Entification of Metadata for Knowledge-Graph-Guided Discovery

Brendan John Honick, Katherine Louise Polley, Jian Qin

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


This paper reports on the application of entification to collections of archival metadata. Entification refers to the identification of the entities described in information objects and associated metadata records. The process presents issues about whether existing records-style metadata records are suitable and how entification will affect user access and interaction with metadata and information objects. In this study, we developed an entification workflow that includes the following steps: data cleaning, entity reconciliation to create linked data through metadata enrichment, accuracy selection, and developing knowledge graphs to communicate the semantic relationships among entities. Additionally, we discuss the implications of entification for practitioners in the field of information science, especially current limitations in the process.

Original languageEnglish (US)
Pages (from-to)111-120
Number of pages10
JournalProceedings of the Association for Information Science and Technology
Issue number1
StatePublished - 2022


  • Archival content representation
  • Knowledge graph
  • Metadata enrichment
  • Metadata entification
  • OpenRefine

ASJC Scopus subject areas

  • General Computer Science
  • Library and Information Sciences


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