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- ItemCreating a Scholarly Knowledge Graph from Survey Article Tables(Cham : Springer, 2020) Oelen, Allard; Stocker, Markus; Auer, Sören; Ishita, Emi; Pang, Natalie Lee San; Zhou, LihongDue 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 that summarizes findings published in the scientific literature. Consequently, survey articles are an excellent resource for generating a scholarly knowledge graph. The presented methodology consists of five steps, in which tables and references are extracted from PDF articles, tables are formatted and finally ingested into the knowledge graph. To evaluate the methodology, 92 survey articles, containing 160 survey tables, have been imported in the graph. In total, 2626 papers have been added to the knowledge graph using the presented methodology. The results demonstrate the feasibility of our approach, but also indicate that manual effort is required and thus underscore the important role of human experts.
- ItemSentence, Phrase, and Triple Annotations to Build a Knowledge Graph of Natural Language Processing Contributions - A Trial Dataset(Beijing : National Science Library, Chinese Academy of Sciences, 2021) D’Souza, Jennifer; Auer, SörenThis work aims to normalize the NlpContributions scheme (henceforward, NlpContributionGraph) to structure, directly from article sentences, the contributions information in Natural Language Processing (NLP) scholarly articles via a two-stage annotation methodology: 1) pilot stage—to define the scheme (described in prior work); and 2) adjudication stage—to normalize the graphing model (the focus of this paper). We re-annotate, a second time, the contributions-pertinent information across 50 prior-annotated NLP scholarly articles in terms of a data pipeline comprising: contribution-centered sentences, phrases, and triple statements. To this end, specifically, care was taken in the adjudication annotation stage to reduce annotation noise while formulating the guidelines for our proposed novel NLP contributions structuring and graphing scheme. The application of NlpContributionGraph on the 50 articles resulted finally in a dataset of 900 contribution-focused sentences, 4,702 contribution-information-centered phrases, and 2,980 surface-structured triples. The intra-annotation agreement between the first and second stages, in terms of F1-score, was 67.92% for sentences, 41.82% for phrases, and 22.31% for triple statements indicating that with increased granularity of the information, the annotation decision variance is greater. NlpContributionGraph has limited scope for structuring scholarly contributions compared with STEM (Science, Technology, Engineering, and Medicine) scholarly knowledge at large. Further, the annotation scheme in this work is designed by only an intra-annotator consensus—a single annotator first annotated the data to propose the initial scheme, following which, the same annotator reannotated the data to normalize the annotations in an adjudication stage. However, the expected goal of this work is to achieve a standardized retrospective model of capturing NLP contributions from scholarly articles. This would entail a larger initiative of enlisting multiple annotators to accommodate different worldviews into a “single” set of structures and relationships as the final scheme. Given that the initial scheme is first proposed and the complexity of the annotation task in the realistic timeframe, our intra-annotation procedure is well-suited. Nevertheless, the model proposed in this work is presently limited since it does not incorporate multiple annotator worldviews. This is planned as future work to produce a robust model. We demonstrate NlpContributionGraph data integrated into the Open Research Knowledge Graph (ORKG), a next-generation KG-based digital library with intelligent computations enabled over structured scholarly knowledge, as a viable aid to assist researchers in their day-to-day tasks. NlpContributionGraph is a novel scheme to annotate research contributions from NLP articles and integrate them in a knowledge graph, which to the best of our knowledge does not exist in the community. Furthermore, our quantitative evaluations over the two-stage annotation tasks offer insights into task difficulty.