Presentation as held at the "Workshop on Knowledge Evolution and Ontology Dynamics" co-located with ISWC 2011. Related to the paper http://ceur-ws.org/Vol-784/evodyn1.pdf

1. Statistical Analysis of Web of
Data Usage
Towards (Visual) Maintenance
Support for Dataset Publishers
Markus Luczak-Rösch, Markus Bischoff
Freie Universität Berlin, Networked Information Systems (www.ag-nbi.de)

2. Who is addressed?
• rather small/simple ontologies
– min. effort for OE
– “under-engineered”
• unknown user requirements

3. We propose: A Usage-dependent Life
Cycle
Requests and
• RDB2RDF Queries • Re-engineering
• Crawling & • SELECT * WHERE ?t • Re-population
transformation a:madeOf a:Plastic •…
•… • SELECT * WHERE ?t
b:madeOf b:Wood
Negotiate
Initial Release
understanding
USAGE

4. (Very) Quick Example
• Out of which
instruments consists
The Beatles band?
• Are the Beatles a “Big Band”?
• What are “british” bands?

6. • Is it what the user expected
to see?
• Did you know that
this happens and
do you know what
to do now?

7. Survey covering approx.
25% of all cloud datasets
• size
• complexity
• engineering methodology
• …
 Publishers of most of the dataset do not
have any (structured) idea how to maintain
their data. Survey ran in October 2010, not yet
published officially

8. Role of the dataset publisher
(more general)
Effort Distribution between Publisher and Consumer
• use common
vocabularies
• provide RDF
Consumer generates/
links to other data mines links
resources Effort
• provide Distribution
schema Publisher provides Links as
links
mappings hints
Christian Bizer: Pay-as-you-go Data Integration (21/9/2010)
Source: Talk of Chris Bizer

9. Role of the dataset publisher
(more specific)*
• Reliability  Is the data valid and complete?
• Peak-load  Temporal profiles of important data?
• Performance  Are caches and indexes optimal?
• Usefulness  What do people find and use frequently?
• Attacks  Is the data threatened by spam?
* w.r.t. Möller et al.: Learning from Linked
Open Data Usage: Patterns & Metrics.

10. Our Usage-based Approach
digging in log files

11. How do people access resources on the Web of Data?
xxx.xxx.xxx.xxx - - [21/Sep/2009:00:00:00 -0600]
"GET /page/Jeroen_Simaeys HTTP/1.1"
200 26777 "" "msnbot/2.0b (+http://search.msn.com/msnbot.htm)"
xxx.xxx.xxx.xxx - - [21/Sep/2009:00:00:00 -0600]
"GET /resource/Guano_Apes HTTP/1.1"
303 0 "" "Mozilla/5.0 (compatible; Googlebot/2.1;
+http://www.google.com/bot.html)"
xxx.xxx.xxx.xxx - - [21/Sep/2009:00:00:01 -0600]
"GET /sparql?query=PREFIX+rdfs%3A+%...“
200 1844 "" ""
What do they get?
• RDF-Graphs
• SPARQL Query Results XML Format
• …, HTML, JSON, … serialization of results
• …, HTML, JSON, … serialization of no results
204 would be great but for now the usage
mining process should respect this 

12. Adapted from Myra Spilipoulou: “Web usage mining for
Web site evaluation”, 2000, Commun. ACM
Log
File Result Patterns
Instructions
Visualization Tool
Preparation Tool
Mining Query
Mining Results
Access Methods and Patterns
Navigation
Patterns
Queries Patterns Triples Filters Sessions
and Statistics
Sequences
Usage Mining
Methods
Prepared Log Data
Preparation Phase Mining Phase

13. Preparation Process
xxx.xxx.xxx.xxx - - [21/Sep/2009:00:00:01 -0600]
"GET /sparql?query=PREFIX+rdfs%3A+%...“
200 1844 "" ""
SPARQL Query Basic Graph
Log Entry Triple Pattern
Selection and Pattern
Extraction Selection
Validation Selection
Query Partitions Database
Query Partition
Query Partition Query Filter
Success
Re-Execution Evaluation
Determination

14. Usage Analysis
• queries
• patterns
• triples
• primitives
ns1:A
rdf:type
Reference for details: M. Luczak-Rösch and H. Mühleisen,
ns2:B "Log File Analysis for Web of Data Endpoints ," in Proc. of
the 8th Extended Semantic Web Conference (ESWC)
Poster-Session, 2011.

15. Metrics
• Ontology heat map • Resource usage
– the amount a class or – triple combinations in
a predicate is used in which a resource is
queries used
• Primitive usage
– position in triples
– triple combinations

16. Metrics
• Time statistics • Error statistics
– hourly accesses – triple patterns that
contradict the schema
but succeeded
• Hosts statistics – triples patterns that
– hourly accesses per fail due to the
host modelling
– primitives and triple
patterns requested by
host

17. Visualizations
network
• weighted nodes overview
and edges
(depending on
the applied
metric) represent
the amount of
usage zoom in and see
details

18. Evaluation Dataset
• Dbpedia 3.3 log files
– 1.700.000 requests from two randomly chosen
days (07/2009)
– analysis against a mirror of the 3.3 dataset
(inconsistent dataset)
– performance issues of dynamic network
visualization and reprocessing of queries 
limited number of analyzed logs

19. Starting Point for Visual Analysis

20. Resource Analysis

21. Predicate Analysis

22. Access Time and Hosts Analysis
All hosts Specific host

23. Hosts and Primitives Analysis
Specific host

24. Inconsitencies & Weaknesses
• ns:Band ns:instrument ?x
inconsistent • ns:Band ns:genre ?y
data
• ns:Band ns:associatedBand ?z
• ns:Band ns:knownFor ?x missing facts
• ns:Band ns:nationality ?y
•…
Complete analysis can be found at http://page.mi.fu-berlin.de/mluczak/pub/visual-analysis-of-web-of-data-usage-dbpedia33/

25. What to learn from usage analysis?
• ontology maintenance
– schema evolution
– instance population
– ontology modularization
– error detection
Image source http://mrg.bz/GgaxPB

26. What else to learn?
• performance scaling
– index generation
– store architecture based on frequent SPARQL
patterns
– hardware scaling at peak times
– modularization of data for different hosts

27. This is ok for the beginning but…
… SONIVIS can do more
 evaluate (with users!) various network visualizations
and find the best one for specific context

28. More for the Future
• Generic patterns for the metrics
+ resolution/evolution patterns
• Common sense of statistics
+ Quality-of-dataset index
Central conclusion:
• Temporal analysis Calculate statistics,
• Network metrics (degree,…) weaknesses and
inconsistencies first and
• Visualize the effects of change do visual editing
afterwards!
Image source: http://mrg.bz/8Co9lA

29. • usage-dependent life cycle support for
LOD vocabularies and the populated
instances
T A • (visual) usage analysis can help to plan
and perform maintenance activities
• this is a benefit for the dataset publisher
a w and the Web of data as a whole
k a
e y
Markus Luczak-Rösch (luczak@inf.fu-berlin.de)
Freie Universität Berlin, Networked Information Systems (www.ag-nbi.de) Image source: http://mrg.bz/jlObbL