Generate FAIR Literature Surveys with Scholarly Knowledge Graphs
arXiv:2006.01747 · doi:10.1145/3383583.3398520
Abstract
Reviewing scientific literature is a cumbersome, time consuming but crucial activity in research. Leveraging a scholarly knowledge graph, we present a methodology and a system for comparing scholarly literature, in particular research contributions describing the addressed problem, utilized materials, employed methods and yielded results. The system can be used by researchers to quickly get familiar with existing work in a specific research domain (e.g., a concrete research question or hypothesis). Additionally, it can be used to publish literature surveys following the FAIR Data Principles. The methodology to create a research contribution comparison consists of multiple tasks, specifically: (a) finding similar contributions, (b) aligning contribution descriptions, (c) visualizing and finally (d) publishing the comparison. The methodology is implemented within the Open Research Knowledge Graph (ORKG), a scholarly infrastructure that enables researchers to collaboratively describe, find and compare research contributions. We evaluate the implementation using data extracted from published review articles. The evaluation also addresses the FAIRness of comparisons published with the ORKG.
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Cited by in corpus (7)
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- LimTopic: LLM-based Topic Modeling and Text Summarization for Analyzing Scientific Articles limitations
- NLPContributions: An Annotation Scheme for Machine Reading of Scholarly Contributions in Natural Language Processing Literature
- Sentence, Phrase, and Triple Annotations to Build a Knowledge Graph of Natural Language Processing Contributions -- A Trial Dataset
- Creating a Scholarly Knowledge Graph from Survey Article Tables
- SmartReviews: Towards Human- and Machine-actionable Representation of Review Articles
- An Approach to Evaluate User Interfaces in a Scholarly Knowledge Communication Domain