Creating a Scholarly Knowledge Graph from Survey Article Tables

@article{Oelen2020CreatingAS,
  title={Creating a Scholarly Knowledge Graph from Survey Article Tables},
  author={Allard Oelen and Markus Stocker and S. Auer},
  journal={ArXiv},
  year={2020},
  volume={abs/2012.00456}
}
Due to the lack of structure, scholarly knowledge remains hardly accessible for machines. Scholarly knowledge graphs have been proposed as a solution. Creating such a knowledge graph requires manual effort and domain experts, and is therefore time-consuming and cumbersome. In this work, we present a human-in-the-loop methodology used to build a scholarly knowledge graph leveraging literature survey articles. Survey articles often contain manually curated and high-quality tabular information… 
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References

SHOWING 1-10 OF 37 REFERENCES
Towards a Knowledge Graph Representing Research Findings by Semantifying Survey Articles
TLDR
This article describes how surveys for research fields can be represented in a semantic way, resulting in a knowledge graph that describes the individual research problems, approaches, implementations and evaluations in a structured and comparable way and demonstrates the utility of the resulting knowledge graph.
Comparing Research Contributions in a Scholarly Knowledge Graph
TLDR
This paper presents a workflow and system designed to compare research contributions in a scientific knowledge graph, implemented in the Open Research Knowledge Graph (ORKG), which enables researchers to find and compare related literature.
An Analysis of the Microsoft Academic Graph
TLDR
The results show that the citation data and publication metadata correlate well with external datasets, and the MAG has very good coverage across different domains with a slight bias towards technical disciplines.
Open Research Knowledge Graph: Next Generation Infrastructure for Semantic Scholarly Knowledge
TLDR
The first steps towards a knowledge graph based infrastructure that acquires scholarly knowledge in machine actionable form are presented thus enabling new possibilities for scholarly knowledge curation, publication and processing.
Crowdsourced semantic annotation of scientific publications and tabular data in PDF
TLDR
The SemAnn approach enables collaborative annotation of text and tables in PDF documents, a format that is still the common denominator of publishing, thus maximising the potential user base and confirming the merit of the low-threshold annotation support for the crowd.
Unsupervised document structure analysis of digital scientific articles
TLDR
This work has developed a processing pipeline that analyses the structure a PDF document using a number of unsupervised machine learning techniques and heuristics and shows that it outperforms a state-of-the-art system in terms of the quality of the extracted body text and table of contents.
A Large Public Corpus of Web Tables containing Time and Context Metadata
TLDR
A large public corpus of Web tables which contains over 233 million tables and has been extracted from the July 2015 version of the CommonCrawl is presented to provide a common ground for evaluating Web table systems.
TableSeer: automatic table metadata extraction and searching in digital libraries
TLDR
Overall, TableSeer eliminates the burden of manually extract table data from digital libraries and enables users to automatically examine tables, and proposes an extensive set of medium-independent metadata for tables that scientists and other users can adopt for representing table information.
Unleashing Tabular Content to Open Data: A Survey on PDF Table Extraction Methods and Tools
TLDR
This paper aims at providing a structured and comprehensive overview of the research in tabular content extraction specifically from PDF documents as well as to provide an overview of most recent practical results in the literature.
Evaluating Reference String Extraction Using Line-Based Conditional Random Fields: A Case Study with German Language Publications
TLDR
This work proposes a classification model that considers every line in a publication as a potential part of a reference string by applying line-based conditional random fields rather than constructing the graphical model based on individual words, dependencies and patterns that are typical in reference sections.
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