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A Knowledge Graph Approach for Exploratory Search in Research Institutions (2311.15688v2)
Published 27 Nov 2023 in cs.DL
Abstract: Over the past decades, research institutions have grown increasingly and consequently also their research output. This poses a significant challenge for researchers seeking to understand the research landscape of an institution. The process of exploring the research landscape of institutions has a vague information need, no precise goal, and is open-ended. Current applications are not designed to fulfill the requirements for exploratory search in research institutions. In this paper, we analyze exploratory search in research institutions and propose a knowledge graph-based approach to enhance this process.
- Is exploratory search different? a comparison of information search behavior for exploratory and lookup tasks. Journal of the Association for Information Science and Technology, 67(11):2635–2651.
- Brainard, J. (2020). Scientists are drowning in covid-19 papers. can new tools keep them afloat. Science, 13(10):1126.
- A century of science: Globalization of scientific collaborations, citations, and innovations. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’17, page 1437–1446, New York, NY, USA. Association for Computing Machinery.
- Studying interaction patterns for knowledge graph exploration. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR, pages 257–264. INSTICC, SciTePress.
- NLP support for faceted navigation in scholarly collection. In Proceedings of the 2009 Workshop on Text and Citation Analysis for Scholarly Digital Libraries (NLPIR4DL), pages 62–70, Suntec City, Singapore. Association for Computational Linguistics.
- Open research knowledge graph: Next generation infrastructure for semantic scholarly knowledge. In Proceedings of the 10th International Conference on Knowledge Capture, K-CAP ’19, page 243–246, New York, NY, USA. Association for Computing Machinery.
- A survey of definitions and models of exploratory search. In Proceedings of the 2017 ACM Workshop on Exploratory Search and Interactive Data Analytics, ESIDA ’17, page 3–8, New York, NY, USA. Association for Computing Machinery.
- A decade of knowledge graphs in natural language processing: A survey. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 601–614, Online only. Association for Computational Linguistics.
- A web-scale system for scientific knowledge exploration. In Proceedings of ACL 2018, System Demonstrations, pages 87–92, Melbourne, Australia. Association for Computational Linguistics.
- An overview of microsoft academic service (mas) and applications. In Proceedings of the 24th International Conference on World Wide Web, WWW ’15 Companion, page 243–246, New York, NY, USA. Association for Computing Machinery.
- Multi-label classification of scientific research documents across domains and languages. In Proceedings of the Third Workshop on Scholarly Document Processing, pages 105–114, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Microsoft Academic Graph: When experts are not enough. Quantitative Science Studies, 1(1):396–413.
- Exploratory Search: Beyond the Query-Response Paradigm, pages 1–98. Springer International Publishing, Cham.