In-place Synchronisation of Hierarchical Archival Descriptions

Bryant, Michael; Reijnhoudt, Linda; Simeonov, Boyan

doi:10.1109/bigdata.2018.8622517

Cited by 4 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The application of NLP to descriptive records rather than unstructured archival records also highlights opportunities to use NLP in the implementation of linked open data, as also explored by Gracy (2015). In a similar vein, Bryant et al (2018) developed a harvesting methodology to synchronize hierarchical archival descriptions across institutions.…”

Section: Description and Accessmentioning

confidence: 99%

Natural language processing and machine learning as practical toolsets for archival processing

Hutchinson

2020

RMJ

View full text Add to dashboard Cite

Purpose This study aims to provide an overview of recent efforts relating to natural language processing (NLP) and machine learning applied to archival processing, particularly appraisal and sensitivity reviews, and propose functional requirements and workflow considerations for transitioning from experimental to operational use of these tools. Design/methodology/approach The paper has four main sections. 1) A short overview of the NLP and machine learning concepts referenced in the paper. 2) A review of the literature reporting on NLP and machine learning applied to archival processes. 3) An overview and commentary on key existing and developing tools that use NLP or machine learning techniques for archives. 4) This review and analysis will inform a discussion of functional requirements and workflow considerations for NLP and machine learning tools for archival processing. Findings Applications for processing e-mail have received the most attention so far, although most initiatives have been experimental or project based. It now seems feasible to branch out to develop more generalized tools for born-digital, unstructured records. Effective NLP and machine learning tools for archival processing should be usable, interoperable, flexible, iterative and configurable. Originality/value Most implementations of NLP for archives have been experimental or project based. The main exception that has moved into production is ePADD, which includes robust NLP features through its named entity recognition module. This paper takes a broader view, assessing the prospects and possible directions for integrating NLP tools and techniques into archival workflows.

show abstract

Section: Description and Accessmentioning

confidence: 99%

Natural language processing and machine learning as practical toolsets for archival processing

Hutchinson

2020

RMJ

View full text Add to dashboard Cite

show abstract

“…A complementary technique is used by the EHRI project (European Holocaust Research Infrastructure). The project automatically synchronises archival metadata from several sources, and complements them with user-generated metadata and thematic "virtual collections" [Bryant 2018]. Archival metadata is also used to organise archival materials in novel and richer ways; an example is the use of sentiment analysis applied to Germaine Greer's archive [Weber 2019].…”

Section: Search and Retrievalmentioning

confidence: 99%

Archives and AI: An Overview of Current Debates and Future Perspectives

Colavizza

Blanke

Jeurgens

et al. 2021

J. Comput. Cult. Herit.

View full text Add to dashboard Cite

The digital transformation is turning archives, both old and new, into data. As a consequence, automation in the form of artificial intelligence techniques is increasingly applied both to scale traditional recordkeeping activities, and to experiment with novel ways to capture, organise, and access records. We survey recent developments at the intersection of Artificial Intelligence and archival thinking and practice. Our overview of this growing body of literature is organised through the lenses of the Records Continuum model. We find four broad themes in the literature on archives and artificial intelligence: theoretical and professional considerations, the automation of recordkeeping processes, organising and accessing archives, and novel forms of digital archives. We conclude by underlining emerging trends and directions for future work, which include the application of recordkeeping principles to the very data and processes that power modern artificial intelligence and a more structural—yet critically aware—integration of artificial intelligence into archival systems and practice.

show abstract

“…While this hierarchical structure itself does, in a literal sense, consist of connections between archival descriptions from different source institutions, it is also an attempt to reflect the physical organisation of the original material and thus not included in this paper as a linking activity (elsewhere we describe EHRI's technical approach to maintaining archival hierarchies, see: (Bryant et al, 2018).) For the same reasons, we also do not cover the portal's textual search functionality, although the ability to search material from multiple sources is perhaps the primary way that archival collections catalogued by EHRI are (implicitly) connected.…”

Section: Background -Different Linking Approachesmentioning

confidence: 99%

Record linking in the EHRI portal

Erez

Blanke

Bryant

et al. 2020

RMJ

Self Cite

View full text Add to dashboard Cite

Purpose This paper aims to describe the European Holocaust Research Infrastructure (EHRI) project's ongoing efforts to virtually integrate trans-national archival sources via the reconstruction of collection provenance as it relates to copy collections (material copied from one archive to another) and the co-referencing of subject and authority terms across material held by distinct institutions. Design/methodology/approach This paper is a case study of approximately 6,000 words length. The authors describe the scope of the problem of archival fragmentation from both cultural and technical perspectives, with particular focus on Holocaust-related material, and describe, with graph-based visualisations, two ways in which EHRI seeks to better integrate information about fragmented material. Findings As a case study, the principal contributions of this paper include reports on our experience with extracting provenance-based connections between archival descriptions from encoded finding aids and the challenges of co-referencing access points in the absence of domain-specific controlled vocabularies. Originality/value Record linking in general is an important technique in computational approaches to humanities research and one that has rightly received significant attention from scholars. In the context of historical archives, however, the material itself is in most cases not digitised, meaning that computational attempts at linking must rely on finding aids which constitute much fewer rich data sources. The EHRI project’s work in this area is therefore quite pioneering and has implications for archival integration on a larger scale, where the disruptive potential of Linked Open Data is most obvious.

show abstract

In-place Synchronisation of Hierarchical Archival Descriptions

Cited by 4 publications

References 2 publications

Natural language processing and machine learning as practical toolsets for archival processing

Natural language processing and machine learning as practical toolsets for archival processing

Archives and AI: An Overview of Current Debates and Future Perspectives

Record linking in the EHRI portal

Contact Info

Product

Resources

About