647 research outputs found
Exploiting citation networks for large-scale author name disambiguation
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 using an
h-index based recall measure, favoring the correct assignment of well-cited
publications, and a name-initials-based precision using WoS metadata and
cross-referenced Google Scholar profiles. Despite the use of limited metadata,
we reach a recall of 87% and a precision of 88% with a preference for
researchers with high h-index values. 47 million articles of WoS can be
disambiguated on a single machine in less than a day. We develop an h-index
distribution model, confirming that the prediction is in excellent agreement
with the empirical data, and yielding insight into the utility of the h-index
in real academic ranking scenarios.Comment: 14 pages, 5 figure
Effect of forename string on author name disambiguation
In author name disambiguation, author forenames are used to decide which name instances are disambiguated together and how much they are likely to refer to the same author. Despite such a crucial role of forenames, their effect on the performance of heuristic (string matching) and algorithmic disambiguation is not well understood. This study assesses the contributions of forenames in author name disambiguation using multiple labeled data sets under varying ratios and lengths of full forenames, reflecting realâworld scenarios in which an author is represented by forename variants (synonym) and some authors share the same forenames (homonym). The results show that increasing the ratios of full forenames substantially improves both heuristic and machineâlearningâbased disambiguation. Performance gains by algorithmic disambiguation are pronounced when many forenames are initialized or homonyms are prevalent. As the ratios of full forenames increase, however, they become marginal compared to those by string matching. Using a small portion of forename strings does not reduce much the performances of both heuristic and algorithmic disambiguation methods compared to using fullâlength strings. These findings provide practical suggestions, such as restoring initialized forenames into a fullâstring format via record linkage for improved disambiguation performances.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155924/1/asi24298.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155924/2/asi24298_am.pd
Whois? Deep Author Name Disambiguation using Bibliographic Data
As the number of authors is increasing exponentially over years, the number
of authors sharing the same names is increasing proportionally. This makes it
challenging to assign newly published papers to their adequate authors.
Therefore, Author Name Ambiguity (ANA) is considered a critical open problem in
digital libraries. This paper proposes an Author Name Disambiguation (AND)
approach that links author names to their real-world entities by leveraging
their co-authors and domain of research. To this end, we use a collection from
the DBLP repository that contains more than 5 million bibliographic records
authored by around 2.6 million co-authors. Our approach first groups authors
who share the same last names and same first name initials. The author within
each group is identified by capturing the relation with his/her co-authors and
area of research, which is represented by the titles of the validated
publications of the corresponding author. To this end, we train a neural
network model that learns from the representations of the co-authors and
titles. We validated the effectiveness of our approach by conducting extensive
experiments on a large dataset.Comment: Accepted for publication @ TPDL202
Effective Unsupervised Author Disambiguation with Relative Frequencies
This work addresses the problem of author name homonymy in the Web of
Science. Aiming for an efficient, simple and straightforward solution, we
introduce a novel probabilistic similarity measure for author name
disambiguation based on feature overlap. Using the researcher-ID available for
a subset of the Web of Science, we evaluate the application of this measure in
the context of agglomeratively clustering author mentions. We focus on a
concise evaluation that shows clearly for which problem setups and at which
time during the clustering process our approach works best. In contrast to most
other works in this field, we are sceptical towards the performance of author
name disambiguation methods in general and compare our approach to the trivial
single-cluster baseline. Our results are presented separately for each correct
clustering size as we can explain that, when treating all cases together, the
trivial baseline and more sophisticated approaches are hardly distinguishable
in terms of evaluation results. Our model shows state-of-the-art performance
for all correct clustering sizes without any discriminative training and with
tuning only one convergence parameter.Comment: Proceedings of JCDL 201
Scaleâfree collaboration networks: An author name disambiguation perspective
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149559/1/asi24158.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149559/2/asi24158_am.pd
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