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This research book explores the adaptation of traditional Entity Linking techniques to Mathematical Entity Linking (MathEL) for STEM disciplines, addressing the limitations of current Information Retrieval methods in handling mathematical expressions. By developing and evaluating novel MathEL approaches using AI, Machine Learning, and the Wikidata Knowledge Graph, significant progress is achieved in areas such as Formula Concept recognition, semantic formula search, mathematical question answering, physics exam question generation, and STEM document classification. The study also introduces a suite of open-source Wikimedia MathEL tools, including AnnoMathTeX, MathQA, and PhysWikiQuiz, designed to advance Mathematical Information Retrieval and support innovative applications in academic and educational contexts.
This book constitutes the refereed proceedings of the 8th International Conference of the CLEF Initiative, CLEF 2017, held in Dublin, Ireland, in September 2017. The 7 full papers and 9 short papers presented together with 6 best of the labs papers were carefully reviewed and selected from 38 submissions. In addition, this volume contains the results of 10 benchmarking labs reporting their year long activities in overview talks and lab sessions. The papers address all aspects of information access in any modality and language and cover a broad range of topics in the field of multilingual and multimodal information access evaluation.
This edited book is comprised of original research that focuses on technological advancements for effective teaching with an emphasis on learning outcomes, ICT trends in higher education, sustainable developments and digital ecosystem in education, management and industries. The contents of the book are classified as; (i) Emerging ICT Trends in Education, Management and Innovations (ii) Digital Technologies for advancements in education, management and IT (iii) Emerging Technologies for Industries and Education, and (iv) ICT Technologies for Intelligent Applications. The book represents a useful tool for academics, researchers, industry professionals and policymakers to share and learn about the latest teaching and learning practices supported by ICT. It also covers innovative concepts applied in education, management and industries using ICT tools.
This two-volume set LNCS 13192-12193 constitutes the refereed proceedings of the 17th International Conference on Information for a Better World: Shaping the Global Future, held in February 2022. Due to COVID-19 pandemic the conference was held virtually. The 32 full papers and the 29 short papers presented in this volume were carefully reviewed and selected from 167 submissions. They cover topics such as: Library and Information Science; Information Governance and Ethics; Data Science; Human-Computer Interaction and Technology ̧ Information Behaviour and Retrieval ̧ Communities and Media ̧ Health Informatics.
This open access book presents an interdisciplinary approach to reveal biases in English news articles reporting on a given political event. The approach named person-oriented framing analysis identifies the coverage’s different perspectives on the event by assessing how articles portray the persons involved in the event. In contrast to prior automated approaches, the identified frames are more meaningful and substantially present in person-oriented news coverage. The book is structured in seven chapters: Chapter 1 presents a few of the severe problems caused by slanted news coverage and identifies the research gap that motivated the research described in this thesis. Chapter 2 discusses m...
Plagiarism is a problem with far-reaching consequences for the sciences. However, even today’s best software-based systems can only reliably identify copy & paste plagiarism. Disguised plagiarism forms, including paraphrased text, cross-language plagiarism, as well as structural and idea plagiarism often remain undetected. This weakness of current systems results in a large percentage of scientific plagiarism going undetected. Bela Gipp provides an overview of the state-of-the art in plagiarism detection and an analysis of why these approaches fail to detect disguised plagiarism forms. The author proposes Citation-based Plagiarism Detection to address this shortcoming. Unlike character-based approaches, this approach does not rely on text comparisons alone, but analyzes citation patterns within documents to form a language-independent "semantic fingerprint" for similarity assessment. The practicability of Citation-based Plagiarism Detection was proven by its capability to identify so-far non-machine detectable plagiarism in scientific publications.
Identifying plagiarism is a pressing problem for research institutions, publishers, and funding bodies. Current detection methods focus on textual analysis and find copied, moderately reworded, or translated content. However, detecting more subtle forms of plagiarism, including strong paraphrasing, sense-for-sense translations, or the reuse of non-textual content and ideas, remains a challenge. This book presents a novel approach to address this problem—analyzing non-textual elements in academic documents, such as citations, images, and mathematical content. The proposed detection techniques are validated in five evaluations using confirmed plagiarism cases and exploratory searches for new...