2021, Abramowicz, Witold, Auer, Sören, Abramowicz, Witold, Auer, Sören, Lewańska, Elżbieta

2021, D’Souza, Jennifer, Auer, Sören

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.

2022, Lezhnina, Olga, Kismihók, Gábor, Prinz, Manuel, Stocker, Markus, Auer, Sören

Scholarly knowledge graphs provide researchers with a novel modality of information retrieval, and their wider use in academia is beneficial for the digitalization of published works and the development of scholarly communication. To increase the acceptance of scholarly knowledge graphs, we present a dashboard, which visualizes the research contributions on an educational science topic in the frame of the Open Research Knowledge Graph (ORKG). As dashboards are created at the intersection of computer science, graphic design, and human-technology interaction, we used these three perspectives to develop a multi-relational visualization tool aimed at improving the user experience. According to preliminary results of the user evaluation survey, the dashboard was perceived as more appealing than the baseline ORKG-powered interface. Our findings can be used for the development of scholarly knowledge graph-powered dashboards in different domains, thus facilitating acceptance of these novel instruments by research communities and increasing versatility in scholarly communication.

2021, Lackner, Arthur, Fathalla, Said, Nayyeri, Mojtaba, Behrend, Andreas, Manthey, Rainer, Auer, Sören, Lehmann, Jens, Vahdati, Sahar

The publish or perish culture of scholarly communication results in quality and relevance to be are subordinate to quantity. Scientific events such as conferences play an important role in scholarly communication and knowledge exchange. Researchers in many fields, such as computer science, often need to search for events to publish their research results, establish connections for collaborations with other researchers and stay up to date with recent works. Researchers need to have a meta-research understanding of the quality of scientific events to publish in high-quality venues. However, there are many diverse and complex criteria to be explored for the evaluation of events. Thus, finding events with quality-related criteria becomes a time-consuming task for researchers and often results in an experience-based subjective evaluation. OpenResearch.org is a crowd-sourcing platform that provides features to explore previous and upcoming events of computer science, based on a knowledge graph. In this paper, we devise an ontology representing scientific events metadata. Furthermore, we introduce an analytical study of the evolution of Computer Science events leveraging the OpenResearch.org knowledge graph. We identify common characteristics of these events, formalize them, and combine them as a group of metrics. These metrics can be used by potential authors to identify high-quality events. On top of the improved ontology, we analyzed the metadata of renowned conferences in various computer science communities, such as VLDB, ISWC, ESWC, WIMS, and SEMANTiCS, in order to inspect their potential as event metrics.

2021, Oelen, Allard, Stocker, Markus, Auer, Sören

The number of scholarly publications grows steadily every year and it becomes harder to find, assess and compare scholarly knowledge effectively. Scholarly knowledge graphs have the potential to address these challenges. However, creating such graphs remains a complex task. We propose a method to crowdsource structured scholarly knowledge from paper authors with a web-based user interface supported by artificial intelligence. The interface enables authors to select key sentences for annotation. It integrates multiple machine learning algorithms to assist authors during the annotation, including class recommendation and key sentence highlighting. We envision that the interface is integrated in paper submission processes for which we define three main task requirements: The task has to be . We evaluated the interface with a user study in which participants were assigned the task to annotate one of their own articles. With the resulting data, we determined whether the participants were successfully able to perform the task. Furthermore, we evaluated the interface’s usability and the participant’s attitude towards the interface with a survey. The results suggest that sentence annotation is a feasible task for researchers and that they do not object to annotate their articles during the submission process.

2023, D’Souza, Jennifer, Mulang, Isaiah Onando, Auer, Sören

In multiple-choice exams, students select one answer from among typically four choices and can explain why they made that particular choice. Students are good at understanding natural language questions and based on their domain knowledge can easily infer the question's answer by “connecting the dots” across various pertinent facts. Considering automated reasoning for elementary science question answering, we address the novel task of generating explanations for answers from human-authored facts. For this, we examine the practically scalable framework of feature-rich support vector machines leveraging domain-targeted, hand-crafted features. Explanations are created from a human-annotated set of nearly 5000 candidate facts in the WorldTree corpus. Our aim is to obtain better matches for valid facts of an explanation for the correct answer of a question over the available fact candidates. To this end, our features offer a comprehensive linguistic and semantic unification paradigm. The machine learning problem is the preference ordering of facts, for which we test pointwise regression versus pairwise learning-to-rank. Our contributions, originating from comprehensive evaluations against nine existing systems, are (1) a case study in which two preference ordering approaches are systematically compared, and where the pointwise approach is shown to outperform the pairwise approach, thus adding to the existing survey of observations on this topic; (2) since our system outperforms a highly-effective TF-IDF-based IR technique by 3.5 and 4.9 points on the development and test sets, respectively, it demonstrates some of the further task improvement possibilities (e.g., in terms of an efficient learning algorithm, semantic features) on this task; (3) it is a practically competent approach that can outperform some variants of BERT-based reranking models; and (4) the human-engineered features make it an interpretable machine learning model for the task.

