Experiments with evolving RDF
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The document discusses experiments with evolving RDF data to address challenges in data preservation, such as version retrieval and storage efficiency. It explores various strategies for archiving data versions, including storing deltas and annotations to track the validity of data over time. The findings illustrate the complexities of maintaining entity resolution amidst evolving data, particularly with the use of blank nodes, and offer insights into improving space efficiency in RDF datasets.
![Annotation of RDF
Tom
cat
has
tuna
eats
Tom
cat
has
Apr 1
dies
Tom
dog
has
dog
food eats
Version 1 Version 2 Version 3
Archive
Tom
cat
has [1–2]
tuna
eats [1–1]
Apr 1
dies [2–2]
dog
has [3—]
dog
food
eats [3—]](https://image.slidesharecdn.com/5-slawekstaworko-experimentsevolvingrdf-141104061000-conversion-gate02/85/Experiments-with-evolving-RDF-4-320.jpg)
![Deblanking
_b1
7 end
_b2
3
_b3
5
LISP-style encoding
list of numbers [5,3,7]
head
head
head
tail
tail
tail
#(7,end)
7 end
_b2
3
_b3
5
head
head
head
tail
tail
tail
#(7,end)
7 end
#(3,7,end)
3
_b3
5
head
head
head
tail
tail
tail
#(7,end)
7 end
#(3,7,end)
3
#(5,3,7,end)
5
head
head
head
tail
tail
tail
Assumption: graph has no cycles consisting of blanks only
Assumption: identity of a blank node is determined by its contents](https://image.slidesharecdn.com/5-slawekstaworko-experimentsevolvingrdf-141104061000-conversion-gate02/85/Experiments-with-evolving-RDF-10-320.jpg)
![Improving space efficiency
Peter
Edinburgh +44 712 4567
phone [1–10]lives [1–10]
Peter
Edinburgh +44 712 4567
phonelives
[1–10]Lift common intervals to subject
dog
has [1–5]
dog
has [1–5]
• Intervals moved from all but 33.7k triples (of total 285k)
• Number of subjects with histories is 34.3k
• Total number of intervals is reduced from 285k to 60k
• The size of the index reduced by almost 80%](https://image.slidesharecdn.com/5-slawekstaworko-experimentsevolvingrdf-141104061000-conversion-gate02/85/Experiments-with-evolving-RDF-13-320.jpg)
Experiments with evolving RDF
- 1. Experiments with evolving RDF Sławek Staworko (joint work with Peter Buneman) University of Edinburgh
- 2. Preservation of evolving data Tom cat has tuna eats Tom cat has Apr 1 dies Tom dog has dog food eats Version 1 Version 2 Version 3 … Archive • Version retrieval • Timeline queries • Storage space efficiency
- 3. Approaches to data preservation • Store all versions • Store the original databases and log the changes • Hybrid approach of the above two • store the initial and every 10th version • store log changes for the intermediate versions • Annotation based approach! • never delete data but annotate its validity with time intervals
- 4. Annotation of RDF Tom cat has tuna eats Tom cat has Apr 1 dies Tom dog has dog food eats Version 1 Version 2 Version 3 Archive Tom cat has [1–2] tuna eats [1–1] Apr 1 dies [2–2] dog has [3—] dog food eats [3—]
- 5. What exactly is the input? Delta = difference between two databases expressed with two atomic operations: inserting a triple and deleting a triple Tom cat has tuna eats Tom cat has Apr 1 dies Tom dog has dog food eats delete (cat, eats, tuna) insert (cat, dies, Apr 1) delete (Tom, has, cat) insert (Tom, has, dog) inset (dog, eats, dog food) delete (cat, dies, Apr 1) Snapshots Deltas Snapshots = complete database instances
- 6. Challenges in preserving evolving data with annotations 1. The task is relatively simple if deltas are know:! • deleting a triple closes its interval! • adding a triple opens a new interval ! 2. It gets complicated when only snapshots are given! • it boils down to computing deltas! • main challenge: identify objects that are the same across versions of the database Entity resolution problem! which data object represent the same entity across different versions! well-studied database problem in various different settings (from duplicate elimination to record matching)
- 7. Entity resolution and RDF URI (Uniform resource identifier) URIs are supposed to make things easy but… • RDF has also blank nodes • URIs don’t exactly solve the problem in the context of evolving/merged ontologies… Two different RDF nodes need not represent different objects
- 8. Blank nodes • LOD initiative frowns upon them • Blank nodes are commonplace (and misused?) Tom cat has Peter believes Tom cathas Peter believes _bsubject pred object _b 2.4 -0.4 Reification Complex number
- 9. Blank nodes (cont.) 1. Reification (Peter believes that Tom has a cat) 2. Data structures (complex types) 3. Anonymization (Tom has a pet) Assumptions on reasonable use of blank nodes:! 1. Represent concrete objects ! 2. The objects can be identified from the context
- 10. Deblanking _b1 7 end _b2 3 _b3 5 LISP-style encoding list of numbers [5,3,7] head head head tail tail tail #(7,end) 7 end _b2 3 _b3 5 head head head tail tail tail #(7,end) 7 end #(3,7,end) 3 _b3 5 head head head tail tail tail #(7,end) 7 end #(3,7,end) 3 #(5,3,7,end) 5 head head head tail tail tail Assumption: graph has no cycles consisting of blanks only Assumption: identity of a blank node is determined by its contents
- 11. Experiements • 10 versions of Experimental Factor Ontology (EFO) data expressed in OWL • 200k triples in the 1st version, 290k in the last • On average 20k blank nodes in each version • 920k triples overall (blank nodes are independent) • many triples do not last more than 1 version
- 12. Experiment Deblanking and life expectancy of an object Round Triples Blanks Life expect. 0 921896 165935 2.55 1 358857 33253 6.39 2 348356 28150 6.57 3 339695 23502 6.88 4 330564 18862 7.10 5 318761 14763 7.24 6 311562 11021 7.39 7 304628 7299 7.54 8 297744 3622 7.83 9 285484 58 7.83 10 285334 2 7.83 11 285334 1 7.83 12 285334 0 7.83
- 13. Improving space efficiency Peter Edinburgh +44 712 4567 phone [1–10]lives [1–10] Peter Edinburgh +44 712 4567 phonelives [1–10]Lift common intervals to subject dog has [1–5] dog has [1–5] • Intervals moved from all but 33.7k triples (of total 285k) • Number of subjects with histories is 34.3k • Total number of intervals is reduced from 285k to 60k • The size of the index reduced by almost 80%
- 14. Future: • Bisimulation • Nested RDF
- 15. Conclusions • Annotation offers an attractive way of representing an evolving RDF dataset (need for nested RDF?) • Evolution of data may require more complex atomic operations. For instance, vocabulary evolution: adding, splitting, merging classes. (can bisimulation help here?)