Representing Semantified Biological Assays in the Open Research Knowledge Graph

Enhancing Knowledge Graph Extraction and Validation From Scholarly Publications Using Bibliographic Metadata

“…They can be easily processed using Application Programming Interfaces (APIs, like REST APIs) and query languages (mainly SPARQL) to assess the reference semantic information and to generate accurate and precise interpretations and predictions, particularly when the analyzed data is multifactorial and ever-changing such as the COVID-19 knowledge (Turki et al, 2021c), information about the laureates of Nobel Prize in Literature (Lebuda and Karwowski, 2016), and the findings of scholarly publications (Fathalla et al, 2017). In particular, the role of open knowledge graphs to facilitate scientific collaboration has been stressed against the backdrop of the COVID-19 pandemic (Anteghini et al, 2020;Colavizza et al, 2021;Turki et al, 2021a). Effectively, the information included in textual or semi-structured resources such as electronic health records, scholarly publications, encyclopedic entries, and citation indexes can be converted into fully structured Research Description Framework (RDF) triples and included in knowledge graphs and then processed in near real-time using computer methods to obtain evolving research outputs that are automatically updated as the knowledge graphs feeding them is regularly curated.…”

Easy Semantification of Bioassays

“…Further, semantified data can enable knowledge-based interoperability between multiple databases simply Supported by TIB Leibniz Information Centre for Science and Technology, the EU H2020 ERC project ScienceGRaph (GA ID: 819536) and the ITN PERICO (GA ID: 812968). 4 https://www.orkg.org/ by reusing identifiers and utilizing no-SQL query languages such as SPARQL [33] that can perform distributed queries over the various data sources. Obtaining improved machine interpretability of scientific findings has seen keen interest in the Life Sciences [23] domain.…”

“…This we recently proposed as a work-in-progress idea leveraging Fig. 1: Illustration of labeling versus clustering to aggregate data points a transformer-based supervised classifier [3,4]. Herein, we carry out in detail the experiments we began and further examine a novel clustering objective to bioassays semantification.…”

Automated Mining of Leaderboards for Empirical AI Research

“…Prospectively, machine learning can assist scientists to record their results in the Leaderboards of next-generation digital libraries such as the Open Research Knowledge Graph (ORKG) [27]. In our age of the "deep learning tsunami," [38] there are many studies that have used neural network models to improve the construction of automated scholarly knowledge mining systems [36,12,7,28]. With the recent introduction of language modeling techniques such as transformers [44], the opportunity to obtain boosted machine learning systems is further accentuated.…”

“…The recent NLPContributionGraph Shared Task [19,17,16] released KG annotations of contributions including the facets of research problem, approach, experimental settings, and results, in an evaluation series that showed it a challenging task. Similarly, in the Life Sciences, comprehensive KGs from reports of biological assays, wet lab protocols and inorganic materials synthesis reactions and procedures [7,8,31,32,33,41] are released as ontologized machine-interpretable formats for training machine readers.…”