Although these attributes may change, an author's co-authors and research topics do not change frequently with time, which means that papers within a period have similar text and relation information in the acad…
Entity resolution is a challenging and hot research area in the field of Information Systems since last decade. Author Name Disambiguation (AND) in Bibliographic Databases (BD) like DBLP , Citeseer , and Scopus is a specialized field of entity resolution. Given many citations of underlying authors, the AND task is to find which citations belong to the same author. In this survey, we start with three basic AND problems, followed by need for solution and challenges. A generic, five-step framework is provided for handling AND issues. These steps are; (1) Preparation of dataset (2) Selection of pu…
Name disambiguation is a key and also a very tough problem in many online systems such as social search and academic search. Despite considerable research, a critical issue that has not been systematically studied is disambiguation on the fly -- to complete the disambiguation in the real-time. This is very challenging, as the disambiguation algorithm must be accurate, efficient, and error tolerance. In this paper, we propose a novel framework -- CONNA -- to train a matching component and a decision component jointly via reinforcement learning. The matching component is responsible for finding …
We present a novel algorithm and validation method for disambiguating author names in very large bibliographic data sets and apply it to the full Web of Science (WoS) citation index. Our algorithm relies only upon the author and citation graphs available for the whole period covered by the WoS. A pair-wise publication similarity metric, which is based on common co-authors, self-citations, shared references and citations, is established to perform a two-step agglomerative clustering that first connects individual papers and then merges similar clusters. This parameterized model is optimized usi…
Name disambiguation is a complex but highly relevant challenge whenever analysing real-world user data, such as data from version control systems. We propose gambit, a rule-based disambiguation tool that only relies on name and email information. We evaluate its performance against two commonly used algorithms with similar characteristics on manually disambiguated ground-truth data from the Gnome GTK project. Our results show that gambit significantly outperforms both algorithms, achieving an F1 score of 0.985.
Purdue University（普渡大学）；Indiana University Purdue University Indianapolis（印第安纳大学-普渡大学印第安纳波利斯分校）
In real-world, our DNA is unique but many people share names. This phenomenon often causes erroneous aggregation of documents of multiple persons who are namesake of one another. Such mistakes deteriorate the performance of document retrieval, web search, and more seriously, cause improper attribution of credit or blame in digital forensic. To resolve this issue, the name disambiguation task is designed which aims to partition the documents associated with a name reference such that each partition contains documents pertaining to a unique real-life person. Existing solutions to this task subst…
In a social community, multiple persons may share the same name, phone number or some other identifying attributes. This, along with other phenomena, such as name abbreviation, name misspelling, and human error leads to erroneous aggregation of records of multiple persons under a single reference. Such mistakes affect the performance of document retrieval, web search, database integration, and more importantly, improper attribution of credit (or blame). The task of entity disambiguation partitions the records belonging to multiple persons with the objective that each decomposed partition is co…
Revisiting gender bias research in bibliometrics: Standardizing methodological variability using Scholarly Data Analysis (SoDA) Cards
School of Information Sciences, University of Illinois Urbana–Champaign（伊利诺伊大学厄巴纳-香槟分校信息科学学院）；School of Information, University of Michigan - Ann Arbor（密歇根大学安娜堡分校信息学院）；School of Social Sciences and Technology, Technical University of Munich（慕尼黑工业大学社会科学与技术学院）
