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- ItemNLPContributions: An Annotation Scheme for Machine Reading of Scholarly Contributions in Natural Language Processing Literature(Aachen : RWTH, 2020) D'Souza, Jennifer; Auer, SörenWe describe an annotation initiative to capture the scholarly contributions in natural language processing (NLP) articles, particularly, for the articles that discuss machine learning (ML) approaches for various information extraction tasks. We develop the annotation task based on a pilot annotation exercise on 50 NLP-ML scholarly articles presenting contributions to five information extraction tasks 1. machine translation, 2. named entity recognition, 3. Question answering, 4. relation classification, and 5. text classification. In this article, we describe the outcomes of this pilot annotation phase. Through the exercise we have obtained an annotation methodology; and found ten core information units that reflect the contribution of the NLP-ML scholarly investigations. The resulting annotation scheme we developed based on these information units is called NLPContributions. The overarching goal of our endeavor is four-fold: 1) to find a systematic set of patterns of subject-predicate-object statements for the semantic structuring of scholarly contributions that are more or less generically applicable for NLP-ML research articles; 2) to apply the discovered patterns in the creation of a larger annotated dataset for training machine readers [18] of research contributions; 3) to ingest the dataset into the Open Research Knowledge Graph (ORKG) infrastructure as a showcase for creating user-friendly state-of-the-art overviews; 4) to integrate the machine readers into the ORKG to assist users in the manual curation of their respective article contributions. We envision that the NLPContributions methodology engenders a wider discussion on the topic toward its further refinement and development. Our pilot annotated dataset of 50 NLP-ML scholarly articles according to the NLPContributions scheme is openly available to the research community at https://doi.org/10.25835/0019761.
- Item24th International Conference on Business Information Systems : Preface(Hannover : TIB Open Publishing, 2021) Abramowicz, Witold; Auer, Sören; Abramowicz, Witold; Auer, Sören; Lewańska, Elżbieta
- ItemCrowdsourcing Scholarly Discourse Annotations(New York, NY : ACM, 2021) Oelen, Allard; Stocker, Markus; Auer, SörenThe 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.
- ItemThe STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources(Paris : European Language Resources Association, 2020) D'Souza, Jennifer; Hoppe, Anett; Brack, Arthur; Jaradeh, Mohamad Yaser; Auer, Sören; Ewerth, RalphWe introduce the STEM (Science, Technology, Engineering, and Medicine) Dataset for Scientific Entity Extraction, Classification, and Resolution, version 1.0 (STEM-ECR v1.0). The STEM-ECR v1.0 dataset has been developed to provide a benchmark for the evaluation of scientific entity extraction, classification, and resolution tasks in a domain-independent fashion. It comprises abstracts in 10 STEM disciplines that were found to be the most prolific ones on a major publishing platform. We describe the creation of such a multidisciplinary corpus and highlight the obtained findings in terms of the following features: 1) a generic conceptual formalism for scientific entities in a multidisciplinary scientific context; 2) the feasibility of the domain-independent human annotation of scientific entities under such a generic formalism; 3) a performance benchmark obtainable for automatic extraction of multidisciplinary scientific entities using BERT-based neural models; 4) a delineated 3-step entity resolution procedure for human annotation of the scientific entities via encyclopedic entity linking and lexicographic word sense disambiguation; and 5) human evaluations of Babelfy returned encyclopedic links and lexicographic senses for our entities. Our findings cumulatively indicate that human annotation and automatic learning of multidisciplinary scientific concepts as well as their semantic disambiguation in a wide-ranging setting as STEM is reasonable.
- ItemSemantic Representation of Physics Research Data(Setúbal, Portugal : Science and Technology Publications, Lda, 2020) Say, Aysegul; Fathalla, Said; Vahdati, Sahar; Lehmann, Jens; Auer, Sören; Aveiro, David; Dietz, Jan; Filipe, JoaquimImprovements in web technologies and artificial intelligence enable novel, more data-driven research practices for scientists. However, scientific knowledge generated from data-intensive research practices is disseminated with unstructured formats, thus hindering the scholarly communication in various respects. The traditional document-based representation of scholarly information hampers the reusability of research contributions. To address this concern, we developed the Physics Ontology (PhySci) to represent physics-related scholarly data in a machine-interpretable format. PhySci facilitates knowledge exploration, comparison, and organization of such data by representing it as knowledge graphs. It establishes a unique conceptualization to increase the visibility and accessibility to the digital content of physics publications. We present the iterative design principles by outlining a methodology for its development and applying three different evaluation approaches: data-driven and criteria-based evaluation, as well as ontology testing.
- ItemInformation extraction pipelines for knowledge graphs(London : Springer, 2023) Jaradeh, Mohamad Yaser; Singh, Kuldeep; Stocker, Markus; Both, Andreas; Auer, SörenIn 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.
- 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.
- ItemRanking facts for explaining answers to elementary science questions(Cambridge : Cambridge University Press, 2023) D’Souza, Jennifer; Mulang, Isaiah Onando; Auer, SörenIn 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.
- ItemAnalysing the requirements for an Open Research Knowledge Graph: use cases, quality requirements, and construction strategies(Berlin ; Heidelberg ; New York : Springer, 2021) Brack, Arthur; Hoppe, Anett; Stocker, Markus; Auer, Sören; Ewerth, RalphCurrent 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.
- ItemA Scholarly Knowledge Graph-Powered Dashboard: Implementation and User Evaluation(Lausanne : Frontiers Media, 2022) Lezhnina, Olga; Kismihók, Gábor; Prinz, Manuel; Stocker, Markus; Auer, SörenScholarly 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.