Showing posts with label text analysis. Show all posts
Showing posts with label text analysis. Show all posts

Oct 4, 2023

Comparing 1961 and May 30 2019 provenances for Manet's La Sultane in the Bührle collection

How many times did the provenance change?
Word/Phrase 1961 Catalogue Count 2019 Bürhle website Count
dealer 1 0
silberberg 1 9
? 0 2
art market 0 1
by 0 7
rosenberg 0 5
might 0 1
until 0 1
no documents 0 1
was never really and completely owned by Silberberg 0 1

Edouard Manet

Young Woman in Oriental Garb

ca. 1871

Dec 14, 2022

Art Provenance Texts as Data: Restitution Histories in Auction Catalogs

Adolph Menzel, restituted to the heirs of Walter Westfeld

When Nazi-looted artworks are restituted, families are often obliged to sell at least some of the artworks. For this they turn to auction houses. As a result, the provenances published by auction houses may provide valuable insights into the art market networks that handled the looted art until the time of restitution

Below is a selection of several provenances published online by Sotheby's. Some of the provenance texts indicate that artworks put up for auction had previously been restituted to the families of persecuted collectors. 

For the artworks that have been restituted, the provenances - before the restitution - offer clues to how the resale market for Nazi-looted art worked. Through which hands did the artworks pass? Do any names recur? 

Dec 7, 2022

How to use information in the provenance texts of Nazi looted art that has been restituted to find other Nazi-looted artworks

This Nazi-looted painting was restituted in 2016.

Often, when a painting is restituted, it is the conclusion of a long and arduous process of archival research to establish the itinerary of the painting and the different actors involved in its looting (or sale, or transfer, or translocation). 
What happens if we take the NAMES that appear in the provenance AFTER an artwork has left the possession of the persecuted Jewish owner and plug them in to some powerful digital tools to check other provenance texts for their presence?

Could this application of digital tools provide clues that lead to other Nazi-looted artworks?

Oct 18, 2022

Unnecessary mysteries

Signs that provenance research has gone off the rails include:

  • extremely long texts that leave the reader so confused that he/she concludes that there is no such thing as knowledge
  • massive use of words indicating uncertainty or unknowability
  • omission of crucial information that is available and which provides important context
  • mention of names which are known to be either persecuted Jewish collectors, Red Flag dealers of Nazi looted art, or associates with forgers WITHOUT mentioning that that's who they are
  • excessive speculation
  • false information

In this series of posts, we will examine a few remarkable examples of needlessly confusing provenance texts published by major museums. 


Aug 12, 2021

Jul 18, 2021

Looted Art Detector Tool: Swiss GLAMHACK2021

Objective: Identify high priority artworks for provenance research

Description: Online Free Digital Tool

Approach: Automatic text analysis using frequency counts

note: The frequency counts target textual indicators of UNCERTAINTY, UNRELIABILITY,  or ANONYMITY, as well as the possible presence of RED FLAG names related to NAZI-looted art, forced sales and duress sales. 
The resulting calculations do not signify that an artwork is looted. They simply quantify observations concerning the text for further analysis.

How it works: 

Jun 21, 2021

Let's run 1000 NEPIP provenances that contain Munich through the Looted Art Detector

In the previous post we gathered one thousand provenances of artworks listed (for the most part) by American museums on the Nazi Era Provenance Internet Portal that contain the word "Munich" or "München" in the provenance text.

In this post, using the Looted Art Detector developed at the Swiss Glamhack2020 and Glamhack2021, we rank artworks that contain a mention of Munich according to the criteria of "Uncertainty". 

Jun 5, 2020

DATASET: Text Analysis Challenge - Detect Looted Art - GLAMhack2020

Download  Provenance Texts Dataset: CSV 

Text Analysis Challenge: Detect Looted Art.

Help Automate Analysis, Flagging and Ranking of Museum Art Provenance Texts by the Probability of a Hidden History

The Question: How to sift through the millions of objects in museums to identify top priorities for intensive research by humans? 

The Goal: Automatically Classify and Rank 60,000+ art provenance texts by probability that further research will turn up a deliberately concealed history of looting, forced sale, theft or forgery. 

The Challenge: Analyse texts quickly for Red Flags, quantify, detect patterns, classify, rank, and learn. Whatever it takes to produce a reliable list of top suspects

For this challenge several datasets will be provided.

