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Subject: Semantics × WGL Institute: TIB ×

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Now showing 1 - 3 of 3
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    An Approach to Evaluate User Interfaces in a Scholarly Knowledge Communication Domain
    (Cham : Springer, 2023) Obrezkov, Denis; Oelen, Allard; Auer, Sören; Abdelnour-Nocera, José L.; Marta Lárusdóttir; Petrie, Helen; Piccinno, Antonio; Winckler, Marco
    The amount of research articles produced every day is overwhelming: scholarly knowledge is getting harder to communicate and easier to get lost. A possible solution is to represent the information in knowledge graphs: structures representing knowledge in networks of entities, their semantic types, and relationships between them. But this solution has its own drawback: given its very specific task, it requires new methods for designing and evaluating user interfaces. In this paper, we propose an approach for user interface evaluation in the knowledge communication domain. We base our methodology on the well-established Cognitive Walkthough approach but employ a different set of questions, tailoring the method towards domain-specific needs. We demonstrate our approach on a scholarly knowledge graph implementation called Open Research Knowledge Graph (ORKG).
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    Ranking facts for explaining answers to elementary science questions
    (Cambridge : Cambridge University Press, 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.
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    Knowledge organization systems in mathematics and in libraries
    (Zenodo, 2017) Kasprzik, Anna
    Based on the project activities planned in the context of the Specialized Information Service for Mathematics (TIB Hannover, FAU Erlangen, L3S, SUB Göttingen) we give an overview over the history and interplay of subject cataloguing in libraries, the development of computerized methods for metadata processing and the rise of the Semantic Web. We survey various knowledge organization systems such as the Mathematics Subject Classification, the German Authority File, the clustering International Authority File VIAF, and lexical databases such as WordNet and their potential use for mathematics in education and research. We briefly address the difference between thesauri and ontologies and the relations they typically contain from a linguistic perspective. We will then discuss with the audience how the current efforts to represent and handle mathematical theories as semantic objects can help deflect the decline of semantic resource annotation in libraries that has been predicted by some due to the existence of highly performant retrieval algorithms (based on statistical, neuronal, or other big data methods). We will also explore the potential characteristics of a fruitful symbiosis between carefully cultivated kernels of semantic structure and automated methods in order to scale those structures up to the level that is necessary in order to cope with the amounts of digital data found in libraries and in (mathematical) research (e.g., in simulations) today.