Generate FAIR Literature Surveys with Scholarly Knowledge Graphs

Reviewing scientific literature is a cumbersome, time consuming but crucial activity in research. Leveraging a scholarly knowledge graph, we present a methodology and a system for comparing scholarly literature, in particular research contributions describing the addressed problem, utilized materials, employed methods and yielded results. The system can be used by researchers to quickly get familiar with existing work in a specific research domain (e.g., a concrete research question or hypothesis). Additionally, it can be used to publish literature surveys following the FAIR Data Principles. The methodology to create a research contribution comparison consists of multiple tasks, specifically: (a) finding similar contributions, (b) aligning contribution descriptions, (c) visualizing and finally (d) publishing the comparison. The methodology is implemented within the Open Research Knowledge Graph (ORKG), a scholarly infrastructure that enables researchers to collaboratively describe, find and compare research contributions. We evaluate the implementation using data extracted from published review articles. The evaluation also addresses the FAIRness of comparisons published with the ORKG

Leveraging human-computer interaction and crowdsourcing for scholarly knowledge graph creation

The number of scholarly publications continues to grow each year, as well as the number of journals and active researchers. Therefore, methods and tools to organize scholarly knowledge are becoming increasingly important. Without such tools, it becomes increasingly difficult to conduct research in an efficient and effective manner. One of the fundamental issues scholarly communication is facing relates to the format in which the knowledge is shared. Scholarly communication relies primarily on narrative document-based formats that are specifically designed for human consumption. Machines cannot easily access and interpret such knowledge, leaving machines unable to provide powerful tools to organize scholarly knowledge effectively. In this thesis, we propose to leverage knowledge graphs to represent, curate, and use scholarly knowledge. The systematic knowledge representation leads to machine-actionable knowledge, which enables machines to process scholarly knowledge with minimal human intervention. To generate and curate the knowledge graph, we propose a machine learning assisted crowdsourcing approach, in particular Natural Language Processing (NLP). Currently, NLP techniques are not able to satisfactorily extract high-quality scholarly knowledge in an autonomous manner. With our proposed approach, we intertwine human and machine intelligence, thus exploiting the strengths of both approaches. First, we discuss structured scholarly knowledge, where we present the Open Research Knowledge Graph (ORKG). Specifically, we focus on the design and development of the ORKG user interface (i.e., the frontend). One of the key challenges is to provide an interface that is powerful enough to create rich knowledge descriptions yet intuitive enough for researchers without a technical background to create such descriptions. The ORKG serves as the technical foundation for the rest of the work. Second, we focus on comparable scholarly knowledge, where we introduce the concept of ORKG comparisons. ORKG comparisons provide machine-actionable overviews of related literature in a tabular form. Also, we present a methodology to leverage existing literature reviews to populate ORKG comparisons via a human-in-the-loop approach. Additionally, we show how ORKG comparisons can be used to form ORKG SmartReviews. The SmartReviews provide dynamic literature reviews in the form of living documents. They are an attempt address the main weaknesses of the current literature review practice and outline how the future of review publishing can look like. Third, we focus designing suitable tasks to generate scholarly knowledge in a crowdsourced setting. We present an intelligent user interface that enables researchers to annotate key sentences in scholarly publications with a set of discourse classes. During this process, researchers are assisted by suggestions coming from NLP tools. In addition, we present an approach to validate NLP-generated statements using microtasks in a crowdsourced setting. With this approach, we lower the barrier to entering data in the ORKG and transform content consumers into content creators. With the work presented, we strive