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.

2023-04-24, Kaliuzhna, Nataliia

The purpose of the article is to conduct a comprehensive, objective and critical analysis of the results of research on open access in Ukraine to assess the current state of the topic and identify the aspects which require deeper study in order to develop an effective mechanism for implementing the principles of open access in practice. Research methods. The method of narrative literature review with a defined search strategy and criteria for selecting publications in four databases, such as Dimensions, Scopus, Web of Science and the depository of electronic copies “Scientific Periodicals of Ukraine” was used. The inclusion criteria were articles published by authors affiliated with Ukrainian institutions. The scientific novelty lies in the fact that the literature analysis helped to expand and deepen knowledge about the thematic areas of open access research in Ukraine; to identify previously unknown links and contradictions between studies; and to identify areas that require further studies, which include, in particular, the analysis of factors and barriers that facilitate or prevent Ukrainian authors from disseminating their works in open access, the dynamics of growth and peculiarities of the distribution of the share of open access publications by year and field of study. Conclusions. Open access is one of the main components of Open Science. It serves as a tool for accelerating knowledge sharing, developing science, helping to eliminate inequality, and helping to solve several global problems. The research of Ukrainian scientists focuses on four primary areas of open access research: the development of open access policies at the level of institutions and the state, the introduction of institutional repositories, the launch of open access journals, and the regulation of copyright in open scientific communication.

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.

2023, Cheema, Gullal S., Hakimov, Sherzod, Müller-Budack, Eric, Otto, Christian, Bateman, John A., Ewerth, Ralph

The analysis of news dissemination is of utmost importance since the credibility of information and the identification of disinformation and misinformation affect society as a whole. Given the large amounts of news data published daily on the Web, the empirical analysis of news with regard to research questions and the detection of problematic news content on the Web require computational methods that work at scale. Today's online news are typically disseminated in a multimodal form, including various presentation modalities such as text, image, audio, and video. Recent developments in multimodal machine learning now make it possible to capture basic “descriptive” relations between modalities–such as correspondences between words and phrases, on the one hand, and corresponding visual depictions of the verbally expressed information on the other. Although such advances have enabled tremendous progress in tasks like image captioning, text-to-image generation and visual question answering, in domains such as news dissemination, there is a need to go further. In this paper, we introduce a novel framework for the computational analysis of multimodal news. We motivate a set of more complex image-text relations as well as multimodal news values based on real examples of news reports and consider their realization by computational approaches. To this end, we provide (a) an overview of existing literature from semiotics where detailed proposals have been made for taxonomies covering diverse image-text relations generalisable to any domain; (b) an overview of computational work that derives models of image-text relations from data; and (c) an overview of a particular class of news-centric attributes developed in journalism studies called news values. The result is a novel framework for multimodal news analysis that closes existing gaps in previous work while maintaining and combining the strengths of those accounts. We assess and discuss the elements of the framework with real-world examples and use cases, setting out research directions at the intersection of multimodal learning, multimodal analytics and computational social sciences that can benefit from our approach.