Talk given by fellow Claus Stadler at the 11th International Conference on Semantic Systems - SEMANTiCS 2015 Paper available here: http://jens-lehmann.org/files/2015/semantics_dbtax.pdf

1. Unsupervised Learning
of an
Extensive and Usable
Taxonomy
for DBpedia
Presented by Claus Stadler
Vienna, 17th September 2015
Marco Fossati, Dimitris Kontokostas, and Jens Lehmann

2. Outline
I.Motivation
II.Research Contribution
III.Problem & Solution
IV.Approach
V.Results & Evaluation
VI.Advantages & Drawbacks
VII.Conclusion & Future Work
2

3. {1}
Motivation
3

4. DBpedia
General-purpose Knowledge Base
Central hub for Linked Data

5. Heterogeneous granularity:
Lack of coverage: 2.8 M typed resources out of 4.9 M
Wikipedia Category System
Chaotic: cycles
Too high granularity: "Radio Stations in Traverse
City, Michigan"
DBpedia ontology (DBPO)
Organisation
Band
SambaSchool
???

6. {2}
Research Contributions
6

7. Exhaustive type coverage
Focus on usability
Replication across language chapters
Unsupervised approach
7

8. {3}
Problem & Solution
8

9. Problem
Untyped resources in DBpedia (coverage)
Total entries = 4.9 million
Typed entries = 2.8 million
Unbalanced DBPO
9

10. Solution
Wikipedia Category System
Almost complete knowledge backbone
Identify a prominent subset
Learn a type taxonomy
10

11. {4}
Approach
11

12. 1.Leaf Node Extraction
2.Prominent Node Discovery
3.Type Taxonomy Generation (T-Box)
4.Pages Type Assignment (A-Box)
12

13. Stage 1:
Leaf Node Extraction
INPUT = cyclic graph; OUTPUT = tree
Bottom-up approach: from leaves to the root
Extract categories linked to actual articles only
Set of categories with no sub-categories =
Leaf Nodes Set:
13
Inuit_deities
Ugandan_monarchies
Inuit_goddesses

14. Stage 2:
Prominent Node Discovery
(A)Leaf Graph Traversal
(B)Natural Language Processing for is-a relations
(C) Interlanguage Links Weight
14

15. Stage 2A:
Leaf Graph Traversal
INPUT = leaf nodes set
For each leaf L :
Get parents;
For each parent P :
Are all its children leaves?
YES: P is a prominent node
NO: L is a prominent node
15
Inuit_goddesses
Inuit_deities
Ugandan_monarchies

16. Stage 2B:
NLP for is-a relations
Category = Noun Phrase (NP)
HEAD extraction
Shallow syntactic parsing
Is the HEAD plural?
YES: class candidate;
Depluralize
16
Deity Monarchy

17. Stage 2C:
Interlanguage Links Weight
The more interlanguage links a category has, the more it
is used across language editions
Prune categories with interlanguage links < Threshold
Threshold = 3
17
Inuit_deities Ugandan_monarchies

18. Stage 3:
T-Box
Cycle removal
Breadth-first, top-down
Short paths
Instance pruning
18
Monarchy < owl#Thing

19. Stage 4:
A-Box
INPUT = prominent nodes heads
For each prominent node head H :
Extract the category set with head = H
Extract the page set for each category ;
For each page P :
Is it an article page?
YES: < P, instance-of, H >
NO: Repeat until it is
19
Monarchy
Bengal_Sultanate
instance-of

20. {5}
Results & Evaluation
20

21. Data
T-Box
(classes)
A-Box
(assertions)
Typed
resources
1,902 10,729,507 4,260,530
21

22. Coverage
System Ratio
DBPO 0.513
MENTA 0.537
SDType 0.147
YAGO 0.673
WiBi 0.794
DBTax 0.994
22

23. T-Box Evaluation:
Settings
50 random classes
10 evaluators (peers)
Resource namespaces hidden to avoid bias

24. T-Box Evaluation:
Questions (1/2)
• “Is this a class or an instance?”
Restaurant VS Puella_Magi_Madoka_Magica (movie)
• “Can this class be broken down into more than one
class?
Mountain VS Musical_groups_from_Gothenburg
• “Is this a valid class hierarchy path?”
wikicategory_Golden_Bear_winners < yagoLegalActorGeo <
owl#Thing

25. T-Box Evaluation:
Questions (2/2)
• “Is this hierarchy too specific?” (too many levels)
Porter_County,_Indiana < Chicago_metropolitan_area <
Metropolitan_areas_of_Illinois < Populated_places_in_Illinois <
owl#Thing
• “Is this hierarchy too broad?” (very few levels)
Gonorynchiforme (fish family) < owl:Thing

26. Resource Classes
Non-
breakable
classes
Valid
paths
Not too
specific
paths
Not too
broad
paths
DBPO 0.66 0.67 0.89 0.97 0.84
YAGO 0.90 0.38 0.81 0.55 0.93
WiBi 0.75 0.38 0.73 0.41 0.85
Wikidata 0.19 0.48 0.85 0.66 0.88
Wikipedia 0.81 0.29 0.66 0.77 0.78
DBTax 0.65 0.76 0.77 0.98 0.40
T-Box Evaluation: Results
26

27. A-Box Evaluation:
Settings
Crowdsourced to the layman
Evaluation set: 500 random entities with no type in
DBpedia
5 judgments per entity
Prevent a worker from answering the same question
twice

28. A-Box Evaluation:
Test Questions
Automatically discard untrusted judgments
Untrusted worker: < 80% correct test questions
Subjective task: missed test questions
They affect # of untrusted judgments
The class label may be ambiguous

30. Resource Precision Recall F1
Untrusted
judgments
Wikidata 0.808 0.982 0.886 1,847
MENTA 0.793 0.589 0.675 1,093
SDType 0.924 0.098 0.178 1,723
YAGO 0.461 0.727 0.565 1,358
WiBi 0.858 0.597 0.704 2,075
DBTax 0.744 1 0.853 518
A-Box Evaluation: Results
30

31. {6}
Advantages & Drawbacks
31

32. Advantages
Exhaustive coverage (almost 100%)
Type coverage comparison
Recall in A-Box evaluation
Intuitive
Crowdsourced (the layman) A-Box evaluation
Least # of untrusted judgments
32

33. Drawbacks
Short hierarchy paths
Cycle removal
Instance pruning
Relatively low precision
NLP may still yield "weird" is-a relations
"Elvis Presley is a Burial"
33
!!!

34. {7}
Conclusion & Future
Work
34

35. Conclusion
Significant type coverage leap
Intuitive for end users
Balance between DBPO (too generic) and YAGO (too
specific)
Integrated in the latest DBpedia release
35

36. Future Work
Merge the T-Box into mappings.dbpedia.org for
curation
Word Sense Disambiguation for homonymous classes
Multilingual deployment (currently English and Italian)
36

37. http://it.dbpedia.org/resource/Elvis_Presley
DBTax in action
at the Italian DBpedia chapter
37

38. Thanks for your
attention!
Download DBTax at:
http://downloads.dbpedia.org/current/core-i18n/en/
Browse the Italian DBTax at:
http://it.dbpedia.org/sparql
Contact the first author at:
fossati@fbk.eu