to transform scholarly communication to improve machine-actionability of scholarly knowledge. The approaches and tools are deployed in a production environment. As a result, the majority of the presented approaches and tools are currently in active use by various research communities and already have an impact on scholarly communication.Die Zahl der wissenschaftlichen Veröffentlichungen nimmt jedes Jahr weiter zu, ebenso wie die Zahl der Zeitschriften und der aktiven Forscher. Daher werden Methoden und Werkzeuge zur Organisation von wissenschaftlichem Wissen immer wichtiger. Ohne solche Werkzeuge wird es immer schwieriger, Forschung effizient und effektiv zu betreiben. Eines der grundlegenden Probleme, mit denen die wissenschaftliche Kommunikation konfrontiert ist, betrifft das Format, in dem das Wissen publiziert wird. Die wissenschaftliche Kommunikation beruht in erster Linie auf narrativen, dokumentenbasierten Formaten, die speziell für Experten konzipiert sind. Maschinen können auf dieses Wissen nicht ohne weiteres zugreifen und es interpretieren, so dass Maschinen nicht in der Lage sind, leistungsfähige Werkzeuge zur effektiven Organisation von wissenschaftlichem Wissen bereitzustellen. In dieser Arbeit schlagen wir vor, Wissensgraphen zu nutzen, um wissenschaftliches Wissen darzustellen, zu kuratieren und zu nutzen. Die systematische Wissensrepräsentation führt zu maschinenverarbeitbarem Wissen. Dieses ermöglicht es Maschinen wissenschaftliches Wissen mit minimalem menschlichen Eingriff zu verarbeiten. Um den Wissensgraphen zu generieren und zu kuratieren, schlagen wir einen Crowdsourcing-Ansatz vor, der durch maschinelles Lernen unterstützt wird, insbesondere durch natürliche Sprachverarbeitung (NLP). Derzeit sind NLP-Techniken nicht in der Lage, qualitativ hochwertiges wissenschaftliches Wissen auf autonome Weise zu extrahieren. Mit unserem vorgeschlagenen Ansatz verknüpfen wir menschliche und maschinelle Intelligenz und nutzen so die Stärken beider Ansätze. Zunächst erörtern wir strukturiertes wissenschaftliches Wissen, wobei wir den Open Research Knowledge Graph (ORKG) vorstellen.Insbesondere konzentrieren wir uns auf das Design und die Entwicklung der ORKG-Benutzeroberfläche (das Frontend). Eine der größten Herausforderungen besteht darin, eine Schnittstelle bereitzustellen, die leistungsfähig genug ist, um umfangreiche Wissensbeschreibungen zu erstellen und gleichzeitig intuitiv genug ist für Forscher ohne technischen Hintergrund, um solche Beschreibungen zu erstellen. Der ORKG dient als technische Grundlage für die Arbeit. Zweitens konzentrieren wir uns auf vergleichbares wissenschaftliches Wissen, wofür wir das Konzept der ORKG-Vergleiche einführen. ORKG-Vergleiche bieten maschinell verwertbare Übersichten über verwandtes wissenschaftliches Wissen in tabellarischer Form. Außerdem stellen wir eine Methode vor, mit der vorhandene Literaturübersichten genutzt werden können, um ORKG-Vergleiche mit Hilfe eines Human-in-the-Loop-Ansatzes zu erstellen. Darüber hinaus zeigen wir, wie ORKG-Vergleiche verwendet werden können, um ORKG SmartReviews zu erstellen. Die SmartReviews bieten dynamische Literaturübersichten in Form von lebenden Dokumenten. Sie stellen einen Versuch dar, die Hauptschwächen der gegenwärtigen Praxis des Literaturreviews zu beheben und zu skizzieren, wie die Zukunft der Veröffentlichung von Reviews aussehen kann. Drittens konzentrieren wir uns auf die Gestaltung geeigneter Aufgaben zur Generierung von wissenschaftlichem Wissen in einer Crowdsourced-Umgebung. Wir stellen eine intelligente Benutzeroberfläche vor, die es Forschern ermöglicht, Schlüsselsätze in wissenschaftlichen Publikationen mittles Diskursklassen zu annotieren. In diesem Prozess werden Forschende mit Vorschlägen von NLP-Tools unterstützt. Darüber hinaus stellen wir einen Ansatz zur Validierung von NLP-generierten Aussagen mit Hilfe von Mikroaufgaben in einer Crowdsourced-Umgebung vor. Mit diesem Ansatz senken wir die Hürde für die Eingabe von Daten in den ORKG und setzen Inhaltskonsumenten als Inhaltsersteller ein. Mit der Arbeit streben wir eine Transformation der wissenschaftlichen Kommunikation an, um die maschinelle Verwertbarkeit von wissenschaftlichem Wissen zu verbessern. Die Ansätze und Werkzeuge werden in einer Produktionsumgebung eingesetzt. Daher werden die meisten der vorgestellten Ansätze und Werkzeuge derzeit von verschiedenen Forschungsgemeinschaften aktiv genutzt und haben bereits einen Einfluss auf die wissenschaftliche Kommunikation.EC/ERC/819536/E