1) DATASET: 60,000+ art provenance texts for analysis

(example)[Pierre Matisse Gallery, New York, New York], by 1932, to M. Gutmann, 1936. Maurice Wertheim, by 1937, bequest, to Fogg Art Museum, 1951.;NOTE: Provenance derived from "Degas to Matisse: The Maurice Wertheim Collection," John O'Brian, Harry N. Abrams, New York, 1988.1951.76 Collector, Paris, Said to have been bought directly from artist, 1918.;Maurice Wertheim, Purchased from the Valentine Gallery, 1937, Bequest to Fogg Art Museum, 1951.1951.52 Guillaume, Paris, France, 1925, 1935. By 1930, per caption of photo, Paul Guillaume dining room dated c. 1930, Georgel 2006;Maurice Wertheim, 1937, Bequest to Fogg Art Museum, 1951.1951.51 owner, Paris, sold, [through Hôtel Drouot, Paris, June 16, 1906, no 30.]. Dr. Alfred Wolff, Munich, (1912). Sir Michael Sadler, England, (1912). [De Hauke & Co., New York], sold, to A. Conger Goodyear, New York, (1929-1937) sold, [through Wildenstein && Co., New York];to Maurice Wertheim, New York (1937-1951) bequest, to Fogg Art Museum, 1951.1951.49

2) DATASET: 1000 Red Flag Names


Bignou, EtienneBignouEtienneEtienne Bignou
Billiet, DirectorBillietDirectorDirector Billiet
Binder, Dr. Moritz JuliusBinderDr. Moritz JuliusDr. Moritz Julius Binder
Bing CollectionBing CollectionBing Collection
Birtschansky, ZacharieBirtschanskyZacharieZacharie Birtschansky
Bisson, E.BissonE.E. Bisson
Blanc, PierreBlancPierrePierre Blanc
Bleye, WilliBleyeWilliWilli Bleye
Bloch, Dr. VitaleBlochDr. VitaleDr. Vitale Bloch
Bloch-Bauer Collection
Bloch-Bauer Collection
Bloch-Bauer Collection
Bode, Dr.BodeDr.Dr. Bode
Bodenschatz, General KarlBodenschatzGeneral KarlGeneral Karl Bodenschatz
Boedecker, AlfredBoedeckerAlfredAlfred Boedecker
Boehler, Julius, Jr.BoehlerJuliusJulius Boehler
Boehler, Julius, Sr.BoehlerJuliusJulius Boehler
Boehm, Dr. FranzBoehmDr. FranzDr. Franz Boehm
Boehmer, BernhardBoehmerBernhardBernhard Boehmer

3) DATESET:  Red Flag Words or Phrases

likelyprivate collector?transfertelephone
probablyanonymous[removedto at least
possiblyart marketuntil at least
maybeunidentifiedby 19
?unknownbefore 19
property of a European collectoraccording to
private collection
property of a lady

You're the doctor and the texts are your patients! Who's in good health and who's sick? How sick? With what disease? What kind of tests and measurements can we perform on the texts to help us to reach a diagnosis? What kind of markers for should we look for? What kind of patterns? 

What digital methods can we use - and put into practice during the Hackathon - to "diagnose" these texts and prioritize them for "treatment" (ie, additional provenance research)?

  • IDENTIFY Red Flag Names and Words in each Text?
  • COUNT Red Flag Names and Words in each Text?
  • CHARACTERIZE each Text (number of words? sentiment? completeness v gaps? other features to be identified that may be useful)?
  • ANALYZE for patterns, links and networks?
  • CALCULATE probability that the provenance conceals a Nazi-era history that will prove problematic if investigated in detail
  • RANK according to urgency for further in-depth provenance research 

(Voyant-Tools Whitelist containing names: 
keywords-14ca7131716f24c62c6529fcc143bbd2  )

What might a successful result look like?

  • A list of 50 provenances from the DATASET ranked most likely to conceal looted art
  • A color-coded evaluation of each provenance (RED, ORANGE, GREEN) by likelihood of concealing looted art
  • Instructions how to analyze the provenances with the tools, functions or code to use  (for example, how to use Voyant-Tools to count all the Red Flag Names and inject the result back into the spreadsheet)
  • Ideas for going further....

Triage: "assignment of degrees of urgency to wounds or illnesses to decide the order of treatment of a large number of patients or casualties."

Link to Glamhack2020 project for participants

Help us to test and improve and test and improve the code!

Link to Code on Github

Issues to resolve:
- While the word list counts and general name extraction seem to work pretty well, reconciliation with the list of 1000 Red Flag Names still needs work. To test: A tighter tolerance combined with a more complete listing of alias might help.
- The extraction of transaction years after 1900 gives interesting though incomplete indicators. The decision to exclude dates in parentheses (as they are sometimes biographical dates and not transaction dates) needs to be reviewed and refined. 
(note: The texts are from multiple sources applying multiple formats and deliberately entered exactly as is)

Results RAW (with uncorrected errors for analysis)