Mining SPARQL queries to understand the behavior of au- tomated programs (or machine agents) is an important step in designing systems for the semantic web. We present techniques that differ from state-of-the-art SPARQL mining techniques in two ways: 1. Move away from one SPARQL query at a time view to SPARQL user session view 2. Look at the results of SPARQL queries in addition to the query itself. Due to these two approaches, we are able to find two new patterns in SPARQL queries that help us reason better about the underlying program that generated the SPARQL queries. Through a variety of experiments, we show that the patterns found have significant support in all the four datasets provided by the USEWOD committee.

1. Characterizing Machine Agent
Behavior through SPARQL Query
Mining
Aravindan Raghuveer
Yahoo! Inc, Bangalore.
aravindr@yahoo-inc.com

2. Introduction: LOD Users
 The LOD cloud has two types of users
- Humans (browsers).
- Programs / machine agents.
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3. Introduction: LOD Access Methods
 The data on the LOD cloud can be accessed
in multiple ways.
 For this work, we categorize them into two
buckets:
- SPARQL : A powerful declarative graph query
language
- Non-SPARQL: Direct linked data requests.
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4. Motivation: User Behavior Understanding
 Deep Understanding of client behavior can help
build “better” serving systems
 Better:
- Secure
- Scalable
- Available
 Prior Work:
- Moller et al , WebSci 2010
- Picalausa et al. Swim 2011
- Kirchberg et. al Usewod 2011
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5. Summarizing. . .
Human Users Machine Agents
Non-SPARQL
SPARQL This paper’s
focus
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6. What this paper is about?
 Mining of the USEWOD query log dataset to
identify:
- Two Trends in Machine Agent Querying
- Two Patterns in Machine Agent Querying
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7. The USEWOD dataset
 Query logs of servers hosting a part of LOD cloud data.
Type # records % SPARQL
(million)
bio2rdf Life sciences ~ 0.2 100%
lgd Geo ~ 1.9 100%
SWDF Conference ~ 16.7 43.38%
dbpedia Structured ~ 36.2 46.9%
wikipedia
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8. Part-1: Two Trends in Machine Agent
Querying
The Theme
“What are the overarching trends for
SPARQL queries?”
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9. Trend-1: SPARQL is here to stay!
0.1 – 1million
SWDF Dbpedia
Take-away: SPARQL query volume is pretty
significant
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10. Trend-2: SPARQL is heavily used by machine
agents.
Took 17 million user agents from SPARQL queries from dbpedia
and..
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11. Part-2: Two Patterns in Machine Agent
Querying
The Theme
“Looking at SPARQL query logs, can we reason
about the program that generated the queries?”
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12. Salient aspects of proposed Query Mining
Techniques
 Move from per query analysis to query session
analysis
 Move from query analysis to query result analysis
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13. Pattern -1 : Loops in Programs
Take-away
• Through a per-user, temporal mining of logs, we
discover patterns that are caused by loops in
program.
• Significant support in all 4 datasets
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14. Per-user Temporal mining
TIME
Loop
Original Logs
User level Session Analysis
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User-1 User-2 User-3 User-4

15. Intra Pattern Loop
 successive queries from the same user, use the
same “template”
 Example: Two successive queries:
SELECT * WHERE {http://bio2rdf.org/dr:D00332
http://bio2rdf.org/ns/bio2rdf#xRef
http://bio2rdf.org/cas:54-47-7}
SELECT * WHERE{http://bio2rdf.org/dr:D00333
http://bio2rdf.org/ns/bio2rdf#xRef
http://bio2rdf.org/cas:54-47-7}
 Only the subject (D00332,D00333) varies
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16. Detecting Intra Pattern Loop
 We convert a query to its canonical form by
replacing variables, URI and literals by
“keywords”.
SELECT * WHERE {http://bio2rdf.org/dr:D00332
 Canonical Form of the previous queries: SELECT *
http://bio2rdf.org/ns/bio2rdf#xRef
http://bio2rdf.org/cas:54-47-7}
WHERE { _URI_ _URI_ _URI_ }
 Queries generated by the same template will have
the same canonical form.
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17. Salient Aspects of Intra Pattern loops
 Iterate over a dictionary of values (categorical)
 Iterate over a numerical range (example LIMIT,
OFFSET parameters in SPARQL queries)
 Multiple levels of nested loops with the same
intra loop pattern.
 4 Parameters to quantify above (in paper)
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18. Inter Pattern Loops
 Found loops that iterate over a set of patterns
P1,P2,P3 ,P1,P2,P3 ,P1,P2,P3
 Typically used when the output of the first query
goes as a parameter to the second query.
(examples in paper)
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19. Results
86% 32%
Take-away:
bio2rdf Significant support
40% for loops! lgd
16%
swdf dbpedia 19
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20. Pattern-2: Querying for dbpedia Linkage
Take-away:
• By executing each query
• analyze the results, we find that a portion of
queries “look” for dbpedia links
• Results:
- 20 months of SWDF queries had average of 8% look
for dbpedia urls
- 2 days worth of lgd queries had 26.5% queries look
for dbpedia urls
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21. Summary & Conclusions
 Proposed 2 new ways of SPARQL query mining:
- Session view
- Analyze results in addition to query
 Showed that machine agents look for dbpedia using the
owl:sameas annotation.
 Influence on system design:
- Can we pre-fetch elements in loop beforehand?
- Priortitize dbpedia attributes for caching
 Influence on log collection & analysis:
- Stratified random sampling to remove effect of loops.
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22. For the great data !!
For the great feedback & comments
For listening!
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23. The famous LOD Cloud . . .
7 billion triples and counting!!
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