Name ambiguity in the context of bibliographic citation records is a hard problem that affects the quality of services and content in digital libraries and similar systems. The challenges of dealing with author name ambiguity have led to a myriad of disambiguation methods. Generally speaking, the proposed methods usually attempt to group citation records of a same author by finding some similarity among them or try to directly assign them to their respective authors. Both approaches may either exploit supervised or unsupervised techniques. In this article, we propose a taxonomy for characterizing the current author name disambiguation methods described in the literature, present a brief survey of the most representative ones and discuss several open challenges.

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A brief survey of automatic methods for author name disambiguation

The reflective practitioner

Author ambiguity mainly arises when several different authors express their names in the same way, generally known as the namesake problem, and also when the name of an author is expressed in many different ways, referred to as the heteronymous name problem. These author ambiguity problems have long been an obstacle to efficient information retrieval in digital libraries, causing incorrect identification of authors and impeding correct classification of their publications. It is a nontrivial task to distinguish those authors, especially when there is very limited information about them. In this paper, we propose a graph based approach to author name disambiguation, where a graph model is constructed using the co-author relations, and author ambiguity is resolved by graph operations such as vertex (or node) splitting and merging based on the co-authorship. In our framework, called a Graph Framework for Author Disambiguation (GFAD), the namesake problem is solved by splitting an author vertex involved in multiple cycles of co-authorship, and the heteronymous name problem is handled by merging multiple author vertices having similar names if those vertices are connected to a common vertex. Experiments were carried out with the real DBLP and Arnetminer collections and the performance of GFAD is compared with three representative unsupervised author name disambiguation systems. We confirm that GFAD shows better overall performance from the perspective of representative evaluation metrics. An additional contribution is that we released the refined DBLP collection to the public to facilitate organizing a performance benchmark for future systems on author disambiguation.

Author name disambiguation using a graph model with node splitting and merging based on bibliographic information

Digital libraries content and quality of services are badly affected by the author name ambiguity problem in the citations and it is considered as one of the hardest problems faced by the digital library researchers. Several techniques have been proposed in the literature for the author name ambiguity problem. In this paper, we reviewed some recently presented author name disambiguation techniques and give some challenges and future research directions. We analyze the recent advancements in this field and classify these techniques into supervised, unsupervised, semi-supervised, graph-based and heuristic-based techniques according to their problem formulation that is mainly used for the author name disambiguation. A few surveys have been conducted to review different techniques for the author name disambiguation. These surveys highlighted only the methodology adopted for author name disambiguation but did not critically review their shortcomings. This survey provides a detailed review of author name disambiguation techniques available in the literature, makes a comparison of these techniques at an abstract level and discusses their limitations.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/EF8B67C2D3BFABBB05F883C899A9934A/S0269888917000182a.pdf/div-class-title-a-survey-of-author-name-disambiguation-techniques-2010-2016-div.pdf

A survey of author name disambiguation techniques: 2010–2016

When a digital library user searches for publications by an author name, she often sees a mixture of publications by different authors who have the same name. With the growth of digital libraries and involvement of more authors, this author ambiguity problem is becoming critical. Author disambiguation (AD) often tries to solve this problem by leveraging metadata such as coauthors, research topics, publication venues and citation information, since more personal information such as the contact details is often restricted or missing. In this paper, we study the problem of how to efficiently disambiguate author names given an incessant stream of published papers. To this end, we propose a "BatchAD+IncAD" framework for dynamic author disambiguation. First, we perform batch author disambiguation (BatchAD) to disambiguate all author names at a given time by grouping all records (each record refers to a paper with one of its author names) into disjoint clusters. This establishes a one-to-one mapping between the clusters and real-world authors. Then, for newly added papers, we periodically perform incremental author disambiguation (IncAD), which determines whether each new record can be assigned to an existing cluster, or to a new cluster not yet included in the previous data. Based on the new data, IncAD also tries to correct previous AD results. Our main contributions are: (1) We demonstrate with real data that a small number of new papers often have overlapping author names with a large portion of existing papers, so it is challenging for IncAD to effectively leverage previous AD results. (2) We propose a novel IncAD model which aggregates metadata from a cluster of records to estimate the author's profile such as her coauthor distributions and keyword distributions, in order to predict how likely it is that a new record is "produced" by the author. (3) Using two labeled datasets and one large-scale raw dataset, we show that the proposed method is much more efficient than state-of-the-art methods while ensuring high accuracy.

Dynamic author name disambiguation for growing digital libraries

Name ambiguity in the context of bibliographic citations is one of the hardest problems currently faced by the Digital Library (DL) community. Here we deal with the problem of disambiguating new citations records inserted into a cleaned DL, without the need to process the whole collection, which is usually necessary for unsupervised methods. Although supervised solutions can deal with this situation, there is the costly burden of generating training data besides the fact that these methods cannot handle well the insertion of records of new authors not already existent in the repository. In this article, we propose a new unsupervised method that identifies the correct authors of the new citation records to be inserted in a DL. The method is based on heuristics that are also used to identify whether the new records belong to authors already in the digital library or not, correctly identifying new authors in most cases. Our experimental evaluation, using synthetic and real datasets, shows gains of up to 19\% when compared to a state-of-the-art method without the cost of having to disambiguate the whole DL at each new load (as done by unsupervised methods) or the need for any training (as done by supervised methods).

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Incremental Unsupervised Name Disambiguation in Cleaned Digital Libraries

MoLIC is a language for modeling human-computer interaction as a conversation that is grounded on Semiotic Engineering theory. It was proposed as an epistemic tool aimed at supporting designers in thinking about and making decisions regarding the designer-to-user communication being conveyed through the system-user communication. In this paper, our goal is to investigate how consolidated MoLIC is since it was first published in 2003. We have carried out a Systematic Literature Review and analyzed how MoLIC has been used by the HCI community, considering how it has been used over the years, the number of authors that reference or use it, forums in which works have been published, as well as the main contributions of the research about it. Our results indicate that MoLIC is well consolidated, as it has been broadly used and studied by national and international authors. The existing version is stable and researchers are interested in applying it to system modeling or to investigate extensions and other aspects related to its use.

An analysis of MoLIC's consolidation

Autism Spectrum Disorder (ASD) is a neurodevelopment disorder mainly characterized by persistent deficits in social communication and social interaction, as well as by restricted and repetitive patterns of behavior, interests, or activities. In the last few years, many studies have presented serious games to support therapy for ASD children. Thus, it is essential that such games be evaluated in order to obtain solid evidence of their positive impact on this type of treatment. In this paper, we aim to investigate how games for autistic children have been evaluated. To achieve this goal, we conducted a Systematic Literature Review and analyzed which evaluation methods have been used, how these methods have been applied and combined, and which quality aspects have been evaluated. In addition, we characterized the number and profile (e.g., age and special need) of the participants involved in this type of evaluation. Our results indicate that there is no consolidated methodology in place, since several methods have been used and combined in different ways to evaluate serious games that support the treatment of ASD children. As contributions, this paper advances knowledge about how serious games have been evaluated in this context.

Ana Paula de Carvalho

Papers

Incremental Unsupervised Name Disambiguation in Cleaned Digital Libraries

An analysis of MoLIC's consolidation

How are games for autistic children being evaluated?