[pd-calculators] Fwd: Computing Copyright (or Public Domain) Status of Cultural Works

[Jonathan Gray]

---------- Forwarded message ----------
I've just put a post about computing copyright (or PD) status:

<http://www.rufuspollock.org/2009/03/12/computing-copyright-or-public-domain-status-of-cultural-works/>

It includes an example of computing copyright status for a set of 64k
items using the OL database. It also discusses the basic item-to-work
matching and author matching problems that are important not just for
PD calculation but generally -- for example in generating works from
items (something I know OL is currently doing).

I've excerpted some of the post below. I'd be interested to hear
people's comments.

I can also provide a more detailed list of the failed matches/false
negatives that I recorded when doing matching against the OL data.

Regards,

Rufus


## The Post

### Determining Copyright (or Public Domain) Status

With our terminology in place determining copyright status is, in
theory, simple:

 1. Given information on an item match it to a work (or works).
 2. For each work obtain relevant information such as date *work*
first *published* (as an *item*) and death dates of author(s)
 3. Compute copyright status based on the copyright laws for your jurisdiction.

While copyright law is not always simple, step three is generally
fairly straightforward, especially if one is willing to accept
something that almost but not quite 100% accurate (say 99.99%
accurate).

What is not so straightforward are the first two steps especially step
1. This is because most datasets give only a limited amount of
information on the items they contain.

Frequently information on authors will be limited or non-existent, and
they certainly may not be unambiguously identified (this is especially
true of datasets containing 'commercial' information such as prices
and availability). Often the exact form of the title, even for the
same item will vary between datasets and that leaves aside the
possibility of varying titles for different titles related to the same
work (is it "Hamlet" or "William Shakespeare's Hamlet" or "Hamlet by
William Shakespeare" or "Hamlet, Prince of Denmark" etc).

At the same time, speed matters because the size of the datasets
involved are fairly substantial. For example, there were approx 64
thousand titles that sold more than 5 copies in 2007 in the UK. If
computing public domain status for each title takes 1 second then a
full run will take 18 hours. If it takes 30s per title it will take 22
days.

### Some Examples

To illustrate the difficulties here we present the results of two
different attempts at computing the PD status for the list of 64k
titles which sold at least 5 copies in the UK in 2007.

#### Example 1: Open Library

We ran [this algorithm][1] (by_work method) against the Open Library
database via their web api. This was a very slow process. First,
because web apis are relatively slow and second because, perhaps due
to overloading, the OL API would stop responding at some point and a
manual reboot would be required (to try avoid overloading the API we'd
already added a significant delay between requests -- another reason
the process was quite slow). Overall it took more around 10 days to
run through the whole 64k item dataset. The results were as follows:

[1]:http://knowledgeforge.net/pdw/hg/file/tip/pdw/openlibrary.py

   Total PD: 2206.0
   Total Items: 63937
   Fraction PD: 0.0345027136087
   Total Matched: 0.588469900058

As this shows matching was not that successful with only around 3/5 of
items successfully matched. Part of this may be due to the fact that
we limit the number of title matches to 10 in order to keep the time
within reasonable bounds as well as the difficulty of allowing enough,
but not too much, fuzziness in the matching process.

Overall, approximately 3.5% of all items were identified as PD (that
being 5.8% of those actually matched). The PD determination algorithm
was a conservative one (an item was PD only if all authors were
positively identified as PD), so these are likely to be lower bounds
(at least assuming the match process was reasonable -- and allowing
for fact that some PD items included non-PD material such as
commentaries).

### Example 2

Our second algorithm ran against a local copy of Philip Harper's
NGCOBA database ([data][ngcoba], [code][ngcoba-code]). The algorithm
was as follows:

[ngcoba]:http://www.kingkong.demon.co.uk/ngcoba/ngcoba.htm
[ngcoba-code]:http://knowledgeforge.net/pdw/hg/file/tip/pdw/getdata/ngcoba.py

 1. Matched by title and authors.
   * If match: compute PD status strictly (all death dates known and
all less than 1937)
   * Else: continue
 2. Pick first author and find all (approx) matching authors (allow
extra first names)
   * If no match: Not PD
   * Intialize PD score to 0
   * For each matched author alter score in following manner:
     * If author PD: +1
     * If not PD: -3
     * If unknown (no death_date) -0.5
   * PD if score > 0 (Else: Not PD)

This algorithm took a few hours to run (this could likely be much
improved with a bit of DB optimization and a move from sqlite to
something better). The results were:

   Total PD: 6404.0
   Total Items: 63917
   Fraction PD: 0.100192437067

As can be seen the fraction PD here was substantially higher at around
10%. One might be concerned that this was due to our more lenient PD
algorithm (the problem was that without such 'leniency' a very large
number of PD works/authors were being misclassified as not PD).
However, basic eye-balling indicates that the number of false
positives is not particularly high (and that there are also some false
negatives).

## Summary

 1. Computing PD status is non-trivial largely because a) it is hard
to match a given item to a work or person b) we lack data such as
authorial death dates and dates of first publication that are
required.
 2. As such we need to adopt approximate and probabilistic methods
(such as the scoring approach)
 3. (Very) preliminary calculations suggest that between 3 and 10% of
titles actively sold at any one time are public domain
   * NB: this does not mean 3-10% of *sales* were public domain (in
fact this is very unlikely since few, if any of the best-selling items
are PD)

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-- 
Jonathan Gray

Community Coordinator
The Open Knowledge Foundation
http://www.okfn.org