1. Stephen Brown Museums and the Web Asia 9-12 December 2013 Hong Kong
Where are the pictures? Linking photographic records across collections using fuzzy logic
Stephen Brown
Museums and the Web Asia
9-12 December 2013 Hong Kong

2. Research question
Can fuzzy logic based data mining algorithms be used to identify matches between different online collections?
Answer: yes

13. erps16578
[Royal Museum, the court (i.e. Bargello Museum, the courtyard), Florence, Italy]
No person listed
1 photomechanical print : photochrom color.
[between ca and ca. 1900].
From the Library of Congress
The Courtyard of the Bargello, Florence
Henry Little
Bromide (Print)
1895
From the ERPS collection 13

17. Method
Data preparation (correcting typographical errors, standardizing data such as dates, removing duplicate entries and mapping data to a common metadata schema).
Data aggregation (combining standardized records in a single XML database where they can be mined for similarities).
Query expansion (extending the range of keywords that are searched for).
Field comparison (comparing the contents of individual fields and combining these to produce an overall similarity metric).

18. Alternative computing logics
Classic logic is binary
True/False zero/one
Set theory
Fuzzy logic
Degrees of truth
Fuzzy set theory 18

19. The concept of tall people
Ben Youngs 5’10”
Toby Flood
6’2”
Geoff Parling 6’6” 19

20. The concept of tall people
Classical approach:
Any one over 6”is tall
Ben Youngs 5’10”
Toby Flood
6’2”
Geoff Parling 6’6” 20

21. The concept of tall people
Classical approach:
Any one over 6”is tall
Ben Youngs 5’10”
Toby Flood
6’2”
Geoff Parling 6’6” 21

22. Classical computing The membership function of the set tall people 15” 6” 7”
Toby Flood 6’2”
Ben Youngs 5’10”
Geoff Parling 6’6” 22

23. The concept of tall people
Fuzzy approach:
Everyone is tall to some degree (as measure by the membership function)
Ben Youngs 5’10”
Toby Flood
6’2”
Geoff Parling 6’6” 23

24. Fuzzy computing The membership function of the set tall people 1 5” 6”5” 6” 7”
Toby Flood 6’2”
Ben Youngs 5’10”
Geoff Parling 6’6” 24

25. Soft computing The membership function of the set tall people 1 5” 6”
0.95
0.7
0.45 5” 6” 7”
Toby Flood 6’2”
Ben Youngs 5’10”
Geoff Parling 6’6” 25

26. Fuzzy computing Allows for vagueness in concepts Soft boundaries
Partial degrees of truth 26

27. Lightweight Semantic SimilarityB
Chrysanthemum 1
Flower
A. Chrysanthemum
B. Flower

28. Lightweight Semantic SimilarityB
Chrysanthemum 1
Flower
Chrysanthemum
Cosine of the angle between A and B = 0
Therefore, no similarity between A and B
Flower

29. Lightweight Semantic Similarity
Chrysanthemum
Flower

30. Lightweight Semantic Similarity
Fuzzy term vectors using synset similarity values from WordNet
Chrysanthemum
Cosine of the angle between A and B > 0
Therefore, some similarity between A and B
Flower

31. Combined similarity metric
IF title is good AND person is good THEN match is good.
IF title is good AND (date is good OR process is good) THEN match is ok.
IF person is good AND title is bad THEN match is ok.
IF title is bad AND person is bad THEN match is bad.

32. Conclusion
Large numbers of small amounts of text are common in collections records.
Text volumes too small for corpus linguistics analysis.
Need for query expansion
Text volumes too small for established Semantic Similarity analysis
Lightweight semantic