Patentee Name Harmonisation - ecoom.be

6 DIRECTIONS FOR FURTHER DEVELOPMENT
6.1 Approximate string searching
Approximate string searching aims to identify patentee names that are slightly different
but might, in fact, be the same. Rather than applying exact string matching, distance measures
are used based on the similarity between the strings, However, it will become apparent that one
is confronted with a trade-off between accuracy and completeness: identifying similar patentee
names might result in matching unrelated patentee names and vice versa.
Several algorithms are available (bigrams, trigrams) but using the Levenshtein measure
(edit-distance measure), which computes string similarity, can be considered an appropriate
and easy-to-implement starting point (see for an example of an implementation approach, the
AGREP tool 11 ). When using the Levenshtein distance, a string P is said to be at distance k to a
string Q if P can be transformed to equal Q with a sequence of k insertions of single characters in
(arbitrary places in) P, deletions of single characters in P, or substitutions of characters 12 .
Using this measure, all patentee names within a certain distance of a given patentee
name can be identified.
However, implementation is not straightforward because of the following issues:
• Performance: approximate string searching is not as fast as exact string searching.
Moreover, if a list has to be matched with itself (e.g. one wants to compare all names in a
list with all other names in that list), processing time will increase exponentially with the
number of items in the list (a list of 100 items requires 10,000 distances to be calculated
and assessed; a list of 1,000 items requires 1,000,000 calculations);
• Large output: approximate string searching can produce a long list of matched strings.
Moreover, if a list is matched with itself, numerous duplicates will appear in the matching
list (if a string matches 50 other strings, the same combinations will appear 50 x 50 times).
• Relevance of error: if the Levenshtein distance is used, the acceptable distance is dependent
on the length of the string. For a string of 3 characters, name variants with a Levenshtein
distance of 1 will in most cases result in false hits (e.g. NEC, NAC, NIC, NEI, and NEA). A
minimum string length might be required.
• Validation: even if a small Levenshtein distance between two larger strings is observed, this
does not automatically mean that the underlying names are identical. A difference of one
character can result in significantly different names. In order to be accurate, additional
validation efforts need to be undertaken.
In view of these issues, applying approximate string searching in an automated way for
all names within the name list has not been included in the outlined method. At the same time,
approximate string searching offers significant potential especially for longer names, as the
following illustrations demonstrate. Implementing such efforts does however imply extensive –
manual – validation efforts before they can become automated.
Table 2 contains the name variations of “INTERNATIONAL BUSINESS MACHINES”
identified with approximate string searching (legal form indications were removed first and
resulting names were condensed).
Table 2: “INTERNATIONAL BUSINESS MACHINES” name variations identified
by approximate string searching
IBMCORPINTERNATIONALBUSINESSMACHINESCORP
IBMCORPORATIONINTERNATIONALBUSINESSMACHINESCORPORATION
IBMINTERNATIONALBUSINESSMACHINES
11
S. Wu and U. Manber, “AGREP -- A fast approximate pattern-matching tool”, proc. Winter 1992 USENIX
Technical Conference
12
Variants of this logic introduce weights for the different operations; e.g. deletions 3, insertions 2, and
substitutions 1.
10