Creating a Scholarly Knowledge Graph from Survey Article Tables

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 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

Representing Semantified Biological Assays in the Open Research Knowledge Graph

In the biotechnology and biomedical domains, recent text mining efforts advocate for machine-interpretable, and preferably, semantified, documentation formats of laboratory processes. This includes wet-lab protocols, (in)organic materials synthesis reactions, genetic manipulations and procedures for faster computer-mediated analysis and predictions. Herein, we present our work on the representation of semantified bioassays in the Open Research Knowledge Graph (ORKG). In particular, we describe a semantification system work-in-progress to generate, automatically and quickly, the critical semantified bioassay data mass needed to foster a consistent user audience to adopt the ORKG for recording their bioassays and facilitate the organisation of research, according to FAIR principles.Comment: In Proceedings of 'The 22nd International Conference on Asia-Pacific Digital Libraries

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

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.Comment: Accepted as a full paper at the ICADL 2022: International Conference on Asian Digital Libraries 202

A Decade of Scholarly Research on Open Knowledge Graphs

The proliferation of open knowledge graphs has led to a surge in scholarly research on the topic over the past decade. This paper presents a bibliometric analysis of the scholarly literature on open knowledge graphs published between 2013 and 2023. The study aims to identify the trends, patterns, and impact of research in this field, as well as the key topics and research questions that have emerged. The work uses bibliometric techniques to analyze a sample of 4445 scholarly articles retrieved from Scopus. The findings reveal an ever-increasing number of publications on open knowledge graphs published every year, particularly in developed countries (+50 per year). These outputs are published in highly-referred scholarly journals and conferences. The study identifies three main research themes: (1) knowledge graph construction and enrichment, (2) evaluation and reuse, and (3) fusion of knowledge graphs into NLP systems. Within these themes, the study identifies specific tasks that have received considerable attention, including entity linking, knowledge graph embedding, and graph neural networks

Improving Access to Scientific Literature with Knowledge Graphs

The transfer of knowledge has not changed fundamentally for many hundreds of years: It is usually document-based - formerly printed on paper as a classic essay and nowadays as PDF. With around 2.5 million new research contributions every year, researchers drown in a flood of pseudo-digitized PDF publications. As a result research is seriously weakened. In this article, we argue for representing scholarly contributions in a structured and semantic way as a knowledge graph. The advantage is that information represented in a knowledge graph is readable by machines and humans. As an example, we give an overview on the Open Research Knowledge Graph (ORKG), a service implementing this approach. For creating the knowledge graph representation, we rely on a mixture of manual (crowd/expert sourcing) and (semi-)automated techniques. Only with such a combination of human and machine intelligence, we can achieve the required quality of the representation to allow for novel exploration and assistance services for researchers. As a result, a scholarly knowledge graph such as the ORKG can be used to give a condensed overview on the state-of-the-art addressing a particular research quest, for example as a tabular comparison of contributions according to various characteristics of the approaches. Further possible intuitive access interfaces to such scholarly knowledge graphs include domain-specific (chart) visualizations or answering of natural language questions.Der Verbreitung wissenschaftlicher Erkenntnisse hat sich seit vielen hundert Jahren nicht grundlegend verändert: Er erfolgt in der Regel dokumentenbasiert - früher als klassischer Aufsatz auf Papier gedruckt und heute online als PDF. Mit rund 2,5 Millionen neuen Forschungsbeiträgen pro Jahr ertrinken Forscher in einer Flut von pseudo-digitalisierten PDF-Publikationen. Als Folge davon wird die Forschung stark geschwächt. In diesem Artikel plädieren wir dafür, wissenschaftliche Beiträge in strukturierter und semantischer Form als Wissensgraph zu repräsentieren. Der Vorteil ist, dass die in einem Wissensgraph dargestellten Informationen für Maschinen und Menschen lesbar sind. Als Beispiel geben wir einen Überblick über den Open Research Knowledge Graph (ORKG), einen Dienst, der diesen Ansatz umsetzt. Für die Erstellung des Wissensgraph setzen wir eine Mischung aus manuellen (crowd/expert sourcing) und (halb-)automatisierten Techniken ein. Nur mit einer solchen Kombination aus menschlicher und maschineller Intelligenz können wir die erforderliche Qualität der Darstellung erreichen, um neuartige Explorations- und Unterstützungsdienste für Forscher zu ermöglichen. Im Ergebnis kann ein Wissensgraph wie der ORKG verwendet werden, um einen komprimierten Überblick über den Stand der Technik in Bezug auf eine bestimmte Forschungsaufgabe zu geben, z.B. als tabellarischer Vergleich der Beiträge nach verschiedenen Merkmalen der Ansätze. Weitere mögliche intuitive Nutzungsschnittstellen zu solchen wissenschaftlichen Wissensgraphen sind domänenspezifische Visualisierungen oder die Beantwortung natürlichsprachlicher Fragen mittels Question Answering.Peer Reviewe

University Libraries and the Open Research Knowledge Graph

The transfer of knowledge has not changed fundamentally for many hundreds of years: It is usually document-based - formerly printed on paper as a classic essay and nowadays as PDF. With around 2.5 million new research contributions every year, researchers drown in a flood of pseudo-digitized PDF publications. As a result research is seriously weakened. In this article, we argue for representing scholarly contributions in a structured and semantic way as a knowledge graph. The advantage is that information represented in a knowledge graph is readable by machines and humans. As an example, we give an overview on the Open Research Knowledge Graph (ORKG), a service implementing this approach. For creating the knowledge graph representation, we rely on a mixture of manual (crowd/expert sourcing) and (semi-)automated techniques. Only with such a combination of human and machine intelligence, we can achieve the required quality of the representation to allow for novel exploration and assistance services for researchers. As a result, a scholarly knowledge graph such as the ORKG can be used to give a condensed overview on the state-of-the-art addressing a particular research quest, for example as a tabular comparison of contributions according to various characteristics of the approaches. Further possible intuitive access interfaces to such scholarly knowledge graphs include domain-specific (chart) visualizations or answering of natural language questions

Sentence, Phrase, and Triple Annotations to Build a Knowledge Graph of Natural Language Processing Contributions - A Trial Dataset

This 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

The SciQA Scientific Question Answering Benchmark for Scholarly Knowledge

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