Construction of the Literature Graph in Semantic Scholar

Ammar, Waleed; Groeneveld, Dirk; Bhagavatula, Chandra; Beltagy, Iz; Crawford, Miles; Downey, Doug; Dunkelberger, Jason; Elgohary, Ahmed; Feldman, Sergey; Ha, Vu; Kinney, R.; Kohlmeier, Sebastian; Lo, Kyle; Murray, Tyler; Ooi, Hsu-Han; Peters, Matthew E.; Power, Joanna; Skjonsberg, Sam; Wang, Lucy Lu; Wilhelm, Chris; Zheng, Yi; Zuylen, Madeleine van; Etzioni, Oren

doi:10.48550/arxiv.1805.02262

Cited by 20 publications

(28 citation statements)

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“…The right panel of Figure 1 gives the scree plot for a citation graph on 22,688 academic journals. This graph was constructed from the Semantic Scholar database [Ammar et al, 2018] of roughly 220 million academic papers. Citations from one paper to another were converted to citations between the journals that published the papers.…”

Section: Motivating Examplesmentioning

confidence: 99%

Estimating Graph Dimension with Cross-validated Eigenvalues

Chen¹,

Roch²,

Rohe³

et al. 2021

Preprint

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In applied multivariate statistics, estimating the number of latent dimensions or the number of clusters is a fundamental and recurring problem. One common diagnostic is the scree plot, which shows the largest eigenvalues of the data matrix in decreasing order; the user searches for a "gap" or "elbow" in the decaying eigenvalues; unfortunately, these patterns can hide beneath the bias of the sample eigenvalues. This methodological problem is conceptually difficult because, in many situations, there is only enough signal to detect a subset of the k population dimensions/eigenvectors. In this situation, one could argue that the correct choice of k is the number of detectable dimensions. We alleviate these problems with cross-validated eigenvalues. Under a large class of random graph models, without any parametric assumptions, we provide a p-value for each sample eigenvector. It tests the null hypothesis that this sample eigenvector is orthogonal to (i.e., uncorrelated with) the true latent dimensions. This approach naturally adapts to problems where some dimensions are not statistically detectable. In scenarios where all k dimensions can be estimated, we prove that our procedure consistently estimates k. In simulations and a data example, the proposed estimator compares favorably to alternative approaches in both computational and statistical performance.

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Section: Motivating Examplesmentioning

confidence: 99%

Estimating Graph Dimension with Cross-validated Eigenvalues

Chen¹,

Roch²,

Rohe³

et al. 2021

Preprint

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show abstract

“…We do not consider the tweets without a retrievable arXiv ID from links or text. Further, we leverage the Semantic Scholar Corpus [7] to extract the yearly citation information of arXiv papers. From the arXiv IDs in the tweets, we extracted yearly citation information of 125,521 papers occurring in 367,124 tweets.…”

Section: Data Collection Schemementioning

confidence: 99%

TweetPap: A Dataset to Study the Social Media Discourse of Scientific Papers

Jain

Singh

2021

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL)

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Nowadays, researchers have moved to platforms like Twitter to spread information about their ideas and empirical evidence. Recent studies have shown that social media affects the scientific impact of a paper. However, these studies only utilize the tweet counts to represent Twitter activity. In this paper, we propose TweetPap, a large-scale dataset that introduces temporal information of citation/tweets and the metadata of the tweets to quantify and understand the discourse of scientific papers on social media. The dataset is publicly available at https://github. com/lingo-iitgn/TweetPap.

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“…P5 The co-occurrence of in-text reference pointers within each in-text reference pointer lists (e.g. [3,5,12]). P6 The identifiers of structural elements (e.g.…”

Section: The Opencitations Data Modelmentioning

confidence: 99%

“…In addition, Wos and CiteSeerX also cover types of citations (P1), and only CiteSeerX includes citation context sentences (P6). Open citation data providers include Crossref [18], Europe PubMed Central (EPMC), DataCite, DBLP, Microsoft Academic Knowledge Graph (MAKG) [11] (which is based on Microsoft Academic Graph [34] and which reuses the SPAR Ontologies and links to resources in Wikidata and OpenCitations), the Semantic Scholar Open Research Corpus (ORC) [3], the Semantic Scholars Graph of References in Context (GORC) [23], Springer Natures SciGraph [15] (which is based on Schema.org), WikiCite (which includes terms aligned to SPAR Ontologies and interlinking with the OpenCitations Corpus), and the OpenCitations datasets [29]. All data models cover P2, and all except MAKG also cover P8.…”

Section: E1: Lexical Abstractimilaritymentioning

confidence: 99%

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The OpenCitations Data Model

Daquino¹,

Peroni²,

Shotton³

et al. 2020

Preprint

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A variety of schemas and ontologies are currently used for the machine-readable description of bibliographic entities and citations. This diversity, and the reuse of the same ontology terms with different nuances, generates inconsistencies in data. Adoption of a single data model would facilitate data integration tasks regardless of the data supplier or context application. In this paper we present the OpenCitations Data Model (OCDM), a generic data model for describing bibliographic entities and citations, developed using Semantic Web technologies. We also evaluate the effective reusability of OCDM according to ontology evaluation practices, mention existing users of OCDM, and discuss the use and impact of OCDM in the wider open science community.

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Construction of the Literature Graph in Semantic Scholar

Cited by 20 publications

References 0 publications

Estimating Graph Dimension with Cross-validated Eigenvalues

Estimating Graph Dimension with Cross-validated Eigenvalues

TweetPap: A Dataset to Study the Social Media Discourse of Scientific Papers

The OpenCitations Data Model

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