path: root/docs/TR-20210730212057-IA-WDS-CG/main.tex

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\title{Internet Archive Scholar Citation Graph Dataset}

\date{August 10, 2021}	% Here you can change the date presented in the paper title
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\author{ Martin Czygan \\
	Internet Archive\\
	San Francisco, CA 94118 \\
	\texttt{martin@archive.org} \\
	%% examples of more authors
	\And
	Bryan Newbold \\
	Internet Archive\\
	San Francisco, CA 94118 \\
	\texttt{bnewbold@archive.org} \\
	\And
	Helge Holzmann \\
	Internet Archive\\
	San Francisco, CA 94118 \\
	\texttt{helge@archive.org} \\
	\And
	Jefferson Bailey \\
	Internet Archive\\
	San Francisco, CA 94118 \\
	\texttt{jefferson@archive.org} \\
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pdftitle={Internet Archive Scholar Citation Graph Dataset},
pdfsubject={cs.DL, cs.IR},
pdfauthor={Martin Czygan, Bryan Newbold, Helge Holzmann, Jefferson Bailey},
pdfkeywords={Web Archiving, Citation Graph},
}

\begin{document}
\maketitle

\begin{abstract}
As part of its scholarly data efforts, the Internet Archive releases a citation
graph dataset derived from scholarly publications and additional data sources. It is
composed of data gathered by the \href{https://fatcat.wiki}{fatcat cataloging project} and related
web-scale crawls targeting primary and secondary scholarly outputs. In
addition, relations are worked out between scholarly publications, web pages
and their archived copies, books from the Open Library project as well as
Wikipedia articles. This first version of the graph consists of over X nodes
and over Y edges. We release this dataset under a Z open license under the
collection at \href{https://archive.org/details/TODO-citation\_graph}{https://archive.org/details/TODO-citation\_graph}, as well as all code
used for derivation under an MIT license.
\end{abstract}


% keywords can be removed
\keywords{Citation Graph Dataset \and Scholarly Communications \and Web Archiving}


\section{Introduction}

The Internet Archive releases a first version of a citation graph dataset
derived from a raw corpus of about 2.5B references gathered from metadata and
from data obtained by PDF extraction tools such as GROBID\citep{lopez2009grobid}.
The goal of this report is to describe briefly the current contents and the
derivation of the Internet Archive Scholar Citation Graph Dataset (IASCG). We expect
this dataset to be iterated upon, with changes both in content and processing.

Modern citation indexes can be traced back to the early computing age, when
projects like the Science Citation Index (1955)\citep{garfield2007evolution}
were first devised, living on in existing commercial knowledge bases today.
Open alternatives were started such as the Open Citations Corpus (OCC) in 2010
- the first version of which contained 6,325,178 individual
references\citep{shotton2013publishing}. Other notable sources from that time
include CiteSeerX\citep{wu2019citeseerx} and CitEc\citep{CitEc}. The last
decade has seen an increase of more openly available reference dataset and
citation projects, like Microsoft Academic\citep{sinha2015overview} and
Initiative for Open Citations\citep{i4oc}\citep{shotton2018funders}. In 2021,
according to \citep{hutchins2021tipping} over 1B citations are publicly
available, marking a tipping point for open citations.

\section{Citation Graph Contents}

% * edges
% * edges exact
% * edges fuzzy
% * edges fuzzy reason (table)
% * number of source docs
% * number of target docs
% * refs to papers
% * refs to books
% * refs to web pages
% * refs to web pages that have been archived
% * refs to web pages that have been archived but not on liveweb any more
%
% Overlaps
%
% * how many edges can be found in COCI as well
% * how many edges can be found in MAG as well
% * how many unique to us edges
%
% Additional numbers
%
% * number of unparsed refs
% * "biblio" field distribution of unparted refs
%
% Potential routes
%
% * journal abbreviation parsing with suffix arrays
% * lookup by name, year and journal


\section{System Design}

TODO: describe limitations, single machine, prohibitive external data store
lookups, and performance advantages of stream processing; “miniature
map-reduce”, id based matching; fuzzy matching; funnel approach; data quality
issues; live system design (es, pg, …)

The constraints for the system design are informed by the volume and the
variety of the data. In total, the raw inputs amount to about X TB uncompressed
textual data. More importantly, while the number of data fields is low, over Y
different combinations of fields are found in the raw reference data. Each
combination of fields may require a slightly different processing path. For
example, references with an arxiv identifier can be processed differently from
references with only a title. We identify about X types of manifestations which
in total amount for Y\% of the reference documents.

Overall, a map-reduce style approach is followed, which e.g. allows for some
uniformity in the overall processing. We extract key value tuples (as TSV) from
the raw JSON data and sort by key. Finally we group pairs with the same key
into groups and apply a function of the elements of the group in order to
generate our target schema (biblioref, called bref, for short).

The key derivation can be exact (e.g. an id like doi, pmid, etc) or based on a
normalization procedure, like a slugified title string. For id based matches we
can generate the bref schema directly. For fuzzy matching candidates, we pass
possible match pairs through a verification procedure, which is implemented for
documents of one specific catalog record schema.

With a few schema conversions, fuzzy matching can be applied to Wikipedia
articles and Open Library editions as well. The aspect of precision and recall
are represented by the two stages: we are generous in the match candidate
generation phase in order to improve recall, but we are strict during
verification, in order to ensure precision.

\section{Fuzzy Matching Approach}

% Take sample of 100 docs, report some precision, recall, F1 on a hand curated small subset.

\section{Discussion}

% need to iterate

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\bibliography{references}  %%% Uncomment this line and comment out the ``thebibliography'' section below to use the external .bib file (using bibtex) .


%%% Uncomment this section and comment out the \bibliography{references} line above to use inline references.
% \begin{thebibliography}{1}

% 	\bibitem{kour2014real}
% 	George Kour and Raid Saabne.
% 	\newblock Real-time segmentation of on-line handwritten arabic script.
% 	\newblock In {\em Frontiers in Handwriting Recognition (ICFHR), 2014 14th
% 			International Conference on}, pages 417--422. IEEE, 2014.

% 	\bibitem{kour2014fast}
% 	George Kour and Raid Saabne.
% 	\newblock Fast classification of handwritten on-line arabic characters.
% 	\newblock In {\em Soft Computing and Pattern Recognition (SoCPaR), 2014 6th
% 			International Conference of}, pages 312--318. IEEE, 2014.

% 	\bibitem{hadash2018estimate}
% 	Guy Hadash, Einat Kermany, Boaz Carmeli, Ofer Lavi, George Kour, and Alon
% 	Jacovi.
% 	\newblock Estimate and replace: A novel approach to integrating deep neural
% 	networks with existing applications.
% 	\newblock {\em arXiv preprint arXiv:1804.09028}, 2018.

% \end{thebibliography}


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