2022, Sheveleva, Tatyana, Wawer, Max Leo, Oladazimi, Pooya, Koepler, Oliver, Nürnberger, Florian, Lachmayer, Roland, Auer, Sören, Mozgova, Iryna

The seamless documentation of research data flows from generation, processing, analysis, publication, and reuse is of utmost importance when dealing with large amounts of data. Semantic linking of process documentation and gathered data creates a knowledge space enabling the discovery of relations between steps of process chains. This paper shows the design of two systems for data deposit and for process documentation using semantic annotations and linking on a use case of a process chain step of the Tailored Forming Technology.

2023, Jaradeh, Mohamad Yaser, Singh, Kuldeep, Stocker, Markus, Both, Andreas, Auer, Sören

In the last decade, a large number of knowledge graph (KG) completion approaches were proposed. Albeit effective, these efforts are disjoint, and their collective strengths and weaknesses in effective KG completion have not been studied in the literature. We extend Plumber, a framework that brings together the research community’s disjoint efforts on KG completion. We include more components into the architecture of Plumber to comprise 40 reusable components for various KG completion subtasks, such as coreference resolution, entity linking, and relation extraction. Using these components, Plumber dynamically generates suitable knowledge extraction pipelines and offers overall 432 distinct pipelines. We study the optimization problem of choosing optimal pipelines based on input sentences. To do so, we train a transformer-based classification model that extracts contextual embeddings from the input and finds an appropriate pipeline. We study the efficacy of Plumber for extracting the KG triples using standard datasets over three KGs: DBpedia, Wikidata, and Open Research Knowledge Graph. Our results demonstrate the effectiveness of Plumber in dynamically generating KG completion pipelines, outperforming all baselines agnostic of the underlying KG. Furthermore, we provide an analysis of collective failure cases, study the similarities and synergies among integrated components and discuss their limitations.

2021, Brack, Arthur, Hoppe, Anett, Stocker, Markus, Auer, Sören, Ewerth, Ralph

Current science communication has a number of drawbacks and bottlenecks which have been subject of discussion lately: Among others, the rising number of published articles makes it nearly impossible to get a full overview of the state of the art in a certain field, or reproducibility is hampered by fixed-length, document-based publications which normally cannot cover all details of a research work. Recently, several initiatives have proposed knowledge graphs (KG) for organising scientific information as a solution to many of the current issues. The focus of these proposals is, however, usually restricted to very specific use cases. In this paper, we aim to transcend this limited perspective and present a comprehensive analysis of requirements for an Open Research Knowledge Graph (ORKG) by (a) collecting and reviewing daily core tasks of a scientist, (b) establishing their consequential requirements for a KG-based system, (c) identifying overlaps and specificities, and their coverage in current solutions. As a result, we map necessary and desirable requirements for successful KG-based science communication, derive implications, and outline possible solutions.

2021, Tavakoli, Mohammadreza, Elias, Mirette, Kismihók, Gábor, Auer, Sören, Scheffel, Maren

Open Educational Resources (OERs) are openly licensed educational materials that are widely used for learning. Nowadays, many online learning repositories provide millions of OERs. Therefore, it is exceedingly difficult for learners to find the most appropriate OER among these resources. Subsequently, the precise OER metadata is critical for providing high-quality services such as search and recommendation. Moreover, metadata facilitates the process of automatic OER quality control as the continuously increasing number of OERs makes manual quality control extremely difficult. This work uses the metadata of 8,887 OERs to perform an exploratory data analysis on OER metadata. Accordingly, this work proposes metadata-based scoring and prediction models to anticipate the quality of OERs. Based on the results, our analysis demonstrated that OER metadata and OER content qualities are closely related, as we could detect high-quality OERs with an accuracy of 94.6%. Our model was also evaluated on 884 educational videos from Youtube to show its applicability on other educational repositories.