A Method for Record Linkage with Sparse Historical Data Article Swipe
Massive digitization of archival material, coupled with automatic document processing techniques and data visualisation tools offers great opportunities for reconstructing and exploring the past. Unprecedented wealth of historical data (e.g. names of persons, places, transaction records) can indeed be gathered through the transcription and annotation of digitized documents and thereby foster large-scale studies of past societies. Yet, the transformation of hand-written documents into well-represented, structured and connected data is not straightforward and requires several processing steps. In this regard, a key issue is entity record linkage, a process aiming at linking different mentions in texts which refer to the same entity. Also known as entity disambiguation, record linkage is essential in that it allows to identify genuine individuals, to aggregate multi-source information about single entities, and to reconstruct networks across documents and document series. In this paper we present an approach to automatically identify coreferential entity mentions of type Person in a data set derived from Venetian apprenticeship contracts from the early modern period (16th-18th c.). Taking advantage of a manually annotated sub-part of the document series, we compute distances between pairs of mentions, combining various similarity measures based on (sparse) context information and person attributes.
Related Topics
Concepts
Computer science
Digitization
Information retrieval
Record linkage
Context (archaeology)
Annotation
Set (abstract data type)
Key (lock)
Process (computing)
Data science
Artificial intelligence
Geography
Demography
Programming language
Population
Computer vision
Archaeology
Operating system
Computer security
Sociology
Metadata
- Type
- article
- Language
- en
- Landing Page
- http://infoscience.epfl.ch/record/217508
- https://infoscience.epfl.ch/record/217508/files/short-paper-garzoniLinkage.pdf
- OA Status
- gold
- Cited By
- 1
- References
- 10
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2304573516
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2304573516Canonical identifier for this work in OpenAlex
- Title
-
A Method for Record Linkage with Sparse Historical DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2016Year of publication
- Publication date
-
2016-01-01Full publication date if available
- Authors
-
Giovanni Colavizza, Maud Ehrmann, Yannick RochatList of authors in order
- Landing page
-
https://infoscience.epfl.ch/record/217508Publisher landing page
- PDF URL
-
https://infoscience.epfl.ch/record/217508/files/short-paper-garzoniLinkage.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://infoscience.epfl.ch/record/217508/files/short-paper-garzoniLinkage.pdfDirect OA link when available
- Concepts
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Computer science, Digitization, Information retrieval, Record linkage, Context (archaeology), Annotation, Set (abstract data type), Key (lock), Process (computing), Data science, Artificial intelligence, Geography, Demography, Programming language, Population, Computer vision, Archaeology, Operating system, Computer security, SociologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2016: 1Per-year citation counts (last 5 years)
- References (count)
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10Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.contracts | 158 |
| abstract_inverted_index.different | 91 |
| abstract_inverted_index.digitized | 46 |
| abstract_inverted_index.distances | 179 |
| abstract_inverted_index.documents | 47, 61, 130 |
| abstract_inverted_index.entities, | 124 |
| abstract_inverted_index.essential | 109 |
| abstract_inverted_index.exploring | 21 |
| abstract_inverted_index.material, | 4 |
| abstract_inverted_index.mentions, | 183 |
| abstract_inverted_index.(16th-18th | 164 |
| abstract_inverted_index.annotation | 44 |
| abstract_inverted_index.historical | 27 |
| abstract_inverted_index.processing | 9, 74 |
| abstract_inverted_index.similarity | 186 |
| abstract_inverted_index.societies. | 55 |
| abstract_inverted_index.structured | 64 |
| abstract_inverted_index.techniques | 10 |
| abstract_inverted_index.attributes. | 195 |
| abstract_inverted_index.information | 121, 192 |
| abstract_inverted_index.large-scale | 51 |
| abstract_inverted_index.reconstruct | 127 |
| abstract_inverted_index.transaction | 34 |
| abstract_inverted_index.digitization | 1 |
| abstract_inverted_index.hand-written | 60 |
| abstract_inverted_index.individuals, | 117 |
| abstract_inverted_index.multi-source | 120 |
| abstract_inverted_index.Unprecedented | 24 |
| abstract_inverted_index.automatically | 142 |
| abstract_inverted_index.coreferential | 144 |
| abstract_inverted_index.opportunities | 17 |
| abstract_inverted_index.transcription | 42 |
| abstract_inverted_index.visualisation | 13 |
| abstract_inverted_index.apprenticeship | 157 |
| abstract_inverted_index.reconstructing | 19 |
| abstract_inverted_index.transformation | 58 |
| abstract_inverted_index.disambiguation, | 105 |
| abstract_inverted_index.straightforward | 70 |
| abstract_inverted_index.well-represented, | 63 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.70319992 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |