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    • Creating and validating a scholarly knowledge graph using natural language processing and microtask crowdsourcing
    Authors: Allard, Oelen; Markus, Stocker; Sören, Auer;  Advisor: -;  Co-Author: - (2023)

    Due to the growing number of scholarly publications, finding relevant articles becomes increasingly difficult. Scholarly knowledge graphs can be used to organize the scholarly knowledge presented within those publications and represent them in machine-readable formats. Natural language processing (NLP) provides scalable methods to automatically extract knowledge from articles and populate scholarly knowledge graphs. However, NLP extraction is generally not sufficiently accurate and, thus, fails to generate high granularity quality data. In this work, we present TinyGenius, a methodology to validate NLP-extracted scholarly knowledge statements using microtasks performed with crowdsourcing. TinyGenius is employed to populate a paper-centric knowledge graph, using five distinct NLP met...
    • Information extraction pipelines for knowledge graphs
    Authors: Mohamad Yaser, Jaradeh; Kuldeep, Singh; Markus, Stocker;  Advisor: -;  Co-Author: - (2023)

    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 sent...