BABY ELEPHÃT – BUILDING AN ANALYTICAL BIBLIOGRAPHY FOR A PROSOPOGRAPHY IN EARLY ENGLISH IMPRINT DATA

NUSHRAT KHAN

Oxford-Illinois Digital Libraries Placement Programme

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ABOUT EEBO-TCP- Collaboration between the universities of Oxford and

Michigan from 1999-2015

- Early English Texts between 1473-1700

- 25000 texts made available online

- Full text searching available through EEBO-TCP Database

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WHY HISTORIC TEXTS ARE INTERESTING

Historic Datasets

Accessibility

Reveal Historical

Information

Semantic Web

Technical Interoperability

Future Research

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WORKSET CONSTRUCTOREnables workset creation from Person, Place, Subject, Genre and Dates parameters (http://eeboo.oerc.ox.ac.uk/)

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HOW DOES IT WORK?

Metadata extracted from TEI Data Clean Up Link the Data

Workflow of Publishing Structured Metadata

Available Metadata Fields

• Title

• Author Name

• Date (Precise Birth, Precise Death, precise-floruit-from, precise-floruit-to, precise-floruit-to)

• Raw Publication Place

• Raw Publication Date

• Publisher

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SAMPLE PUBLISHER DATAPublisher

By Rycharde Iugge, printer to the Quenes Maiestie,

Printed by I[ohn] C[harlewood] for Iohn Hinde, dwelling in Paules Church-yarde, at the signe of the golden Hinde,

Printed by Benjamin Took and John Crook, and are to be sold by Mary Crook & Andrew Crook ...,

Printed by Peter Smith, and at Saint-Omer at the English College Press],

s.n.],

[By J. Charlewood] for Edward White, dwelling at the little North doore of Paules Church, at the signe of the Gunne,

Imprinted by Richard Field, and are to be sold by Richard Garbrand [, Oxford],

[By I. Jaggard?] for M. S[parke.,

Imprinted by E: G[riffin]: for Iohn Budge, and Ralph Mab,

By [J. King for?] Iohn waley dwellyng in Foster lane,

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INSIDE THE DATA

Work

Printed By

Sold At

Printed For

Sold By

Printed At

?

:

.

[ ]

,

…[ ]?

“”,.

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WORKFLOW

Data Cleaning

Named Entity Extraction (Person – Printed by, Printed for and Sold by)

Storing Triples and generate RDF

Happy Querying !

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ENTITY RECOGNITION APPROACHES

NLTK Entity Extractor

Regular Expression

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REVERB

For automatically identifying and extracting binary relationships from English sentences

Input Output

Argument1, Relation Phrase, Argument2Raw text

Bananas are an excellent source of potassium

(bananas, be source of, potassium)

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OPEN CALAIS

Not as efficient on short textsi.e. Printed by A. Bells

Input text too short

Example Sentence:Printed by Melchisedech Bradwood for William Aspley

Cannot detect as a person

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NLTK ENTITY RECOGNIZER

Step 1 Extracted all the entities labeled as PERSON for each sentence

work_000001|Rycharde Iuggework_000003|Paulswork_000004|Iohn Charlewoodwork_000004|Iohn Hindework_000005|Ioan Danterwork_000006|Francis Grovework_000007|Henry Godduswork_000008|Arthur Iohnsonwork_000012|Leonard Lichfieldwork_000013|Langly Curtiswork_000014|Benjamin Tookwork_000014|John Crookwork_000014|Mary Crookwork_000014|Andrew Crookwork_000015|William Keblewhite

All the entities NLTK can

extract for each record

(with some limitations)

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LIMITATIONS OF NLTK

• NLTK does not identify initials as names, i.e A. B.

• Extracts only the surname in the expressions like A. Bells, Edw: Allde

• Identifies the word “Printer” in sentences where it’s mentioned in capital letters after ‘by’. i.e Printed by John Bill, Printer to the King's most Excellent Majesty

• In case of complex sentences containing multiple names it cannot detect and extract all the names efficiently

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FINDING RELATIONSHIPS WITHIN SENTENCES("'Printed", 'JJ')('by', 'IN')(PERSON Benjamin/NNP Took/NNP)('and', 'CC')(PERSON John/NNP Crook/NNP)('and', 'CC')('are', 'VBP')('to', 'TO')('be', 'VB')('sold', 'VBN')('by', 'IN')(PERSON Mary/NNP Crook/NNP)('&', 'CC')(PERSON Andrew/NNP Crook/NNP)

Look for preceding

preposition

Separate the entities based on

‘by’ or ‘for’

“You're having a hard time because it's hard. This is really not an easy task to approach. – jonrsharpe Jul 31 '14"

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DATA REFINING

Printed & Sold by Sold

ByPrinted

By

Printed For

De-duplicate the ‘Sold by’ Put back the

ones in ‘Printed and Sold by’

Extracted separately using ‘Regex’

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GENERATING UNIQUE URI

Ideal case : Assign unique URI to the same person

Exception in this case:

• Few authoritative sources to refer to

• Time consuming validation

• Very limited information about each person available

Assigned unique URI to every instance

Python uuid module – uuid4() function

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WORKING WITH ONTOLOGY

Checked existing ontologies for ‘Printed by’ and ‘Printed for’ relationships --- MODS, MADS, BibFrame etc

EEBOO Ontology

Modify the existing ontology to define the new relationships

Work

Author

Printed By

Printed For

Sold By

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STORING TRIPLES AND GENERATING RDF

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QUERYING ON THE DATA 1Top 20 Publishers Top 20 Printed for Top 20 Sold By

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QUERYING ON THE DATA 2

The sellers for the works published by Henri Hills

Both Printed and Sold by Henri Hills

Sellers who worked with Henri Hills-Will Larner, Jane Underhill, Francis Smith

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FUTURE DIRECTION

• Train NLTK to capture the names properly

• Extract specific place names from the publisher field. i.e. sold at Golden Hinde

• In case of initials figure out how to identify the names, i.e. whether R. Charles is Robert Charles or Ruth Charles etc. May be request help from domain expert

• Analyze how name expressions have changed over time

• Identify the authors using authoritative sources and domain specific knowledge, i.e. London Book Trades Index, British Book Trade Index

• Analyze and visualize the data by mapping

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GRATITUDE

• Terhi Nurmikko-Fuller

• David M. Weigl

• Professor David De Roure

• Kevin Page

• Pip Willcox

And everybody else at OeRC!