Knowledge graphs have gained increasing popularity in the last decade in science and technology. However, knowledge graphs are currently relatively simple to moderate semantic structures that are mainly a collection of factual statements. Question answering (QA) benchmarks and systems were so far mainly geared towards encyclopedic knowledge graphs such as DBpedia and Wikidata. We present SciQA a scientific QA benchmark for scholarly knowledge. The benchmark leverages the Open Research Knowledge Graph (ORKG) which includes almost 170,000 resources describing research contributions of almost 15,000 scholarly articles from 709 research fields. Following a bottom-up methodology, we first manually developed a set of 100 complex questions that can be answered using this knowledge graph. Furthermore, we devised eight question templates with which we automatically generated further 2465 questions, that can also be answered with the ORKG. The questions cover a range of research fields and question types and are translated into corresponding SPARQL queries over the ORKG. Based on two preliminary evaluations, we show that the resulting SciQA benchmark represents a challenging task for next-generation QA systems. This task is part of the open competitions at the 22nd International Semantic Web Conference 2023 as the Scholarly Question Answering over Linked Data (QALD) Challenge. 

/pdf/the-sciqa-scientific-question-answering-benchmark-for-ffw7y1ap.pdf

The SciQA Scientific Question Answering Benchmark for Scholarly Knowledge

Scientific knowledge graphs have been proposed as a solution to structure the content of research publications in a machine-actionable way and enable more efficient, computer-assisted workflows for many research activities. Crowd-sourcing approaches are used frequently to build and maintain such scientific knowledge graphs. To contribute to scientific knowledge graphs, researchers need simple and easy-to-use solutions to generate new knowledge graph elements and establish the practice of semantic representations in scientific communication. In this paper, we present a workflow for authors of scientific documents to specify their contributions with a LaTeX package, called SciKGTeX, and upload them to a scientific knowledge graph. The SciKGTeX package allows authors of scientific publications to mark the main contributions of their work directly in LaTeX source files. The package embeds marked contributions as metadata into the generated PDF document, from where they can be extracted automatically and imported into a scientific knowledge graph, such as the ORKG. This workflow is simpler and faster than current approaches, which make use of external web interfaces for data entry. Our user evaluation shows that SciKGTeX is easy to use, with a score of 79 out of 100 on the System Usability Scale, as participants of the study needed only 7 minutes on average to annotate the main contributions on a sample abstract of a published paper. Further testing shows that the embedded contributions can be successfully uploaded to ORKG within ten seconds. SciKGTeX simplifies the process of manual semantic annotation of research contributions in scientific articles. Our workflow demonstrates how a scientific knowledge graph can automatically ingest research contributions from document metadata.

pdf/scikgtex-a-latex-package-to-semantically-annotate-11rx6gjn.pdf

SciKGTeX - A LaTeX Package to Semantically Annotate Contributions in Scientific Publications

Abstract: Open Science practices have become well established in recent years. In this position paper, we argue that Open Data in particular holds great potential for empirical research in sports science, and sport and exercise psychology in particular, since it fosters the reintegration of scientific knowledge as primary research data in subsequent research life cycles. On that account, the sports science community has to develop a unified position on research data management, which supports the implementation of Open Science practices and standards. To this end, in this article we first define Open Science and research data management (RDM) and describe them in the context of sports science. We then present examples of existing, relevant RDM solutions, with a particular focus on sport and exercise psychology and neighboring disciplines. Finally, we derive perspectives for the development of a sustainable RDM structure and present current developments within the German sports science community.

Perspectives and Potentials of Open Data for the Sports Sciences

Research publications are the primary vehicle for sharing scientific progress in the form of new discoveries, methods, techniques, and insights. Unfortunately, the lack of a large-scale, comprehensive, and easy-to-use resource capturing the myriad relationships between publications, their authors, and venues presents a barrier to applications for gaining a deeper understanding of science. In this paper, we present PubGraph, a new resource for studying scientific progress that takes the form of a large-scale knowledge graph (KG) with more than 385M entities, 13B main edges, and 1.5B qualifier edges. PubGraph is comprehensive and unifies data from various sources, including Wikidata, OpenAlex, and Semantic Scholar, using the Wikidata ontology. Beyond the metadata available from these sources, PubGraph includes outputs from auxiliary community detection algorithms and large language models. To further support studies on reasoning over scientific networks, we create several large-scale benchmarks extracted from PubGraph for the core task of knowledge graph completion (KGC). These benchmarks present many challenges for knowledge graph embedding models, including an adversarial community-based KGC evaluation setting, zero-shot inductive learning, and large-scale learning. All of the aforementioned resources are accessible at https://pubgraph.isi.edu/ and released under the CC-BY-SA license. We plan to update PubGraph quarterly to accommodate the release of new publications.

PubGraph: A Large-Scale Scientific Knowledge Graph

The Open Research Knowledge Graph (ORKG) is an Open Science digital infrastructure for the production, curation, publication, and reuse of machine-actionable scholarly knowledge. Built on top of the RDF data model and extensible ontologies, the ORKG provides a common vocabulary for researchers to describe their research contributions and data, improving the discoverability and reusability of scholarly knowledge and research data. The ORKG includes tools for visualizing the relationships between different entities, making it easier to understand the connections between different pieces of research and their findings. It facilitates collaboration between researchers by providing a collaborative platform for organizing and sharing scholarly knowledge and data, reducing duplication and enabling more efficient use of resources. As research becomes increasingly data-driven, tools like the ORKG will become essential for enabling efficient, transparent, and collaborative research.

Organizing Scholarly Knowledge in the Open Research Knowledge Graph

The Open Research Knowledge Graph is an infrastructure for the production, curation, publication and use of FAIR scientific information. Its mission is to shape a future scholarly publishing and communication where the contents of scholarly articles are FAIR research data.

FAIR scientific information with the Open Research Knowledge Graph

The Open Research Knowledge Graph (ORKG) is a digital library for machine-actionable scholarly knowledge, with a focus on structured research comparisons obtained through expert crowdsourcing. While the ORKG has attracted a community of more than 1,000 users, the curated data has not been subject to an in-depth quality assessment so far. Here, proposed as a first exemplary step, within a team of domain experts, we evaluate the quality of six selected ORKG Comparisons based on three criteria, namely: 1) the quality of semantic modelling, 2) the maturity of the Comparisons in terms of their completeness, syntactic representation, identifier stability, and their linkability mechanisms ensuring the interoperability and discoverability. Finally, 3) the informative usefulness of the Comparisons to expert and lay users. We have found that each criterion addresses a unique and independent aspect of quality. Backed by the observations of our quality evaluations presented in this paper, a fitting model of knowledge graph quality appears one that is indeed multidimensional as ours.

Quality Assessment of Research Comparisons in the Open Research Knowledge Graph

Rivest, Shamir, Adleman, RSA algorithm is a popular public key cryptosystem and is known to be secure, however, this fact relies on the difficulty of factoring large numbers. No algorithm has been published that can factor all integers in polynomial time. This paper proposes a new function that can be used to improve the process of factoring integer numbers. It gets the factor faster than known methods. By making use of such proposed function, corresponding two algorithms are proposed and pseudocoded. The utilization of these algorithms along with the basics of the theory of numbers led to three other new factoring algorithms. The five algorithms are implemented and verified using Python Language. The tabulated results that represent the time of factorization versus the number of digits of the large number have indicated the applicability of the last three algorithms.

/pdf/new-pragmatic-algorithms-to-improve-factoring-of-large-2egwejk60e.pdf

New Pragmatic Algorithms to Improve Factoring of Large Numbers

One of the pillars of the scientific method is reproducibility – the ability to replicate the results of a prior study if the same procedures are followed. A lack of reproducibility can lead to wasted resources, false conclusions, and a loss of public trust in science. Ensuring reproducibility is challenging due to the heterogeneity of the methods used in different fields of science. In this article, we present an approach for increasing the reproducibility of research results, by semantically describing and interlinking relevant artifacts such as data, software scripts or simulations in a knowledge graph. In order to ensure the flexibility to adapt the approach to different fields of science, we devise a template model, which allows defining typical descriptions required to increase reproducibility of a certain type of study. We provide a scoring model for gradually assessing the reproducibility of a certain study based on the templates and provide a knowledge graph infrastructure for curating reproducibility descriptions along with semantic research contribution descriptions. We demonstrate the feasibility of our approach with an example in data science. 

Increasing Reproducibility in Science by Interlinking Semantic Artifact Descriptions in a Knowledge Graph

. Knowledge Graphs (KG) have gained increasing importance in science, business and society in the last years. However, most knowledge graphs were either extracted or compiled from existing sources. There are only relatively few examples where knowledge graphs were genuinely created by an intertwined human-machine collaboration. Also, since the quality of data and knowledge graphs is of paramount importance, a number of data quality assessment models have been proposed. However, they do not take the speciﬁc aspects of intertwined human-machine curated knowledge graphs into account. In this work, we propose a graded maturity model for scholarly knowledge graphs (KGMM), which speciﬁcally focuses on aspects related to the joint, evolutionary curation of knowledge graphs for digital libraries. Our model comprises 5 maturity stages with 20 quality measures. We demonstrate the implementation of our model in a large scale scholarly knowledge graph curation eﬀort.

KGMM - A Maturity Model for Scholarly Knowledge Graphs based on Intertwined Human-Machine Collaboration

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FAIR scientific information with the Open Research Knowledge Graph

Quality Assessment of Research Comparisons in the Open Research Knowledge Graph

New Pragmatic Algorithms to Improve Factoring of Large Numbers

Increasing Reproducibility in Science by Interlinking Semantic Artifact Descriptions in a Knowledge Graph

KGMM - A Maturity Model for Scholarly Knowledge Graphs based on Intertwined Human-Machine Collaboration