Knowledge-Driven Intelligent Survey Systems Towards Open Science

Elspeth Edelstein, Jeff Z. Pan* (Corresponding Author), Ricardo Soares, Adam Wyner

*Corresponding author for this work

Research output: Contribution to journalArticle

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Abstract

In this paper, we propose Knowledge Graph (KG), an articulated underlying semantic structure, as a semantic bridge between humans, systems, and scientific knowledge. To illustrate our proposal, we focus on KG based intelligent survey systems. In state of the art systems, information is hard-coded or implicit, making it hard for researchers to reuse, customise, link, or transmit structured knowledge. Furthermore, such systems do not facilitate dynamic interaction based on semantic structure. We design and implement a knowledge-driven intelligent survey system which is based on knowledge graph, a widely used technology that facilitates sharing and querying hypotheses, survey content, results, and analyses. The approach is developed, implemented, and tested in the field of Linguistics. Syntacticians and morphologists develop theories of grammar of natural languages. To evaluate theories, they seek intuitive grammaticality (well-formedness) judgments from native speakers, which either support hypotheses or provide counter-evidence. Our preliminary
experiments show that a knowledge graph based linguistic survey can provide more nuanced results than traditional document-based grammaticality judgment surveys by allowing for tagging and manipulation of specific linguistic variables.
Original languageEnglish
Pages (from-to)397-421
Number of pages25
JournalNew Generation Computing
Volume38
Issue number3
Early online date11 Mar 2020
DOIs
Publication statusPublished - Jul 2020

Keywords

  • knowledge graph
  • intelligent survey system
  • grammaticality judgments
  • Grammaticality judgments
  • Intelligent survey system
  • Knowledge Graph

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